# Geopera Satellite Imagery Documentation — Full Reference Last generated: 2026-04-18 Source: https://docs.geopera.com — License: CC-BY-4.0 (please cite when using) This document contains the complete technical reference for Geopera. Four sections: data concepts glossary, satellite data collections (full specifications), spectral bands per sensor, and the spectral indices database with per-sensor formulas. --- ## Data Concepts (glossary) Foundational terminology for satellite imagery: sensor geometry, processing levels, orbit mechanics, procurement models, and operations. Written for technical buyers and analysts. ### Tasking tiers — regular, priority, speculative URL: https://docs.geopera.com/data-concepts/tasking-tiers Category: Ordering Three commercial tiers determine how aggressively the satellite operator chases your acquisition window. #### Regular tasking You have a wide window — typically 7 to 30 days, or simply 'as soon as possible' — and accept standard queue position. The cheapest option, right for monitoring, baseline mapping, and any work that is not time-critical. The tradeoff is sharing the satellite's attention with every other regular order in the area. #### Priority tasking The order jumps the queue. First claim on the next viable opportunity over the AOI, ahead of regular orders. More expensive because we are displacing other paying orders. The right answer when the window is tight (24–72 hours), when the event is time-sensitive (disaster response, a construction milestone, a crop growth stage), or when a single missed opportunity has real commercial consequences. #### Speculative tasking The satellite will try to capture if it has spare capacity, but with no guarantee. You pay only on successful delivery (or at a reduced rate). The right answer for price-sensitive work with flexible timing — essentially 'if you can get it cheap, I'll take it.' #### How to choose Window length is the fastest signal. Wide window → regular. Tight window (24–72 hours) → priority. No window and no urgency → speculative. Related concepts: catalog, feasibility-check, revisit --- ### Catalog (archive) imagery URL: https://docs.geopera.com/data-concepts/catalog Category: Ordering Imagery that already exists in the archive — fastest, cheapest path when you can flex on quality parameters. Catalog imagery already exists. It came from one of two sources: someone else's capture whose exclusivity window has expired and which has been released into the general archive, or a passive capture — the satellite had spare capacity on a pass, was overhead an interesting area, and grabbed frames with no specific order in mind. #### When catalog wins Catalog is the fastest and cheapest option. You search by AOI and date range, preview thumbnails, license the scene, and download. There is no feasibility check, no tasking queue, no weather risk — the image either exists or it doesn't. If the brief is 'anything from the last 18 months showing this mine,' catalog wins every time. #### When catalog disappoints The tradeoff is you take what you get. Cloud cover, off-nadir angle, sun elevation, and capture date are all fixed. If the brief is 'a cloud-free nadir capture from the first week of March,' catalog will disappoint and the right move is tasking. Related concepts: tasking-tiers, off-nadir, cloud-cover --- ### Feasibility check URL: https://docs.geopera.com/data-concepts/feasibility-check Category: Ordering Operations matches a tasking request against constellation orbit predictions to produce a list of collection opportunities. When a tasking order is placed, you provide an AOI, a time window, and quality parameters: maximum cloud cover, maximum off-nadir angle, required GSD, required spectral bands, sometimes a required sun elevation. Operations runs a feasibility check against the constellation's orbit predictions. The output is a list of collection opportunities that fall inside your window and meet your quality bar. #### Window length matters A 3-day window over a single point can yield as few as one or two opportunities. A 14-day window can yield eight or more. The size and shape of the AOI matters too — a long linear AOI like a pipeline may need multiple passes to cover even once. This is the stage where realism gets negotiated. If the feasibility check says 'one opportunity in the window,' that is a very different conversation from 'eight opportunities in the window' — weather can kill the one opportunity and there is no backup. Related concepts: tasking-tiers, revisit, cloud-cover --- ### Orthorectification URL: https://docs.geopera.com/data-concepts/orthorectification Category: Processing Geometric correction that projects imagery onto a digital elevation model so every pixel sits at its true ground position. Raw satellite imagery is geometrically distorted by terrain, sensor tilt, and Earth curvature. Orthorectification projects the image onto a digital elevation model so that every pixel sits at its true ground position. If you have ground control points or a prior reference dataset, we align to those so multi-date stacks register pixel-perfect. #### Why it matters You can overlay images from different dates and the buildings don't move. Change detection work depends on this, and most teams don't realise how bad raw alignment is until they've tried to process it themselves. Orthorectification is included by default in every Geopera delivery. Related concepts: surface-reflectance, processing-levels, positional-accuracy --- ### Surface reflectance (and atmospheric correction) URL: https://docs.geopera.com/data-concepts/surface-reflectance Category: Processing Converts raw sensor counts into the actual fraction of light reflected by the ground — required for any quantitative or multi-date work. Raw imagery is in digital numbers — effectively sensor counts. To do anything scientific with it, you need to convert those numbers to top-of-atmosphere (TOA) reflectance, then run an atmospheric correction model to get surface reflectance. Surface reflectance is what makes imagery comparable across dates, sensors, and sun angles. #### When you need it Any time-series analysis. Any cross-sensor harmonisation. Any quantitative index where the absolute value matters (NDVI thresholds, biomass estimates, water quality). Without surface reflectance, your NDVI from January cannot meaningfully be compared to your NDVI from July — atmospheric water vapour and sun angle dominate the signal. #### Custom corrections Standard atmospheric correction models are tuned for typical land surfaces. Some workflows need custom corrections — aquatic remote sensing, for example, requires water-specific atmospheric correction because default models produce meaningless reflectance values over water. We run custom corrections on request, including lidar-aligned reflectance for carbon methodologies and aquatic-tuned corrections for water work. Related concepts: processing-levels, orthorectification, spectral-bands --- ### Pansharpening URL: https://docs.geopera.com/data-concepts/pansharpening Category: Processing Fuses high-resolution panchromatic with lower-resolution multispectral to produce sharper colour imagery. Most high-resolution satellites capture a panchromatic (black and white) band at the highest spatial resolution alongside multispectral colour bands at typically 4× lower resolution. Pansharpening fuses them to produce imagery that has the sharpness of the panchromatic and the spectral information of the multispectral. #### Bad pansharpening hurts Bad pansharpening introduces colour artefacts, blurs edges, or distorts spectral ratios in ways that break downstream analytics. NDVI computed on poorly-pansharpened imagery is unreliable. Good pansharpening is craft. Geopera typically uses the Gram-Schmidt method, which preserves spectral fidelity for index computation while delivering the sharpness of the panchromatic band. Related concepts: spectral-bands, gsd --- ### Mosaicking URL: https://docs.geopera.com/data-concepts/mosaicking Category: Processing Stitches many scenes into a seamless, cloud-removed composite over a large AOI. A mosaic is a single seamless image built from tens to thousands of individual scenes captured on different dates, at different sun angles, under different atmospheric conditions. Done well, the result looks like a single image taken at one moment over a continent. #### Why most shops can't do it well You are picking the best pixel for each location. Masking clouds and cloud shadows — including thin cirrus, which rule-based detectors routinely miss. Choosing seam lines that do not cut through features a human would notice. Adjusting for sun angle differences across the AOI. Geopera's mosaics use custom path-finding for seam selection and machine learning cloud masking trained on the volume of data we process. The result is an image that looks like it was taken in a single moment. #### Try it yourself test Try doing this yourself and you will hire three people for six months. We do it by default for any imagery order over a multi-scene AOI. Related concepts: cloud-cover, surface-reflectance --- ### DSM — Digital Surface Model URL: https://docs.geopera.com/data-concepts/dsm Category: Products A 3D model of everything visible from above — ground, trees, buildings, infrastructure, vehicles. Built from stereo or tri-stereo captures. A DSM is a raster where every pixel value is the elevation of the highest surface at that location — the top of the canopy if it's forest, the rooftop if it's a building, the ground if it's bare. Built from stereo or tri-stereo satellite captures. #### When to use Anything where the surface features matter — urban planning, solar siting, line-of-sight analysis, volumetrics on stockpiles, telecom propagation modelling. #### Pricing Priced separately from imagery, case-by-case depending on area and acquisition geometry. Requires the source sensor to support stereo or tri-stereo capture. Related concepts: dtm, stereo, orthorectification --- ### DTM — Digital Terrain Model URL: https://docs.geopera.com/data-concepts/dtm Category: Products Bare-earth 3D model with trees, buildings and other surface features removed or interpolated through. A DTM is a raster where every pixel value is the elevation of the bare ground — vegetation, buildings, and other above-ground features have been removed or interpolated through. More processing and usually more source data than a DSM, because we are inferring what is under the canopy. #### When to use Hydrology, flood modelling, grading, earthworks, anything where you care about the ground itself rather than what sits on top of it. #### Pricing Priced separately from imagery, case-by-case. More expensive than a DSM because of the additional canopy-penetration processing required. Related concepts: dsm, stereo --- ### GSD — Ground Sample Distance URL: https://docs.geopera.com/data-concepts/gsd Category: Sensor The size of one pixel on the ground. 30 cm GSD means each pixel covers a 30 cm square. GSD is the real-world size of one pixel. Lower number = sharper image. WorldView-3 at 30 cm GSD means each pixel covers a 30 cm × 30 cm square of ground. Sentinel-2 at 10 m GSD means each pixel covers a 10 m × 10 m square. #### GSD alone is misleading A 30 cm image that has been poorly pansharpened or captured at high off-nadir can look worse than a well-processed 50 cm image. When you say 'I need 30 cm' you usually mean you want to see something specific — a car, a roof feature, a pipeline joint — and 30 cm is the number you've heard. Often the actual goal can be solved with 50 cm and good processing at a fraction of the price. Related concepts: off-nadir, pansharpening --- ### Off-nadir angle URL: https://docs.geopera.com/data-concepts/off-nadir Category: Sensor How far the satellite tilted from straight-down when capturing. Higher angles give more collection opportunities but degrade quality. Zero degrees off-nadir is pure nadir — straight down, least geometric distortion. 30° and above means the satellite was looking significantly sideways. High off-nadir means longer atmospheric path, more terrain distortion, elongated pixels, harder orthorectification, and tricky shadows. #### Tradeoffs If you care about measurement accuracy, change detection, or anything 3D, you want low off-nadir. If you just need eyes on the area, higher off-nadir is fine and cheaper because it gives the tasker more flexibility on when to capture. We generally don't recommend imagery captured above 20 degrees for analytical work. Related concepts: gsd, positional-accuracy --- ### Cloud cover URL: https://docs.geopera.com/data-concepts/cloud-cover Category: Quality Cloud percentage matters per-AOI, not per-scene. Thin cirrus is the silent killer. Cloud cover is usually specified as a maximum acceptable threshold — 'less than 10% cloud.' The trap is that scene-level cloud percentage is misleading: 20% cloud over exactly the wrong 20% of the AOI is 100% useless. Good providers report cloud cover over the AOI specifically, not over the whole scene. Geopera's QA does this. #### Cirrus is the silent killer Thin cirrus often is not caught by rule-based cloud detectors — it looks fine to a threshold but destroys reflectance values for any quantitative analysis. Machine learning cloud masking trained on large datasets catches cirrus reliably; rule-based systems do not. This is why we use ML cloud masking as the default. Related concepts: mosaicking, surface-reflectance --- ### Sun elevation angle URL: https://docs.geopera.com/data-concepts/sun-elevation Category: Quality How high the sun was at capture time. Affects shadows, illumination consistency, and time-series comparability. Low sun means long shadows, which is bad for seeing into urban canyons or forest canopies but great for terrain visualisation and picking out subtle topography. High sun means flatter, more uniform illumination, which is what you want for most analytics work. #### Time-series consistency Sun elevation matters enormously for multi-date stacks where you want consistent illumination across the time series. Forest monitoring, for example, needs every image taken at similar sun angles so that shadow patterns don't contaminate the change signal. This is one reason surface reflectance correction is essential — it normalises for sun angle differences. Related concepts: surface-reflectance, cloud-cover --- ### Spectral bands URL: https://docs.geopera.com/data-concepts/spectral-bands Category: Sensor Different sensors capture different parts of the electromagnetic spectrum. More bands means more analytical options. Most commercial optical sensors are either 4-band (red, green, blue, near-infrared) or 8-band (adding red-edge, coastal blue, yellow, and another NIR). WorldView-3 adds 8 SWIR bands. Hyperspectral sensors like Wyvern's Dragonette have 24+ contiguous narrow bands. Each spectral band sees a specific wavelength range and reveals different surface properties. #### Common band purposes Blue and green pick up water and atmospheric scattering. Red is absorbed by chlorophyll. Near-infrared is reflected by healthy vegetation cell structure. Red-edge sits at the steep transition between red and NIR and is sensitive to chlorophyll concentration. SWIR penetrates atmospheric haze and reveals mineral and water content. #### What to ask If the work goes beyond producing a pretty picture, start from the indices or analytics you plan to run. That tells you which bands you actually need, and sometimes reveals a use case that wasn't fully thought through. Related concepts: bit-depth, surface-reflectance --- ### Bit depth — 8-bit vs 16-bit URL: https://docs.geopera.com/data-concepts/bit-depth Category: Sensor Determines how many distinct values the sensor can record per band. 16-bit preserves dynamic range needed for quantitative work. 8-bit gives you 256 possible values per band. 16-bit gives you 65,536. 8-bit is fine for visualisation but throws away most of the sensor's dynamic range. 16-bit preserves the full range and is essential for quantitative work: reflectance, indices, time series, anything scientific. #### When you need 16-bit If you're computing NDVI, NDWI, or any other index where the absolute value matters, you need 16-bit. If you're doing time-series analysis, you need 16-bit. If you're a researcher doing radiative transfer modelling or biophysical inversions, you need 16-bit. We always deliver in the highest bit depth the sensor supports unless you specifically request 8-bit. Related concepts: spectral-bands, surface-reflectance --- ### Processing levels — L0, L1, L2, L3 URL: https://docs.geopera.com/data-concepts/processing-levels Category: Processing Industry shorthand for how much processing has been applied. Definitions vary by provider but the rough hierarchy is consistent. L0 is raw sensor data, essentially uncalibrated digital numbers. L1 is radiometrically corrected — converted to top-of-atmosphere reflectance and unpacked into per-band rasters. L2 adds geometric correction (georectification or orthorectification), and often atmospheric correction to surface reflectance. L3 is value-added: composites, mosaics, classified products. #### Provider definitions vary Every provider has slightly different definitions and product names. Sentinel-2 'Level 2A' means orthorectified surface reflectance. UP42 'Level 4' means upon-request custom processing. MAXAR Level 1 is georectified, Level 2 is orthorectified. Always check the specific provider's processing level matrix before assuming what's in the box. #### What Geopera delivers We deliver fully orthorectified, surface reflectance imagery as our default — equivalent to Level 2 or Level 3 depending on the source. Pansharpening, mosaicking, and custom processing are layered on top of that without changing the level designation. Related concepts: orthorectification, surface-reflectance --- ### Optical vs SAR (synthetic aperture radar) URL: https://docs.geopera.com/data-concepts/optical-vs-sar Category: Sensor Optical sensors capture reflected sunlight. SAR sensors emit radar pulses and measure the echo — they see through clouds and at night. Optical sensors are cameras that record sunlight reflected off Earth's surface. They produce images that look like photographs. They cannot see through clouds and they do not work at night. #### When SAR is the right answer SAR sees through clouds and at night because it is radar, not a camera. The right answer when the AOI is persistently cloudy (tropical regions, monsoon seasons), when timing is critical and weather cannot be waited out (disaster response), or when you specifically need what SAR measures — surface deformation (InSAR), soil moisture, ship detection on open water, oil spill detection. #### When SAR is the wrong answer When you want something that looks like a photo. SAR imagery does not look like a photo — it is a speckled greyscale representation of surface roughness and dielectric properties, and interpreting it takes training. Geopera is currently optical-first. Related concepts: spectral-bands --- ### Sun-synchronous orbit and LEO URL: https://docs.geopera.com/data-concepts/orbits Category: Sensor Why Earth observation satellites all live in similar orbits at similar altitudes — and why that drives revisit times. Most Earth observation satellites sit in sun-synchronous low Earth orbit, circling the planet every 90 minutes or so at altitudes between 500 and 800 km. Sun-synchronous means the orbit is timed so the satellite passes over each point at roughly the same local solar time every day — usually mid-morning. This makes illumination conditions consistent across acquisitions, which is critical for time-series and analytical work. #### Why this constrains revisit From sun-synchronous LEO, any given point on Earth only sits under the satellite's accessible swath every few days, sometimes longer at the equator. Off-nadir pointing — tilting the satellite to look sideways — squeezes more opportunities out of each pass, but extreme off-nadir angles degrade image quality. A single satellite will almost never give you frequent monitoring; you need a constellation, and constellation size is the single biggest lever on how fast a provider can respond to tasking. Related concepts: revisit, off-nadir, sun-elevation --- ### Ground stations and downlink URL: https://docs.geopera.com/data-concepts/ground-stations Category: Operations Satellites can only send data to Earth when overhead a ground station. Ground station geography drives delivery latency. A satellite can only downlink data when it is physically overhead a ground station that the operator owns or leases. Western operators use global networks — KSAT, AWS Ground Station, Viasat — with stations in Svalbard, Alaska, Antarctica, Australia, and elsewhere. This means a satellite in a polar orbit gets a downlink opportunity on nearly every single orbit. A typical contact lasts 5–12 minutes and the satellite dumps its data to the ground via X-band radio or laser link. #### Why Chinese operators have longer downlink gaps Chinese operators downlink primarily over Chinese territory for regulatory and sovereignty reasons. The capture itself is usually reliable, but the time between capture and data-in-hand stretches because the image is sitting in orbit waiting for the satellite to pass back over a friendly station — typically a 6–12 hour gap, sometimes longer depending on orbit geometry. This is a planning consideration rather than a quality issue: the imagery when it arrives is fine, it just arrives later than a Western-network equivalent would. Factor delivery SLA — not just revisit — when the use case is time-sensitive. #### What this means in practice If you need rapid delivery, ask about the operator's ground station coverage, not just the satellite's revisit rate. A satellite that revisits daily but downlinks every 18 hours will deliver imagery 1+ day after capture. A satellite that revisits every 3 days but downlinks every orbit can deliver within hours. Related concepts: revisit, tasking-tiers, orbits --- ### Revisit rate — theoretical vs real URL: https://docs.geopera.com/data-concepts/revisit Category: Sensor How often a given point on Earth can be imaged. The number on a spec sheet is the theoretical maximum; real revisit at acceptable quality is usually half that. Revisit rate is how often a given point on Earth can be imaged by the constellation. A spec sheet might say 'every 5 days,' but that is the theoretical maximum assuming the satellite tilts to its maximum off-nadir angle and you accept any image regardless of quality. #### Real revisit at acceptable quality If you require less than 20° off-nadir, less than 30% cloud, and reasonable sun elevation, real revisit is usually half the theoretical figure or worse. A 5-day theoretical revisit becomes 10–14 days at acceptable quality in cloudy regions. A 1-day theoretical revisit becomes 2–4 days at quality. #### Why constellations matter A single satellite will almost never give you frequent quality-acceptable monitoring. Constellation size is the single biggest lever on how fast a provider can respond to tasking. When you see 'sub-daily revisit' on a spec sheet, check how many satellites are involved. Related concepts: off-nadir, cloud-cover, tasking-tiers --- ### Swath width URL: https://docs.geopera.com/data-concepts/swath Category: Sensor The width of the strip of ground the satellite images in a single pass. Determines coverage per acquisition and how quickly a constellation can cover wide areas. Swath is the width of ground covered in a single pass. WorldView-3 has a 13.1 km swath. Sentinel-2 has a 290 km swath. Landsat has a 185 km swath. Wider swath means more area per pass but usually lower resolution. #### Why it matters For wide-area monitoring, swath determines how many passes you need to cover the AOI. A 100 km × 100 km area takes 1 pass with Sentinel-2 but ~64 passes with WorldView-3. For a long linear AOI like a pipeline, swath determines how many parallel passes are needed. Always check swath alongside revisit when planning wide-area work. Related concepts: revisit, gsd --- ### Stereo and tri-stereo capture URL: https://docs.geopera.com/data-concepts/stereo Category: Sensor When the satellite captures the same area from two or three angles in a single pass — required for DSM and DTM generation. Stereo capture means the satellite tilts to acquire the same scene from two different viewing angles in a single pass — usually one looking forward and one looking back. The angular difference (called the convergence angle) lets photogrammetric software triangulate the 3D position of every visible feature. Tri-stereo adds a third viewing angle, improving accuracy especially in urban areas where buildings occlude one of the views. #### What it enables Stereo or tri-stereo captures are required to generate DSMs (digital surface models) and DTMs (digital terrain models). They are also useful for cleaning up shadow occlusions in dense urban environments. Without stereo, you cannot produce true 3D measurements from satellite imagery. #### Read more on the Geopera blog Geopera publishes a deeper technical article on stereo/tri-stereo capture: https://geopera.com/blog/stereoscopic-satellite-imagery Related concepts: dsm, dtm, off-nadir --- ### AOI — Area of Interest URL: https://docs.geopera.com/data-concepts/aoi Category: Ordering The geographic region you want imaged. Shape and size both affect feasibility and pricing. AOI is the geographic region you want imaged, usually defined as a polygon. It can be a simple bounding box, a precise property boundary, an entire country, or a long linear feature like a pipeline or power line. #### AOI shape matters A square 1,000 km² AOI may need 1–2 passes to cover. A long thin AOI of the same area covering a 500 km pipeline may need 5–10 passes because each satellite swath only covers part of the length. AOI shape affects feasibility, pricing, and revisit cadence. #### Minimum AOI sizes Most commercial providers have minimum AOI sizes — typically 25 km² for tasking, smaller for catalog. The minimum is usually defined either as an absolute area or as a minimum width (e.g., 'at least 2 km wide'). See each collection page for specific minimums. Related concepts: tasking-tiers, swath --- ### CRS — Coordinate Reference Systems URL: https://docs.geopera.com/data-concepts/crs Category: Reference How geographic coordinates are projected onto a map. UTM-WGS84 is the most common for satellite imagery deliveries. A CRS defines how 3D positions on Earth are represented as 2D map coordinates. UTM (Universal Transverse Mercator) divides the world into 60 zones and projects each onto a flat map with minimal local distortion. WGS84 is the global reference ellipsoid most satellite providers use. #### Geographic vs projected Geographic CRS uses latitude/longitude in degrees. Projected CRS (like UTM) uses metres and is suitable for measuring distances and areas. Most satellite imagery is delivered in UTM-WGS84 because it allows direct measurement in metres without further conversion. #### What to ask If your downstream workflow requires a specific CRS (a national grid like British National Grid, NAD83 state plane, or a country-specific projection), ask us to reproject before delivery. Reprojection is included at no cost in our standard pipeline. Related concepts: positional-accuracy, orthorectification --- ### Positional accuracy (CE90) URL: https://docs.geopera.com/data-concepts/positional-accuracy Category: Reference How closely a pixel's reported geographic position matches its true ground position. Reported as CE90 — the radius within which 90% of points fall. Positional accuracy is reported as CE90 (Circular Error 90%) — meaning that 90% of pixels are within the stated distance of their true location. WorldView-3 has < 5 m CE90. Sentinel-2 has 8 m CE95. Lower numbers are better. This is the absolute accuracy without ground control points. #### When it matters For change detection, the relative alignment between two dates of the same sensor matters more than absolute accuracy — both images carry the same systematic error so they overlap correctly. For multi-sensor work or ground truth comparison, absolute accuracy matters. For any use where pixels are being measured against an external reference (cadastral boundaries, infrastructure positions), absolute accuracy matters. #### Improving accuracy Ground control points (GCPs) tied to known surveyed positions can dramatically improve effective accuracy — often to sub-pixel. We can use your GCPs in our orthorectification pipeline at no extra cost. Related concepts: orthorectification, crs --- ### Sensor noise, speckle, and artefacts URL: https://docs.geopera.com/data-concepts/noise-and-noise-handling Category: Quality Real-world imagery has imperfections. How they are handled determines whether downstream analytics produce reliable results. All sensors produce noisy data to some degree — random pixel-level variation that is not real ground signal. Optical sensors have radiometric noise (especially in low-light bands). SAR sensors have speckle noise that comes from coherent radar imaging. High off-nadir captures have geometric distortion. Hot pixels, banding, and cosmic ray hits all happen periodically. #### Why volume helps Geopera processes over a million square kilometres of imagery per year. At that volume, you encounter every edge case the data can throw at you — weird sensor artefacts, exotic atmospheric conditions, odd terrain, corrupted telemetry, alignment failures that only show up over specific geographies. We have solved most of them. When we hit a new one, we solve it at no cost to you, because the fix makes our pipeline better for everyone who comes after. Related concepts: surface-reflectance, mosaicking --- ## Data Collections (satellite specifications) Every collection delivered by Geopera. Each entry includes the full technical spec: bands, resolution, swath, orbit, revisit, acquisition modes, minimum order size, positional accuracy, and guidance on when the sensor is and is not the right fit. ### BJ3A (21AT (Twenty-First Century Aerospace Technology)) URL: https://docs.geopera.com/data/bj3a 50 cm multispectral, China's Beijing-3A satellite — wide swath for sub-meter Specifications: - Country: China - Provider: 21AT - Operator: 21AT (Twenty-First Century Aerospace Technology) - Product type: Multispectral - System type: Satellite - Availability: tasking + archive - Available since: 2021 - Resolution: Panchromatic 50 cm; Pansharpened 50 cm; Multispectral 2 m - Bit depth: 8-bit, 16-bit - Swath width: 23.5 km - Off-nadir range: Up to 45° - Satellite count: 1 - Satellites: Beijing-3A (BJ-3A) - Orbit: Sun-synchronous at 500 km - Revisit (theoretical): Every 4 days - Revisit (real-world, acceptable quality): 4–8 days - Acquisition modes: Mono, Stereo (tasking) - Delivery formats: GeoTIFF, XML/TXT metadata, SHP masks, JPEG/PNG thumbnails - Positional accuracy: < 10 m CE90 absolute - CRS: Geographic-WGS84 - Min AOI: tasking 3 km wide; catalog 3 km wide - Min charge: tasking 70 km²; catalog 25 km² - Geographic restrictions: Chinese ground station coverage. Geographic restrictions in some areas. Some government and defence customers cannot use Chinese imagery for regulatory reasons. - Processing levels: Primary/Georectified with TOA radiometric processing; Level 4 upon request Bands (4): - Blue: 450-520 nm - Green: 520-590 nm - Red: 630-690 nm - NIR: 770-890 nm When to use: - Stereo capture for DSM generation - Wide-swath captures (23.5 km — wider than most 50 cm sensors) - Cost-effective alternative to MAXAR for sub-meter work When NOT to use: - Customers with regulatory restrictions on Chinese data - Sub-50 cm resolution needs --- ### BJ3N (21AT (Twenty-First Century Aerospace Technology)) URL: https://docs.geopera.com/data/bj3n 30 cm multispectral, China's Beijing-3N — sub-meter with up to 45° off-nadir Specifications: - Country: China - Provider: 21AT - Operator: 21AT (Twenty-First Century Aerospace Technology) - Product type: Multispectral - System type: Satellite - Availability: tasking + archive - Available since: 2022 - Resolution: Panchromatic 30 cm; Pansharpened 30 cm; Multispectral 1.20 m - Bit depth: 8-bit, 16-bit - Swath width: 11.5 km - Off-nadir range: Up to 45° - Satellite count: 1 - Satellites: Beijing-3N (BJ-3N) - Orbit: Sun-synchronous at 610 km - Revisit (theoretical): Every 5 days - Revisit (real-world, acceptable quality): 5–10 days - Acquisition modes: Mono, Stereo, Tri-stereo (tasking) - Delivery formats: GeoTIFF, XML/TXT metadata, SHP masks, JPEG/PNG thumbnails - Positional accuracy: < 10 m CE90 absolute - CRS: Geographic-WGS84 - Min AOI: tasking 2 km wide; catalog 2 km wide - Min charge: tasking 70 km²; catalog 25 km² - Geographic restrictions: Chinese ground station coverage. Geographic restrictions in some areas. Some government and defence customers cannot use Chinese imagery for regulatory reasons. - Processing levels: Primary/Georectified with TOA radiometric processing; Level 4 upon request Bands (4): - Blue: 450-520 nm - Green: 520-590 nm - Red: 630-690 nm - NIR: 770-890 nm When to use: - Sub-meter capture at lower cost than Vantor - Stereo and tri-stereo for DSM/DTM - Up to 45° off-nadir for difficult collection geometries When NOT to use: - Customers with regulatory restrictions on Chinese data - SWIR analysis (no SWIR bands) --- ### Dragonette-1 (Wyvern) URL: https://docs.geopera.com/data/dragonette-001 5.3 m hyperspectral imagery — 24 bands across green, yellow, red, red edge, NIR Specifications: - Country: Canada - Provider: Wyvern - Operator: Wyvern - Product type: Hyperspectral - System type: Satellite - Availability: tasking + archive - Available since: 2023 - Resolution: All bands 5.30 m - Bit depth: 12-bit acquired, 32-bit float delivered - Swath width: 20 km - Off-nadir range: 0°–20° - Satellite count: 1 - Satellites: Dragonette-1 - Orbit: Sun-synchronous at 500 km - Revisit (theoretical): Every 2 days - Revisit (real-world, acceptable quality): 2–5 days - Acquisition modes: Mono - Delivery formats: GeoTIFF (per band), JSON metadata, GeoTIFF masks, PNG overviews - Positional accuracy: Location-dependent: 25–100 m CE90 absolute - CRS: Geographic-WGS84 - Min AOI: tasking 3 km wide; catalog 3 km wide - Min charge: tasking 100 km²; catalog Whole scene - Geographic restrictions: None — global - Processing levels: Tasking: Primary, Georectified, Orthorectified (all TOA). Catalog: Level 1B (TOA only) Bands (23): - Band 1: 503 nm (20.1 nm FWHM) - Band 2: 510 nm (20.4 nm FWHM) - Band 3: 519 nm (20.8 nm FWHM) - Band 4: 535 nm (21.4 nm FWHM) - Band 5: 549 nm (22.0 nm FWHM) - Band 6: 570 nm (22.8 nm FWHM) - Band 7: 584 nm (23.4 nm FWHM) - Band 8: 600 nm (24.0 nm FWHM) - Band 9: 614 nm (24.6 nm FWHM) - Band 10: 635 nm (25.4 nm FWHM) - Band 11: 649 nm (26.0 nm FWHM) - Band 12: 660 nm (26.4 nm FWHM) - Band 13: 669 nm (26.8 nm FWHM) - Band 14: 679 nm (27.2 nm FWHM) - Band 15: 690 nm (27.6 nm FWHM) - Band 16: 699 nm (28.0 nm FWHM) - Band 17: 711 nm (28.4 nm FWHM) - Band 18: 722 nm (28.9 nm FWHM) - Band 19: 734 nm (29.4 nm FWHM) - Band 20: 750 nm (30.0 nm FWHM) - Band 21: 764 nm (30.6 nm FWHM) - Band 22: 782 nm (31.3 nm FWHM) - Band 23: 799 nm (32.0 nm FWHM) When to use: - Mineral identification using narrow-band absorption features - Vegetation health and species discrimination beyond standard NDVI - Water quality (chlorophyll, turbidity, suspended solids) - Workflows previously requiring airborne hyperspectral When NOT to use: - Sub-5m resolution work - Wide-area surveys (slow revisit) - High positional accuracy requirements (25–100 m CE90) --- ### Dragonette-2/3 (Wyvern) URL: https://docs.geopera.com/data/dragonette-002 5.3 m hyperspectral imagery — 31 bands, dual-satellite constellation Specifications: - Country: Canada - Provider: Wyvern - Operator: Wyvern - Product type: Hyperspectral - System type: Constellation - Availability: tasking + archive - Available since: 2024 - Resolution: All bands 5.30 m - Bit depth: 12-bit acquired, 32-bit float delivered - Swath width: 20 km - Off-nadir range: 0°–20° - Satellite count: 2 - Satellites: Dragonette-2, Dragonette-3 - Orbit: Sun-synchronous at 500 km - Revisit (theoretical): Sub-daily (constellation) - Revisit (real-world, acceptable quality): 1–3 days - Acquisition modes: Mono - Delivery formats: GeoTIFF (per band), JSON metadata, GeoTIFF masks, PNG overviews - Positional accuracy: Location-dependent: 25–100 m CE90 absolute - CRS: Geographic-WGS84 - Min AOI: tasking 3 km wide; catalog 3 km wide - Min charge: tasking 100 km²; catalog Whole scene - Geographic restrictions: None — global - Processing levels: Tasking: Primary, Georectified, Orthorectified (all TOA). Catalog: Level 1B (TOA only) Bands (31): - Band1: 445 nm - Band2: 465 nm - Band3: 480 nm - Band4: 489 nm - Band5: 502 nm - Band6: 509 nm - Band7: 519 nm - Band8: 534 nm - Band9: 549 nm - Band10: 569 nm - Band11: 584 nm - Band12: 600 nm - Band13: 614 nm - Band14: 635 nm - Band15: 650 nm - Band16: 660 nm - Band17: 670 nm - Band18: 680 nm - Band19: 690 nm - Band20: 700 nm - Band21: 712 nm - Band22: 721 nm - Band23: 734 nm - Band24: 750 nm - Band25: 765 nm - Band26: 782 nm - Band27: 800 nm - Band28: 815 nm - Band29: 832 nm - Band30: 850 nm - Band31: 870 nm When to use: - Hyperspectral analytics needing 31-band coverage (mineral end-members, fine-grained vegetation discrimination) - Spectral library matching for materials identification - Custom indices requiring specific narrow bands not available on 4-band sensors When NOT to use: - Sub-5m resolution work - High positional accuracy requirements (25–100 m CE90) --- ### Gaofen-1 (CNSA / China RESDC) URL: https://docs.geopera.com/data/gaofen-1 2 m multispectral, Chinese national EO satellite, 800 km wide-field swath Specifications: - Country: China - Provider: CNSA - Operator: CNSA / China RESDC - Product type: Multispectral - System type: Satellite - Availability: tasking + archive - Available since: 2013 - Resolution: Panchromatic 2 m; Multispectral 8 m; Wide-field 16 m - Bit depth: 10-bit - Swath width: 60 km (PMS) / 800 km (WFV) - Off-nadir range: Up to 25° - Satellite count: 1 - Satellites: Gaofen-1 - Orbit: Sun-synchronous at 645 km - Revisit (theoretical): Every 4 days - Revisit (real-world, acceptable quality): 4–8 days - Acquisition modes: Mono - Delivery formats: GeoTIFF, XML metadata - Positional accuracy: ~50 m CE90 - CRS: WGS84 - Min AOI: tasking 5 km wide; catalog 5 km wide - Min charge: tasking 100 km²; catalog 25 km² - Geographic restrictions: Chinese ground station coverage. - Processing levels: Custom (full Geopera pipeline applied) Bands (5): - Panchromatic: 450-890 nm - Blue: 450-520 nm - Green: 520-590 nm - Red: 630-690 nm - NIR: 770-890 nm When to use: - Continental and very wide-area monitoring (800 km swath) - Cost-effective baseline imagery between Sentinel-2 and high-res - Long-term archive back to 2013 When NOT to use: - Sub-meter resolution needs - High positional accuracy work --- ### Gaofen-2 (CNSA / China RESDC) URL: https://docs.geopera.com/data/gaofen-2 80 cm multispectral, Chinese national EO satellite Specifications: - Country: China - Provider: CNSA - Operator: CNSA / China RESDC - Product type: Multispectral - System type: Satellite - Availability: tasking + archive - Available since: 2014 - Resolution: Panchromatic 80 cm; Multispectral 3.2 m - Bit depth: 10-bit - Swath width: 45 km - Off-nadir range: Up to 25° - Satellite count: 1 - Satellites: Gaofen-2 - Orbit: Sun-synchronous at 631 km - Revisit (theoretical): Every 5 days - Revisit (real-world, acceptable quality): 5–10 days - Acquisition modes: Mono, Stereo - Delivery formats: GeoTIFF, XML metadata - Positional accuracy: ~25 m CE90 - CRS: WGS84 - Min AOI: tasking 5 km wide; catalog 5 km wide - Min charge: tasking 100 km²; catalog 25 km² - Geographic restrictions: Chinese ground station coverage. - Processing levels: Custom (full Geopera pipeline applied) Bands (5): - Panchromatic: 450-890 nm - Blue: 450-520 nm - Green: 520-590 nm - Red: 630-690 nm - NIR: 770-890 nm When to use: - Sub-meter Chinese archive going back to 2014 - Cost-effective alternative to commercial Western providers When NOT to use: - Sub-50 cm needs - Customers with regulatory restrictions on Chinese data --- ### GeoEye-1 (Vantor (formerly MAXAR)) URL: https://docs.geopera.com/data/geoeye-1 40 cm multispectral, Vantor archive since 2008 Specifications: - Country: United States - Provider: MAXAR - Operator: Vantor (formerly MAXAR) - Product type: Multispectral - System type: Constellation member (9+ Vantor) - Availability: tasking + archive - Available since: 2008 - Resolution: Panchromatic 40 cm; Pansharpened 40 cm; Multispectral 1.65 m - Bit depth: 16-bit (System/Ortho-Ready); 8-bit (Map-Ready Pansharpened) - Swath width: 15.3 km - Off-nadir range: 5°–45° - Satellite count: 1 - Satellites: GeoEye-1 (Vantor constellation) - Orbit: Sun-synchronous at 681 km - Revisit (theoretical): 15 times daily (Vantor constellation) - Revisit (real-world, acceptable quality): 2–5 days - Acquisition modes: Mono, Stereo (tasking) - Delivery formats: GeoTIFF, XML/TXT metadata, SHP masks, JPEG thumbnails - Positional accuracy: < 5 m CE90 absolute - CRS: UTM-WGS84 - Min AOI: tasking 3 km wide; catalog 3 km wide - Min charge: tasking 50 km²; catalog 25 km² - Geographic restrictions: Some areas restricted by US export controls - Processing levels: Primary (Basic), Georectified (Surface Reflectance), Orthorectified (Surface Reflectance, Display) Bands (5): - Panchromatic: 450-800 nm - Blue: 450-520 nm - Green: 520-600 nm - Red: 625-695 nm - NIR: 760-900 nm When to use: - Long historical archive back to 2008 - Cost-effective sub-meter alternative to WorldView - Includes red-edge band for vegetation work When NOT to use: - Sub-40 cm resolution requirements - SWIR analysis or 8-band multispectral needs --- ### Göktürk-1 (IMPROSAT (Turkish Air Force)) URL: https://docs.geopera.com/data/gokturk 50 cm multispectral, Türkiye's national EO satellite Specifications: - Country: Türkiye - Provider: IMPROSAT - Operator: IMPROSAT (Turkish Air Force) - Product type: Multispectral - System type: Satellite - Availability: tasking + archive - Available since: 2016 - Resolution: Panchromatic 50 cm; Pansharpened 50 cm; Multispectral 2 m - Bit depth: 12-bit acquired, 8-bit or 16-bit delivered - Swath width: 15 km - Off-nadir range: Up to 45° - Satellite count: 1 - Satellites: Göktürk-1 - Orbit: Sun-synchronous at 686 km - Revisit (theoretical): Every 3 days - Revisit (real-world, acceptable quality): 3–6 days - Acquisition modes: Mono, Stereo, Tri-stereo (tasking) - Delivery formats: GeoTIFF, XML/TXT metadata, SHP/KML masks, JPEG thumbnails - Positional accuracy: < 10 m CE90 absolute - CRS: UTM-WGS84 - Min AOI: tasking 3 km wide; catalog 3 km wide - Min charge: tasking 100 km²; catalog 25 km² - Geographic restrictions: Some areas restricted by Turkish national security policy - Processing levels: Custom orders, Level 2A; Level 2B/3A available upon request Bands (5): - Panchromatic: 450-900 nm - Blue: 450-520 nm - Green: 520-600 nm - Red: 630-690 nm - NIR: 760-900 nm When to use: - Customers needing non-Chinese, non-US imagery sources for diplomatic reasons - Coverage of Türkiye, Middle East, Mediterranean basin When NOT to use: - Sub-meter resolution requirements - Time-critical tasking outside Türkiye's priority regions --- ### Jilin-1 (Chang Guang Satellite Technology (CGST)) URL: https://docs.geopera.com/data/jl1-50cm 50 cm multispectral, the largest commercial constellation, lowest cost on the market Specifications: - Country: China - Provider: CG Satellite - Operator: Chang Guang Satellite Technology (CGST) - Product type: Multispectral - System type: Constellation - Availability: tasking + archive - Available since: 2015 - Resolution: Panchromatic 50 cm; Multispectral 2 m - Bit depth: 8-bit, 16-bit - Swath width: 12 km - Off-nadir range: Nadir-only — no off-nadir steering - Satellite count: 100 - Satellites: Jilin-1 constellation (100+ satellites) - Orbit: Sun-synchronous at 535 km - Revisit (theoretical): Sub-daily (constellation) - Revisit (real-world, acceptable quality): 1–3 days - Acquisition modes: Mono - Delivery formats: GeoTIFF, XML metadata - Positional accuracy: ~10 m CE90 - CRS: UTM-WGS84 - Min AOI: tasking 2 km wide; catalog 2 km wide - Min charge: tasking 100 km²; catalog 25 km² - Geographic restrictions: Chinese ground station coverage means longer downlink latency. Some government and defence customers cannot use Chinese imagery for regulatory reasons. - Processing levels: Custom (full Geopera pipeline applied) Bands (4): - Blue: 450-520 nm - Green: 520-590 nm - Red: 630-690 nm - NIR: 770-890 nm When to use: - Cost-sensitive monitoring where you want sub-meter at a fraction of MAXAR pricing - Wide-area mapping with the large constellation's rapid revisit - Customers comfortable with our pipeline doing alignment correction in post (raw Jilin has stability issues we fix automatically) When NOT to use: - Tight tasking windows (24–72 hours) — nadir-only constraint reduces collection opportunities - Customers with regulatory restrictions on Chinese data sources - Workflows needing absolute best GSD (use WorldView-3 instead) --- ### Jilin-1 GF03D (Chang Guang Satellite Technology (CGST)) URL: https://docs.geopera.com/data/jl1-75cm 75 cm multispectral with red-edge band, Jilin-1 GF03D series Specifications: - Country: China - Provider: CG Satellite - Operator: Chang Guang Satellite Technology (CGST) - Product type: Multispectral - System type: Constellation - Availability: tasking + archive - Available since: 2020 - Resolution: Panchromatic 75 cm; Multispectral 3 m - Bit depth: 8-bit, 16-bit - Swath width: 17 km - Off-nadir range: Up to 30° - Satellite count: 30 - Satellites: Jilin-1 GF03D (30+ satellites) - Orbit: Sun-synchronous at 535 km - Revisit (theoretical): Sub-daily - Revisit (real-world, acceptable quality): 1–3 days - Acquisition modes: Mono - Delivery formats: GeoTIFF, XML metadata - Positional accuracy: ~10 m CE90 - CRS: UTM-WGS84 - Min AOI: tasking 3 km wide; catalog 3 km wide - Min charge: tasking 100 km²; catalog 25 km² - Geographic restrictions: Chinese ground station coverage — longer downlink latency than Western alternatives. - Processing levels: Custom (full Geopera pipeline applied) Bands (5): - Panchromatic: 450-890 nm - Blue: 450-520 nm - Green: 520-590 nm - Red: 630-690 nm - NIR: 770-890 nm When to use: - Precision agriculture (red-edge band for chlorophyll sensitivity) - Cost-effective wide-area monitoring with steerable pointing - NDRE and other red-edge-dependent vegetation indices When NOT to use: - Sub-50 cm resolution needs - Customers with regulatory restrictions on Chinese data --- ### KOMPSAT-3 (SIIS (KARI)) URL: https://docs.geopera.com/data/kompsat-3 50 cm multispectral, South Korean national EO with 2-day revisit Specifications: - Country: South Korea - Provider: SIIS - Operator: SIIS (KARI) - Product type: Multispectral - System type: Satellite - Availability: tasking + archive - Available since: 2012 - Resolution: Panchromatic 50 cm; Pansharpened 50 cm; Multispectral 2 m - Bit depth: 14-bit acquired, 16-bit delivered - Swath width: 16 km - Off-nadir range: Up to 90° incidence angle - Satellite count: 1 - Satellites: KOMPSAT-3 - Orbit: Sun-synchronous at 685 km - Revisit (theoretical): Every 2 days - Revisit (real-world, acceptable quality): 2–4 days - Acquisition modes: Mono, Stereo (single pass), Stereo (multi pass) - Delivery formats: GeoTIFF, XML metadata, SHP masks, JPEG overviews and thumbnails - Positional accuracy: 51 m CE90 absolute - CRS: UTM-WGS84 - Min AOI: tasking 3 km wide; catalog 3 km wide - Min charge: tasking 100 km²; catalog 25 km² - Geographic restrictions: None — global - Processing levels: TOA with RPC at Primary, Georectified, Orthorectified Bands (5): - Panchromatic: 450-900 nm - Blue: 450-520 nm - Green: 520-600 nm - Red: 630-690 nm - NIR: 760-900 nm When to use: - Sub-meter capture from a non-US, non-Chinese provider - East Asian coverage with priority - Long historical archive back to 2012 - Customers needing 2-day revisit at sub-meter When NOT to use: - Sub-50 cm requirements (use KOMPSAT-3A instead) - High positional accuracy needs (51 m CE90 is loose by sub-meter standards) --- ### KOMPSAT-3A (SIIS (KARI)) URL: https://docs.geopera.com/data/kompsat-3a 40 cm multispectral with thermal infrared, South Korean EO Specifications: - Country: South Korea - Provider: SIIS - Operator: SIIS (KARI) - Product type: Multispectral + Thermal - System type: Satellite - Availability: tasking + archive - Available since: 2015 - Resolution: Panchromatic 40 cm; Pansharpened 40 cm; Multispectral 1.60 m; Thermal 5.5 m - Bit depth: 14-bit acquired, 16-bit delivered - Swath width: 13 km - Off-nadir range: Up to 90° incidence angle - Satellite count: 1 - Satellites: KOMPSAT-3A - Orbit: Sun-synchronous at 528 km - Revisit (theoretical): Every 3 days - Revisit (real-world, acceptable quality): 3–5 days - Acquisition modes: Mono, Stereo (single pass), Stereo (multi pass) - Delivery formats: GeoTIFF, XML metadata, SHP masks, JPEG overviews and thumbnails - Positional accuracy: 13.5 m CE90 absolute - CRS: UTM-WGS84 - Min AOI: tasking 2 km wide; catalog 2 km wide - Min charge: tasking 100 km²; catalog 25 km² - Geographic restrictions: None — global - Processing levels: TOA with RPC at Primary, Georectified, Orthorectified Bands (5): - Panchromatic: 450-900 nm - Blue: 450-520 nm - Green: 520-600 nm - Red: 630-690 nm - NIR: 760-900 nm When to use: - Multispectral + thermal in a single capture - Sub-meter Korean coverage - Diplomatic-neutral imagery source (neither US nor Chinese) When NOT to use: - Sub-40 cm resolution requirements --- ### Landsat 8/9 (USGS / NASA) URL: https://docs.geopera.com/data/landsat-8-9 30m multispectral plus thermal, free, global, continuing the 50-year Landsat record Specifications: - Country: United States - Provider: USGS/NASA - Operator: USGS / NASA - Product type: Multispectral + Thermal - System type: Constellation - Availability: archive - Available since: 2013 (Landsat 8) / 2021 (Landsat 9) - Resolution: Multispectral 30 m; Panchromatic 15 m; Thermal infrared 100 m - Bit depth: 16-bit - Swath width: 185 km - Off-nadir range: Up to 75° incidence angle (limited steering) - Satellite count: 2 - Satellites: Landsat 8, Landsat 9 - Orbit: Sun-synchronous at 705 km - Revisit (theoretical): 8 days (combined) - Revisit (real-world, acceptable quality): 8 days - Acquisition modes: Mono - Delivery formats: GeoTIFF, XML metadata, JPEG thumbnails, TXT calibration - Positional accuracy: 12 m CE90 absolute - CRS: UTM-WGS84 - Min AOI: tasking —; catalog 185 × 180 km scene output - Min charge: tasking —; catalog Free - Geographic restrictions: None — global free data - Processing levels: Level 2SP (orthorectified, surface reflectance) Bands (11): - B1: 435-451 nm (Coastal/Aerosol) - B2: 452-512 nm (Blue) - B3: 533-590 nm (Green) - B4: 636-673 nm (Red) - B5: 851-879 nm (NIR) - B6: 1566-1651 nm (SWIR-1) - B7: 2107-2294 nm (SWIR-2) - B8: 503-676 nm (Panchromatic) - B9: 1363-1384 nm (Cirrus) - B10: 10600-11190 nm (TIR-1) - B11: 11500-12510 nm (TIR-2) When to use: - Long-term land-cover change studies leveraging the unbroken Landsat record back to 1972 - Thermal infrared analysis (urban heat, surface temperature, fire detection) - Continental and global mapping where 30 m is sufficient - Cross-validation with Sentinel-2 in harmonised time series When NOT to use: - Field-scale agriculture (30 m too coarse for many farms) - Object detection or detailed feature extraction - Sub-weekly monitoring --- ### NAIP (USDA (National Agriculture Imagery Program)) URL: https://docs.geopera.com/data/naip 60 cm aerial multispectral, US-only, biennial archive since 2003 Specifications: - Country: United States - Provider: USDA - Operator: USDA (National Agriculture Imagery Program) - Product type: Multispectral (aerial) - System type: Aerial - Availability: archive only - Available since: 2003 - Resolution: All bands 60 cm (varies by year — 30 cm to 1 m) - Bit depth: 8-bit - Swath width: Aerial flight lines - Off-nadir range: N/A (aerial) - Satellite count: 0 - Satellites: Aircraft-mounted sensors (multiple operators) - Orbit: Aerial at Variable - Revisit (theoretical): Biennial (every 2 years) - Revisit (real-world, acceptable quality): Biennial - Acquisition modes: Mono - Delivery formats: GeoTIFF, County or state mosaics - Positional accuracy: < 6 m CE90 - CRS: NAD83 / state plane - Min AOI: tasking —; catalog Aerial flight lines - Min charge: tasking —; catalog 25 km² - Geographic restrictions: United States only (CONUS, Hawaii, Alaska, Puerto Rico) - Processing levels: Orthorectified, NAIP-standard Bands (4): - Red: 604-664 nm - Green: 533-587 nm - Blue: 420-492 nm - NIR: 833-920 nm When to use: - Free, sub-meter US imagery for agriculture, vegetation, infrastructure - Multi-decade time-series of US land use change - Validation dataset for satellite work over the US When NOT to use: - Anything outside the United States - Time-sensitive work — biennial cadence only --- ### Sentinel-2 (European Space Agency (ESA)) URL: https://docs.geopera.com/data/sentinel-2 10m multispectral imagery, free, global, every 5 days Specifications: - Country: European Union - Provider: ESA - Operator: European Space Agency (ESA) - Product type: Multispectral - System type: Constellation - Availability: archive - Available since: 2017 (Europe), 2018 (global) - Resolution: Visible/NIR (B02–B04, B08) 10 m; Red edge + SWIR (B05–B07, B8A, B11–B12) 20 m; Atmospheric (B01, B09, B10) 60 m - Bit depth: 12-bit acquired, 16-bit delivered - Swath width: 290 km - Off-nadir range: Fixed nadir - Satellite count: 3 - Satellites: Sentinel-2A, 2B, 2C - Orbit: Sun-synchronous at 786 km - Revisit (theoretical): 5 days - Revisit (real-world, acceptable quality): 5 days at equator (combined) - Acquisition modes: Mono - Delivery formats: GeoTIFF, JPEG2000, GeoJSON/JSON/XML metadata, JPEG thumbnails - Positional accuracy: 8 m CE95 absolute - CRS: UTM-WGS84 - Min AOI: tasking —; catalog AOI; output is 110 × 110 km scene - Min charge: tasking —; catalog Free - Geographic restrictions: None — global free data - Processing levels: Level 2A (orthorectified, surface reflectance) Bands (13): - B1: 443 nm (Coastal aerosol) - B2: 490 nm (Blue) - B3: 560 nm (Green) - B4: 665 nm (Red) - B5: 705 nm (Vegetation Red Edge) - B6: 740 nm (Vegetation Red Edge) - B7: 783 nm (Vegetation Red Edge) - B8: 842 nm (NIR) - B8A: 865 nm (Vegetation Red Edge) - B9: 945 nm (Water vapour) - B10: 1375 nm (SWIR - Cirrus) - B11: 1610 nm (SWIR) - B12: 2190 nm (SWIR) When to use: - Time-series vegetation, water, and land-cover monitoring at moderate resolution - Cost-free wide-area baseline imagery before commissioning higher-resolution captures - Algorithm development and validation against an open, well-calibrated dataset - Long-term change detection (multi-year archive) When NOT to use: - Object-level identification (vehicles, individual buildings) — resolution is too coarse - Sub-weekly cadence over a single point — combined revisit caps at 5 days - Rapid response to acute events smaller than ~30 m --- ### SuperView-1 (SPACEWILL (CASIC)) URL: https://docs.geopera.com/data/superview-1 50 cm multispectral, four-satellite Chinese constellation Specifications: - Country: China - Provider: SPACEWILL - Operator: SPACEWILL (CASIC) - Product type: Multispectral - System type: Constellation - Availability: tasking + archive - Available since: 2017 - Resolution: Panchromatic 50 cm; Multispectral 2 m - Bit depth: 8-bit, 11-bit - Swath width: 12 km - Off-nadir range: Up to 30° - Satellite count: 4 - Satellites: SuperView-1 (4 satellites) - Orbit: Sun-synchronous at 530 km - Revisit (theoretical): < 1 day - Revisit (real-world, acceptable quality): 1–3 days - Acquisition modes: Mono, Stereo - Delivery formats: GeoTIFF, XML metadata - Positional accuracy: ~5 m CE90 - CRS: WGS84 - Min AOI: tasking 2 km wide; catalog 2 km wide - Min charge: tasking 100 km²; catalog 25 km² - Geographic restrictions: Chinese ground station coverage. - Processing levels: Custom (full Geopera pipeline applied) Bands (4): - Blue: 450-520 nm - Green: 520-590 nm - Red: 630-690 nm - NIR: 770-890 nm When to use: - Sub-daily revisit at sub-meter resolution from a Chinese provider - Cost-effective alternative to Vantor Legion When NOT to use: - Customers with regulatory restrictions on Chinese data --- ### SuperView-2 (SPACEWILL (CASIC)) URL: https://docs.geopera.com/data/superview-2 42 cm multispectral with red-edge, SPACEWILL's newer constellation Specifications: - Country: China - Provider: SPACEWILL - Operator: SPACEWILL (CASIC) - Product type: Multispectral - System type: Constellation - Availability: tasking + archive - Available since: 2022 - Resolution: Panchromatic 42 cm; Multispectral 1.68 m - Bit depth: 10-bit - Swath width: 12 km - Off-nadir range: Up to 35° - Satellite count: 2 - Satellites: SuperView-2 (2 satellites) - Orbit: Sun-synchronous at 500 km - Revisit (theoretical): Every 1 day - Revisit (real-world, acceptable quality): 1–3 days - Acquisition modes: Mono, Stereo - Delivery formats: GeoTIFF, XML metadata - Positional accuracy: ~5 m CE90 - CRS: WGS84 - Min AOI: tasking 2 km wide; catalog 2 km wide - Min charge: tasking 100 km²; catalog 25 km² - Geographic restrictions: Chinese ground station coverage. - Processing levels: Custom (full Geopera pipeline applied) Bands (7): - Blue: 450-520 nm - Green: 520-590 nm - Red: 630-690 nm - Red_Edge: 690-730 nm - NIR1: 770-890 nm - NIR2: 860-1040 nm - SWIR: 1550-1750 nm When to use: - Sub-50 cm resolution from a Chinese provider with red-edge support - Precision agriculture indices needing red-edge band When NOT to use: - Customers with regulatory restrictions on Chinese data --- ### SuperView Neo (SPACEWILL (CASIC)) URL: https://docs.geopera.com/data/superview-30cm 30 cm multispectral, SPACEWILL's flagship SuperView Neo Specifications: - Country: China - Provider: SPACEWILL - Operator: SPACEWILL (CASIC) - Product type: Multispectral - System type: Constellation - Availability: tasking + archive - Available since: 2023 - Resolution: Panchromatic 30 cm; Multispectral 1.20 m - Bit depth: 10-bit - Swath width: 12 km - Off-nadir range: Up to 35° - Satellite count: 2 - Satellites: SuperView Neo (2 satellites) - Orbit: Sun-synchronous at 500 km - Revisit (theoretical): < 1 day - Revisit (real-world, acceptable quality): 1–2 days - Acquisition modes: Mono, Stereo - Delivery formats: GeoTIFF, XML metadata - Positional accuracy: ~5 m CE90 - CRS: WGS84 - Min AOI: tasking 2 km wide; catalog 2 km wide - Min charge: tasking 100 km²; catalog 25 km² - Geographic restrictions: Chinese ground station coverage. - Processing levels: Custom (full Geopera pipeline applied) Bands (4): - Blue: 450-520 nm - Green: 520-590 nm - Red: 630-690 nm - NIR: 770-890 nm When to use: - 30 cm resolution from a Chinese provider at lower cost than Vantor - Defence and security customers without US-source restrictions When NOT to use: - Customers with regulatory restrictions on Chinese data - Workflows requiring SWIR --- ### TripleSat (21AT) URL: https://docs.geopera.com/data/triplesat 80 cm multispectral, three-satellite constellation with daily revisit Specifications: - Country: China (operated internationally) - Provider: 21AT - Operator: 21AT - Product type: Multispectral - System type: Constellation - Availability: tasking + archive - Available since: 2016 - Resolution: Panchromatic 80 cm; Multispectral 3.20 m - Bit depth: 8-bit, 16-bit - Swath width: 23.4 km - Off-nadir range: Up to 45° - Satellite count: 3 - Satellites: TripleSat (3 satellites) - Orbit: Sun-synchronous at 651 km - Revisit (theoretical): Daily - Revisit (real-world, acceptable quality): 1–3 days - Acquisition modes: Mono - Delivery formats: GeoTIFF, XML/TXT metadata, JPEG/PNG thumbnails - Positional accuracy: < 20 m CE90 absolute at nadir - CRS: Geographic-WGS84 - Min AOI: tasking 3 km wide; catalog 3 km wide - Min charge: tasking 70 km²; catalog 25 km² - Geographic restrictions: Chinese ground station coverage. - Processing levels: Level 1 (Primary with RPC), Level 2A (Georectified), Level 4, Level 4A — all with TOA radiometric Bands (4): - Blue: 450-520 nm - Green: 520-590 nm - Red: 630-690 nm - NIR: 770-890 nm When to use: - Daily revisit at sub-meter resolution - Wide-area baseline mapping with three-satellite coverage - Low minimum charge (5 km²) for small AOIs When NOT to use: - Sub-meter requirements - Customers with regulatory restrictions on Chinese data --- ### WorldView 1 (Vantor (formerly MAXAR)) URL: https://docs.geopera.com/data/worldview-1 50 cm panchromatic — black and white only, no spectral bands Specifications: - Country: United States - Provider: MAXAR - Operator: Vantor (formerly MAXAR) - Product type: Panchromatic - System type: Constellation member (9+ Vantor) - Availability: tasking + archive - Available since: 2007 - Resolution: Panchromatic 50 cm - Bit depth: 16-bit; 8-bit Map-Ready also available - Swath width: 17.7 km - Off-nadir range: 5°–45° - Satellite count: 1 - Satellites: WorldView-1 (Vantor constellation) - Orbit: Sun-synchronous at 496 km - Revisit (theoretical): 15 times daily (Vantor constellation) - Revisit (real-world, acceptable quality): 1–5 days - Acquisition modes: Mono - Delivery formats: GeoTIFF, XML/TXT metadata, SHP masks, JPEG thumbnails - Positional accuracy: < 5 m CE90 absolute - CRS: UTM-WGS84 - Min AOI: tasking 3 km wide; catalog 3 km wide - Min charge: tasking 50 km²; catalog 25 km² - Geographic restrictions: Some areas restricted by US export controls - Processing levels: Primary (Basic), Georectified (TOA), Orthorectified (TOA) Bands (1): - Panchromatic: 400-900 nm When to use: - Visual interpretation, base mapping, infrastructure monitoring - Stereo pairs for DSM/DTM generation - Defence and intelligence work where colour is unnecessary When NOT to use: - Any spectral analysis (single panchromatic band — no NDVI, NDWI, etc.) - Vegetation, water, mineral, or any quantitative analytics --- ### WorldView 2 (Vantor (formerly MAXAR)) URL: https://docs.geopera.com/data/worldview-2 50 cm multispectral with 8 bands, 16-year archive since 2009 Specifications: - Country: United States - Provider: MAXAR - Operator: Vantor (formerly MAXAR) - Product type: Multispectral - System type: Constellation member (9+ Vantor) - Availability: tasking + archive - Available since: 2009 - Resolution: Panchromatic 50 cm; Pansharpened 50 cm; Multispectral 2 m - Bit depth: 16-bit (System/Ortho-Ready); 8-bit (Map-Ready Pansharpened) - Swath width: 16.4 km - Off-nadir range: 5°–45° - Satellite count: 1 - Satellites: WorldView-2 (Vantor constellation) - Orbit: Sun-synchronous at 770 km - Revisit (theoretical): 15 times daily (Vantor constellation) - Revisit (real-world, acceptable quality): 1–4 days - Acquisition modes: Mono, Stereo (tasking) - Delivery formats: GeoTIFF, XML/TXT metadata, SHP masks, JPEG thumbnails - Positional accuracy: < 5 m CE90 absolute - CRS: UTM-WGS84 - Min AOI: tasking 3 km wide; catalog 3 km wide - Min charge: tasking 50 km²; catalog 25 km² - Geographic restrictions: Some areas restricted by US export controls - Processing levels: Primary (Basic), Georectified (Surface Reflectance), Orthorectified (Surface Reflectance, Display) Bands (9): - Panchromatic: 450-800 nm - Coastal: 400-450 nm - Blue: 450-510 nm - Green: 510-580 nm - Yellow: 585-625 nm - Red: 630-690 nm - Red_Edge: 705-745 nm - NIR1: 770-895 nm - NIR2: 860-1040 nm When to use: - Long historical archive back to 2009 - 8-band multispectral analytics where 30 cm is overkill - Cost-effective alternative to WorldView-3 for non-mineral work When NOT to use: - Sub-50 cm resolution requirements - SWIR analysis (no SWIR bands) --- ### WorldView 3 (Vantor (formerly MAXAR)) URL: https://docs.geopera.com/data/worldview-3 30 cm multispectral with 8 bands and SWIR, the gold standard for commercial Earth observation Specifications: - Country: United States - Provider: MAXAR - Operator: Vantor (formerly MAXAR) - Product type: Multispectral + SWIR - System type: Constellation member (9+ Vantor) - Availability: tasking + archive - Available since: 2014 - Resolution: Panchromatic 30 cm; Pansharpened 30 cm; Multispectral 1.20 m; SWIR 3.70 m - Bit depth: 16-bit (System/Ortho-Ready); 8-bit (Map-Ready Pansharpened) - Swath width: 13.1 km - Off-nadir range: 5°–45° - Satellite count: 1 - Satellites: WorldView-3 (Vantor constellation) - Orbit: Sun-synchronous at 617 km - Revisit (theoretical): 15 times daily (Vantor constellation) - Revisit (real-world, acceptable quality): 1–3 days at acceptable quality - Acquisition modes: Mono, Stereo (tasking) - Delivery formats: GeoTIFF, XML/TXT metadata, SHP masks, JPEG thumbnails - Positional accuracy: < 5 m CE90 absolute - CRS: UTM-WGS84 - Min AOI: tasking 2 km wide; catalog 2 km wide - Min charge: tasking 50 km²; catalog 25 km² - Geographic restrictions: Some areas restricted by US export controls - Processing levels: Primary (Basic), Georectified (Surface Reflectance), Orthorectified (Surface Reflectance, Display) Bands (17): - Panchromatic: 450-800 nm - Coastal: 400-450 nm - Blue: 450-510 nm - Green: 510-580 nm - Yellow: 585-625 nm - Red: 630-690 nm - Red Edge: 705-745 nm - NIR1: 770-895 nm - NIR2: 860-1040 nm - SWIR1: 1195-1225 nm - SWIR2: 1550-1590 nm - SWIR3: 1640-1680 nm - SWIR4: 1710-1750 nm - SWIR5: 2145-2185 nm - SWIR6: 2185-2225 nm - SWIR7: 2235-2285 nm - SWIR8: 2295-2365 nm When to use: - Mineral exploration and geological mapping (8 SWIR bands cover diagnostic absorption features) - Defence and intelligence work where 30 cm is non-negotiable - Coastal and bathymetric studies (coastal blue band) - High-precision change detection on infrastructure When NOT to use: - Wide-area monitoring where cost per km² matters - Customers with budget pressure who can flex on resolution to 50 cm --- ### WorldView 4 (Vantor (formerly MAXAR)) URL: https://docs.geopera.com/data/worldview-4 31 cm multispectral with 4 bands — archive only, sensor failed in orbit 2019 Specifications: - Country: United States - Provider: MAXAR - Operator: Vantor (formerly MAXAR) - Product type: Multispectral - System type: Satellite - Availability: archive only - Available since: 2016 - Resolution: Panchromatic 31 cm; Multispectral 1.24 m - Bit depth: 16-bit; 8-bit Map-Ready also available - Swath width: 13.1 km - Off-nadir range: 5°–45° - Satellite count: 0 - Satellites: WorldView-4 (failed in orbit January 2019) - Orbit: Sun-synchronous at 617 km - Revisit (theoretical): N/A — archive only - Revisit (real-world, acceptable quality): N/A - Acquisition modes: Mono, Stereo (archive) - Delivery formats: GeoTIFF, XML/TXT metadata, SHP masks, JPEG thumbnails - Positional accuracy: < 5 m CE90 absolute - CRS: UTM-WGS84 - Min AOI: tasking —; catalog 2 km wide - Min charge: tasking —; catalog 25 km² - Geographic restrictions: Some areas restricted by US export controls - Processing levels: Primary (Basic), Georectified (Surface Reflectance), Orthorectified (Surface Reflectance, Display) Bands (5): - Panchromatic: 450-800 nm - Blue: 450-510 nm - Green: 510-580 nm - Red: 630-690 nm - NIR: 770-895 nm When to use: - Historical 30 cm archive between 2016 and February 2019 - Filling gaps in WorldView-3 coverage during that period When NOT to use: - Any work needing data after February 2019 (sensor failed in orbit) - New tasking — only archive available --- ### WorldView Legion (Vantor (formerly MAXAR)) URL: https://docs.geopera.com/data/worldview-legion 34 cm multispectral, Vantor's newest constellation with 15× daily revisit Specifications: - Country: United States - Provider: MAXAR - Operator: Vantor (formerly MAXAR) - Product type: Multispectral - System type: Constellation member (9+ Vantor) - Availability: tasking + archive - Available since: 2024 - Resolution: Panchromatic 34 cm; Pansharpened 34 cm; Multispectral 1.36 m - Bit depth: 8-bit (HD15); 16-bit (other products) - Swath width: 10 km - Off-nadir range: 5°–45° - Satellite count: 6 - Satellites: WorldView Legion (6 satellites — part of 9+ Vantor constellation) - Orbit: Mid-inclination 45° + Sun-synchronous at 518 km - Revisit (theoretical): 15 times daily (Vantor constellation) - Revisit (real-world, acceptable quality): Several daily revisits at mid-latitudes - Acquisition modes: Mono - Delivery formats: GeoTIFF, XML/TXT metadata, SHP masks, JPEG thumbnails - Positional accuracy: < 5 m CE90 absolute - CRS: UTM-WGS84 - Min AOI: tasking 2 km wide; catalog 2 km wide - Min charge: tasking 50 km²; catalog 25 km² - Geographic restrictions: Some areas restricted by US export controls - Processing levels: Primary (Basic), Georectified (Surface Reflectance), Orthorectified (Surface Reflectance, Display) Bands (9): - Panchromatic: 450-800 nm - Coastal: 400-450 nm - Blue: 450-510 nm - Green: 510-580 nm - Yellow: 585-625 nm - Red: 630-690 nm - Red_Edge: 705-745 nm - NIR1: 770-895 nm - NIR2: 860-1040 nm When to use: - High-resolution monitoring with sub-daily revisit needs - Time-critical tasking (events, construction milestones, security) - Workflows that previously needed Planet SkySat plus a higher-res capture When NOT to use: - Customers needing the long historical archive of WorldView-2/3 - Spectral work requiring SWIR --- ## Satellite Sensors (spectral bands) ### BJ3A (21AT) URL: https://docs.geopera.com/spectral-indices/sensors/bj3a Bands (4): Blue = 450-520 nm; Green = 520-590 nm; Red = 630-690 nm; NIR = 770-890 nm Compatible indices (87): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, gari, gemi2, mcari1, mcari2, msavi_hyper, msr670, mtvi1, mtvi2, ndwi_mcfeeters, ngrdi, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, sevi, si, sipi, sipi2, sr, sr2, tdvi, tgi, trivi, tsavi, tvi, vari, vari_visible, vgnirbi, vi6t, vig, vrnirbi, wdrvi, wdvi --- ### BJ3N (21AT) URL: https://docs.geopera.com/spectral-indices/sensors/bj3n Bands (4): Blue = 450-520 nm; Green = 520-590 nm; Red = 630-690 nm; NIR = 770-890 nm Compatible indices (87): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, gari, gemi2, mcari1, mcari2, msavi_hyper, msr670, mtvi1, mtvi2, ndwi_mcfeeters, ngrdi, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, sevi, si, sipi, sipi2, sr, sr2, tdvi, tgi, trivi, tsavi, tvi, vari, vari_visible, vgnirbi, vi6t, vig, vrnirbi, wdrvi, wdvi --- ### Dragonette-1 (Wyvern) URL: https://docs.geopera.com/spectral-indices/sensors/dragonette-001 Bands (23): Band 1 = 503 nm (20.1 nm FWHM); Band 2 = 510 nm (20.4 nm FWHM); Band 3 = 519 nm (20.8 nm FWHM); Band 4 = 535 nm (21.4 nm FWHM); Band 5 = 549 nm (22.0 nm FWHM); Band 6 = 570 nm (22.8 nm FWHM); Band 7 = 584 nm (23.4 nm FWHM); Band 8 = 600 nm (24.0 nm FWHM); Band 9 = 614 nm (24.6 nm FWHM); Band 10 = 635 nm (25.4 nm FWHM); Band 11 = 649 nm (26.0 nm FWHM); Band 12 = 660 nm (26.4 nm FWHM); Band 13 = 669 nm (26.8 nm FWHM); Band 14 = 679 nm (27.2 nm FWHM); Band 15 = 690 nm (27.6 nm FWHM); Band 16 = 699 nm (28.0 nm FWHM); Band 17 = 711 nm (28.4 nm FWHM); Band 18 = 722 nm (28.9 nm FWHM); Band 19 = 734 nm (29.4 nm FWHM); Band 20 = 750 nm (30.0 nm FWHM); Band 21 = 764 nm (30.6 nm FWHM); Band 22 = 782 nm (31.3 nm FWHM); Band 23 = 799 nm (32.0 nm FWHM) Compatible indices (108): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bi, carter2, ci_rededge, ci_soil, cirededge710, cri550, cri700, ctr3, ctr4, gari, gemi2, mcari, mcari1, mcari2, mcari710, mcari_mtvi2, mcari_osavi, mcari_osavi750, msavi_hyper, msr670, msr705_750, mtvi1, mtvi2, ndre, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osavi2, osi, pi, pisi, pri_standard, psri2, rcc, redsi, reip1, reip2, rendvi, rep, rgbvi, rgri, ri4xs, rndvi, s2rep, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sr, sr2, sr3, sr555, sr705, tcari, tcari_osavi, tcari_osavi705, tcariosavi, tcariosavi705, tci, tsavi, tvi, tvi_broge, tvi_triangular, vari_rededge, vari_visible, vi700, vig, vog1, vog2, vog3, wdrvi, wdvi --- ### Dragonette-2/3 (Wyvern) URL: https://docs.geopera.com/spectral-indices/sensors/dragonette-002 Bands (31): Band1 = 445 nm; Band2 = 465 nm; Band3 = 480 nm; Band4 = 489 nm; Band5 = 502 nm; Band6 = 509 nm; Band7 = 519 nm; Band8 = 534 nm; Band9 = 549 nm; Band10 = 569 nm; Band11 = 584 nm; Band12 = 600 nm; Band13 = 614 nm; Band14 = 635 nm; Band15 = 650 nm; Band16 = 660 nm; Band17 = 670 nm; Band18 = 680 nm; Band19 = 690 nm; Band20 = 700 nm; Band21 = 712 nm; Band22 = 721 nm; Band23 = 734 nm; Band24 = 750 nm; Band25 = 765 nm; Band26 = 782 nm; Band27 = 800 nm; Band28 = 815 nm; Band29 = 832 nm; Band30 = 850 nm; Band31 = 870 nm Compatible indices (130): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cirededge710, cri550, cri700, ctr3, ctr4, gari, gemi2, lci, mcari, mcari1, mcari2, mcari710, mcari_mtvi2, mcari_osavi, mcari_osavi750, mnd705, msavi_hyper, msr670, msr705, msr705_750, mtvi1, mtvi2, ndre, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osavi2, osi, pi, pisi, pndvi, pri_standard, psri2, rcc, redsi, reip1, reip2, rendvi, rep, rgbvi, rgri, ri4xs, rndvi, s2rep, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, sr705, tcari, tcari_osavi, tcari_osavi705, tcariosavi, tcariosavi705, tci, tdvi, tgi, trivi, trrvi, tsavi, ttvi, tvi, tvi_broge, tvi_triangular, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vog1, vog2, vog3, vrnirbi, wdrvi, wdvi --- ### Gaofen-1 (CNSA) URL: https://docs.geopera.com/spectral-indices/sensors/gaofen-1 Bands (5): Panchromatic = 450-890 nm; Blue = 450-520 nm; Green = 520-590 nm; Red = 630-690 nm; NIR = 770-890 nm Compatible indices (105): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, ctr3, ctr4, gari, gemi2, lci, mcari1, mcari2, msavi_hyper, msr670, msr705, mtvi1, mtvi2, ndre, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, tcari, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_triangular, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vrnirbi, wdrvi, wdvi --- ### Gaofen-2 (CNSA) URL: https://docs.geopera.com/spectral-indices/sensors/gaofen-2 Bands (5): Panchromatic = 450-890 nm; Blue = 450-520 nm; Green = 520-590 nm; Red = 630-690 nm; NIR = 770-890 nm Compatible indices (105): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, ctr3, ctr4, gari, gemi2, lci, mcari1, mcari2, msavi_hyper, msr670, msr705, mtvi1, mtvi2, ndre, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, tcari, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_triangular, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vrnirbi, wdrvi, wdvi --- ### GeoEye-1 (MAXAR) URL: https://docs.geopera.com/spectral-indices/sensors/geoeye-1 Bands (5): Panchromatic = 450-800 nm; Blue = 450-520 nm; Green = 520-600 nm; Red = 625-695 nm; NIR = 760-900 nm Compatible indices (112): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cirededge710, cri550, cri700, ctr3, ctr4, gari, gemi2, lci, mcari1, mcari2, mcari710, mcari_osavi750, mnd705, msavi_hyper, msr670, msr705, msr705_750, mtvi1, mtvi2, ndre, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osavi2, osi, pi, pisi, pndvi, psri2, rcc, redsi, rendvi, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, tcari_osavi705, tcariosavi, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_broge, tvi_triangular, vari, vari_rededge, vari_visible, vgnirbi, vi6t, vig, vog3, vrnirbi, wdrvi, wdvi --- ### Göktürk-1 (IMPROSAT) URL: https://docs.geopera.com/spectral-indices/sensors/gokturk Bands (5): Panchromatic = 450-900 nm; Blue = 450-520 nm; Green = 520-600 nm; Red = 630-690 nm; NIR = 760-900 nm Compatible indices (105): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, ctr3, ctr4, gari, gemi2, lci, mcari1, mcari2, msavi_hyper, msr670, msr705, mtvi1, mtvi2, ndre, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, tcari, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_triangular, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vrnirbi, wdrvi, wdvi --- ### Jilin-1 (CG Satellite) URL: https://docs.geopera.com/spectral-indices/sensors/jl1-50cm Bands (4): Blue = 450-520 nm; Green = 520-590 nm; Red = 630-690 nm; NIR = 770-890 nm Compatible indices (87): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, gari, gemi2, mcari1, mcari2, msavi_hyper, msr670, mtvi1, mtvi2, ndwi_mcfeeters, ngrdi, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, sevi, si, sipi, sipi2, sr, sr2, tdvi, tgi, trivi, tsavi, tvi, vari, vari_visible, vgnirbi, vi6t, vig, vrnirbi, wdrvi, wdvi --- ### Jilin-1 GF03D (CG Satellite) URL: https://docs.geopera.com/spectral-indices/sensors/jl1-75cm Bands (5): Panchromatic = 450-890 nm; Blue = 450-520 nm; Green = 520-590 nm; Red = 630-690 nm; NIR = 770-890 nm Compatible indices (105): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, ctr3, ctr4, gari, gemi2, lci, mcari1, mcari2, msavi_hyper, msr670, msr705, mtvi1, mtvi2, ndre, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, tcari, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_triangular, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vrnirbi, wdrvi, wdvi --- ### KOMPSAT-3 (SIIS) URL: https://docs.geopera.com/spectral-indices/sensors/kompsat-3 Bands (5): Panchromatic = 450-900 nm; Blue = 450-520 nm; Green = 520-600 nm; Red = 630-690 nm; NIR = 760-900 nm Compatible indices (105): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, ctr3, ctr4, gari, gemi2, lci, mcari1, mcari2, msavi_hyper, msr670, msr705, mtvi1, mtvi2, ndre, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, tcari, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_triangular, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vrnirbi, wdrvi, wdvi --- ### KOMPSAT-3A (SIIS) URL: https://docs.geopera.com/spectral-indices/sensors/kompsat-3a Bands (5): Panchromatic = 450-900 nm; Blue = 450-520 nm; Green = 520-600 nm; Red = 630-690 nm; NIR = 760-900 nm Compatible indices (105): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, ctr3, ctr4, gari, gemi2, lci, mcari1, mcari2, msavi_hyper, msr670, msr705, mtvi1, mtvi2, ndre, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, tcari, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_triangular, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vrnirbi, wdrvi, wdvi --- ### Landsat 8/9 (USGS/NASA) URL: https://docs.geopera.com/spectral-indices/sensors/landsat-8-9 Bands (11): B1 = 435-451 nm (Coastal/Aerosol); B2 = 452-512 nm (Blue); B3 = 533-590 nm (Green); B4 = 636-673 nm (Red); B5 = 851-879 nm (NIR); B6 = 1566-1651 nm (SWIR-1); B7 = 2107-2294 nm (SWIR-2); B8 = 503-676 nm (Panchromatic); B9 = 1363-1384 nm (Cirrus); B10 = 10600-11190 nm (TIR-1); B11 = 11500-12510 nm (TIR-2) Compatible indices (115): alteration, arvi, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bwdrvi, ci_soil, cri550, dvi, evi, fe3_iron, ferric_oxides, ferrous_iron, ferrous_silicates, gari, gdvi, gemi, gemi2, gndvi, gosavi, gossan, grvi, gsavi, gvi_tc, hue, intensity, ipvi, laterite, lswi, mndwi, msavi, nbr, ndbi, ndii, ndmi2, ndsi, ndvi, ndvi_classic, ndvic, ndwi, ngrdi, nirv, nirvh2, nirvp, nli, nmdi, norm_g, norm_nir, norm_r, normg, normnir, normr, nrfig, nrfir, nsds, nsdsi1, nsdsi2, nsdsi3, nstv1, nstv2, nwi, ocvi, osi, pi, pisi, pndvi, rcc, rgbvi, rgri, ri4xs, rndvi, s3, sarvi, saturation, savi, savi2, savit, sbi, sevi, si, sipi, slavi, snirvlswi, snirvndpi, snirvndvilswip, snirvndvilswis, snirvswir, sr, sr2, swi, swm, tdvi, tgi, trivi, tsavi, tvi, ui, vari, vari_visible, vgnirbi, vi6t, vibi, vig, vrnirbi, wdrvi, wdvi, wet_tc, wi1, wi2, wi2015, wri --- ### NAIP (USDA) URL: https://docs.geopera.com/spectral-indices/sensors/naip Bands (4): Red = 604-664 nm; Green = 533-587 nm; Blue = 420-492 nm; NIR = 833-920 nm Compatible indices (74): arvi, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bwdrvi, carter6, ci_soil, dvi, evi, fe3_iron, gari, gdvi, gemi, gemi2, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, ndwi_mcfeeters, ngrdi, nirv, nirvh2, nirvp, nli, norm_g, norm_nir, norm_r, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi, savi2, savit, sevi, si, sipi, sr, sr2, tdvi, tgi, trivi, tsavi, tvi, vari, vari_visible, vgnirbi, vi6t, vig, vrnirbi, wdrvi, wdvi --- ### Sentinel-2 (ESA) URL: https://docs.geopera.com/spectral-indices/sensors/sentinel-2 Bands (13): B1 = 443 nm (Coastal aerosol); B2 = 490 nm (Blue); B3 = 560 nm (Green); B4 = 665 nm (Red); B5 = 705 nm (Vegetation Red Edge); B6 = 740 nm (Vegetation Red Edge); B7 = 783 nm (Vegetation Red Edge); B8 = 842 nm (NIR); B8A = 865 nm (Vegetation Red Edge); B9 = 945 nm (Water vapour); B10 = 1375 nm (SWIR - Cirrus); B11 = 1610 nm (SWIR); B12 = 2190 nm (SWIR) Compatible indices (168): ndvi, ari, ari2, arvi, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cirededge710, cri550, cri700, ctr4, dvi, evi, fe3_iron, ferric_oxides, ferrous_iron, ferrous_silicates, gari, gdvi, gemi, gemi2, gndvi, gosavi, gossan, grvi, gsavi, gvi_tc, hue, intensity, ipvi, laterite, lci, mcari, mcari1, mcari2, mcari710, mcari_mtvi2, mcari_osavi, mcari_osavi750, mnd705, mndwi, msavi, msavi_hyper, msr670, msr705, msr705_750, mtvi1, mtvi2, nbr, nd_mir_nir, ndbi, ndmi2, ndre, ndsi, ndvi_classic, ndvic, ndwi, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, nmdi, norm_g, norm_nir, norm_r, normg, normnir, normr, nrfig, nrfir, nsds, nsdsi1, nsdsi2, nsdsi3, nstv1, nstv2, nwi, ocvi, osavi2, osi, pi, pisi, pndvi, psri2, rcc, redsi, reip1, reip2, rendvi, rep, rgbvi, rgri, ri4xs, rndvi, s2rep, s2wi, s3, sarvi, saturation, savi, savi2, savit, sbi, seli, sevi, si, sipi, sipi2, slavi, snirvlswi, snirvndpi, snirvndvilswip, snirvndvilswis, snirvswir, sr, sr2, sr3, sr555, sr705, swi, swm, tcari, tcari_osavi, tcari_osavi705, tcariosavi, tcariosavi705, tci, tdvi, tgi, trivi, trrvi, tsavi, ttvi, tvi, tvi_broge, tvi_triangular, twi, ui, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vibi, vig, vog1, vrnirbi, wdrvi, wdvi, wet_tc, wi1, wi2, wi2015, wri --- ### SuperView Neo (SPACEWILL) URL: https://docs.geopera.com/spectral-indices/sensors/superview-30cm Bands (4): Blue = 450-520 nm; Green = 520-590 nm; Red = 630-690 nm; NIR = 770-890 nm Compatible indices (87): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, gari, gemi2, mcari1, mcari2, msavi_hyper, msr670, mtvi1, mtvi2, ndwi_mcfeeters, ngrdi, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, sevi, si, sipi, sipi2, sr, sr2, tdvi, tgi, trivi, tsavi, tvi, vari, vari_visible, vgnirbi, vi6t, vig, vrnirbi, wdrvi, wdvi --- ### SuperView-1 (SPACEWILL) URL: https://docs.geopera.com/spectral-indices/sensors/superview-1 Bands (4): Blue = 450-520 nm; Green = 520-590 nm; Red = 630-690 nm; NIR = 770-890 nm Compatible indices (87): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, gari, gemi2, mcari1, mcari2, msavi_hyper, msr670, mtvi1, mtvi2, ndwi_mcfeeters, ngrdi, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, sevi, si, sipi, sipi2, sr, sr2, tdvi, tgi, trivi, tsavi, tvi, vari, vari_visible, vgnirbi, vi6t, vig, vrnirbi, wdrvi, wdvi --- ### SuperView-2 (SPACEWILL) URL: https://docs.geopera.com/spectral-indices/sensors/superview-2 Bands (7): Blue = 450-520 nm; Green = 520-590 nm; Red = 630-690 nm; Red_Edge = 690-730 nm; NIR1 = 770-890 nm; NIR2 = 860-1040 nm; SWIR = 1550-1750 nm Compatible indices (125): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, ctr4, ferric_oxides, gari, gemi2, gossan, lci, lswi, mcari1, mcari2, mcari_mtvi2, mcari_osavi, mndwi, mnli, msavi_hyper, msi, msr670, msr705, mtvi1, mtvi2, nbr, nd_mir_nir, ndbi, ndii, ndmi, ndmi2, ndre, ndvic, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, pwi, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, s3, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, swi, swm, tcari, tcari_osavi, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vibi, vig, vrnirbi, wdrvi, wdvi, wri --- ### TripleSat (21AT) URL: https://docs.geopera.com/spectral-indices/sensors/triplesat Bands (4): Blue = 450-520 nm; Green = 520-590 nm; Red = 630-690 nm; NIR = 770-890 nm Compatible indices (87): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, gari, gemi2, mcari1, mcari2, msavi_hyper, msr670, mtvi1, mtvi2, ndwi_mcfeeters, ngrdi, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, rcc, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, sevi, si, sipi, sipi2, sr, sr2, tdvi, tgi, trivi, tsavi, tvi, vari, vari_visible, vgnirbi, vi6t, vig, vrnirbi, wdrvi, wdvi --- ### WorldView 1 (MAXAR) URL: https://docs.geopera.com/spectral-indices/sensors/worldview-1 Bands (1): Panchromatic = 400-900 nm Compatible indices (4): nhfd, saturation, seli, vi6t --- ### WorldView 2 (MAXAR) URL: https://docs.geopera.com/spectral-indices/sensors/worldview-2 Bands (9): Panchromatic = 450-800 nm; Coastal = 400-450 nm; Blue = 450-510 nm; Green = 510-580 nm; Yellow = 585-625 nm; Red = 630-690 nm; Red_Edge = 705-745 nm; NIR1 = 770-895 nm; NIR2 = 860-1040 nm Compatible indices (113): gdvi, gndvi, gosavi, grvi, gsavi, msavi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, ari, ari2, arvi, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter1, carter2, carter6, ci_rededge, ci_soil, cri550, cri700, ctr3, ctr4, dvi, evi, fe3_iron, gari, gemi, gemi2, hue, intensity, ipvi, lci, mcari1, mcari2, mcari_mtvi2, mcari_osavi, msavi_hyper, msr670, msr705, mtvi1, mtvi2, ndre, ndvi, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, psri2, pwi, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, tcari, tcari_osavi, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_broge, tvi_triangular, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vrnirbi, wdrvi, wdvi --- ### WorldView 3 (MAXAR) URL: https://docs.geopera.com/spectral-indices/sensors/worldview-3 Bands (17): Panchromatic = 450-800 nm; Coastal = 400-450 nm; Blue = 450-510 nm; Green = 510-580 nm; Yellow = 585-625 nm; Red = 630-690 nm; Red Edge = 705-745 nm; NIR1 = 770-895 nm; NIR2 = 860-1040 nm; SWIR1 = 1195-1225 nm; SWIR2 = 1550-1590 nm; SWIR3 = 1640-1680 nm; SWIR4 = 1710-1750 nm; SWIR5 = 2145-2185 nm; SWIR6 = 2185-2225 nm; SWIR7 = 2235-2285 nm; SWIR8 = 2295-2365 nm Compatible indices (161): gdvi, gndvi, gosavi, grvi, gsavi, msavi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, alteration, ari, ari2, arvi, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter1, carter2, carter6, ci_rededge, ci_soil, cri550, cri700, ctr3, ctr4, dvi, evi, fe3_iron, ferric_oxides, ferrous_iron, ferrous_silicates, gari, gemi, gemi2, gossan, gvi_tc, host_rock, hue, intensity, ipvi, kaolinitic, laterite, lci, lswi, mcari1, mcari2, mcari_mtvi2, mcari_osavi, mndwi, mnli, msavi_hyper, msi, msr670, msr705, mtvi1, mtvi2, muscovite, nbr, nd_mir_nir, ndbi, ndii, ndli, ndmi, ndmi2, ndre, ndsi, ndvi, ndvic, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, nmdi, normg, normnir, normr, nrfig, nrfir, nsds, nsdsi1, nsdsi2, nsdsi3, nstv1, nstv2, nwi, ocvi, osi, pi, pisi, pndvi, psri2, pwi, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, s2wi, s3, sarvi, saturation, savi, savi2, savit, sbi, seli, sevi, si, sipi, sipi2, slavi, snirvlswi, snirvndpi, snirvndvilswip, snirvndvilswis, snirvswir, sr, sr2, sr3, sr555, swi, swm, tcari, tcari_osavi, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_broge, tvi_triangular, ui, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vrnirbi, wdrvi, wdvi, wet_tc, wi1, wi2 --- ### WorldView 4 (MAXAR) URL: https://docs.geopera.com/spectral-indices/sensors/worldview-4 Bands (5): Panchromatic = 450-800 nm; Blue = 450-510 nm; Green = 510-580 nm; Red = 630-690 nm; NIR = 770-895 nm Compatible indices (106): arvi, dvi, evi, fe3_iron, gdvi, gemi, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, msavi, ndvi, ndvi_classic, ndwi, norm_g, norm_nir, norm_r, savi, ari, ari2, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter2, ci_rededge, ci_soil, cri550, cri700, ctr3, ctr4, gari, gemi2, lci, mcari1, mcari2, msavi_hyper, msr670, msr705, mtvi1, mtvi2, ndre, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, psri2, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_broge, tvi_triangular, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vrnirbi, wdrvi, wdvi --- ### WorldView Legion (MAXAR) URL: https://docs.geopera.com/spectral-indices/sensors/worldview-legion Bands (9): Panchromatic = 450-800 nm; Coastal = 400-450 nm; Blue = 450-510 nm; Green = 510-580 nm; Yellow = 585-625 nm; Red = 630-690 nm; Red_Edge = 705-745 nm; NIR1 = 770-895 nm; NIR2 = 860-1040 nm Compatible indices (113): ari, ari2, arvi, arvi2, bai, bgi, bi, bi2, bndvi, bnirv, bri, bwdrvi, carter1, carter2, carter6, ci_rededge, ci_soil, cri550, cri700, ctr3, ctr4, dvi, evi, fe3_iron, gari, gdvi, gemi, gemi2, gndvi, gosavi, grvi, gsavi, hue, intensity, ipvi, lci, mcari1, mcari2, mcari_mtvi2, mcari_osavi, msavi, msavi_hyper, msr670, msr705, mtvi1, mtvi2, ndre, ndvi, ndvi_classic, ndwi, ndwi_mcfeeters, ngrdi, nhfd, nirv, nirvh2, nirvp, nli, norm_g, norm_nir, norm_r, normg, normnir, normr, ocvi, osi, pi, pisi, pndvi, psri2, pwi, rcc, redsi, rgbvi, rgri, ri4xs, rndvi, sarvi, saturation, savi, savi2, savit, seli, sevi, si, sipi, sipi2, sr, sr2, sr3, sr555, tcari, tcari_osavi, tcariosavi, tci, tdvi, tgi, trivi, trrvi, tsavi, tvi, tvi_broge, tvi_triangular, vari, vari700, vari_rededge, vari_visible, vgnirbi, vi6t, vi700, vig, vrnirbi, wdrvi, wdvi --- ## Spectral Indices ### Vegetation Category (149 indices) #### ARI — Anthocyanin Reflectance Index URL: https://docs.geopera.com/spectral-indices/ari Category: vegetation The Anthocyanin Reflectance Index (ARI) was developed by Gitelson et al. to non-destructively estimate anthocyanin content in plant leaves. It isolates the anthocyanin absorption peak around 550nm by subtracting the 700nm band that reflects only chlorophyll. ARI is particularly useful for monitoring plant stress, senescence, and physiological status. Formula: (1 / 550nm) - (1 / 700nm) Wavelengths: 550 (550), 700 (700) Applications: anthocyanin content estimation, plant stress detection, leaf senescence monitoring, autumn foliage assessment, fruit ripeness evaluation, plant physiological status References: Gitelson, A.A., Merzlyak, M.N., and Chivkunova, O.B. (2001) - Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74(1), 38-45 Sensor-specific formulas: - BJ3A: (1 / Green) - (1 / Red) - BJ3N: (1 / Green) - (1 / Red) - Dragonette-1: (1 / Band 5) - (1 / Band 16) - Dragonette-2/3: (1 / Band9) - (1 / Band20) - Gaofen-1: (1 / Green) - (1 / Panchromatic) - Gaofen-2: (1 / Green) - (1 / Panchromatic) - GeoEye-1: (1 / Green) - (1 / Red) - Göktürk-1: (1 / Green) - (1 / Panchromatic) - Jilin-1: (1 / Green) - (1 / Red) - Jilin-1 GF03D: (1 / Green) - (1 / Panchromatic) - KOMPSAT-3: (1 / Green) - (1 / Panchromatic) - KOMPSAT-3A: (1 / Green) - (1 / Panchromatic) - Sentinel-2: (1 / B3) - (1 / B5) - SuperView Neo: (1 / Green) - (1 / Red) - SuperView-1: (1 / Green) - (1 / Red) - SuperView-2: (1 / Green) - (1 / Red_Edge) - TripleSat: (1 / Green) - (1 / Red) - WorldView 2: (1 / Green) - (1 / Red_Edge) - WorldView 3: (1 / Green) - (1 / Red Edge) - WorldView 4: (1 / Green) - (1 / Red) - WorldView Legion: (1 / Green) - (1 / Red_Edge) --- #### ARVI — Atmospherically Resistant Vegetation Index URL: https://docs.geopera.com/spectral-indices/arvi Category: vegetation Vegetation index that minimizes atmospheric effects by incorporating blue band correction. More resistant to atmospheric scattering than NDVI, particularly useful in areas with atmospheric haze or high aerosol content. Formula: (NIR - Red - γ(Red - Blue)) / (NIR + Red - γ(Red - Blue)) Wavelengths: Blue (450-510 nm), Red (630-690 nm), NIR (780-1400 nm) Applications: Agriculture, Vegetation Analysis, Atmospheric Correction, Vegetation Vitality Assessment, Environmental Monitoring References: Kaufman & Tanré (1992); Bannari et al. (1995) Sensor-specific formulas: - BJ3A: (NIR - Red - γ(Red - Blue)) / (NIR + Red - γ(Red - Blue)) - BJ3N: (NIR - Red - γ(Red - Blue)) / (NIR + Red - γ(Red - Blue)) - Dragonette-1: (Band 22 - Band 10 - γ(Band 10 - Band 1)) / (Band 22 + Band 10 - γ(Band 10 - Band 1)) - Dragonette-2/3: (Band26 - Band14 - γ(Band14 - Band2)) / (Band26 + Band14 - γ(Band14 - Band2)) - Gaofen-1: (NIR - Red - γ(Red - Panchromatic)) / (NIR + Red - γ(Red - Panchromatic)) - Gaofen-2: (NIR - Red - γ(Red - Panchromatic)) / (NIR + Red - γ(Red - Panchromatic)) - GeoEye-1: (NIR - Red - γ(Red - Panchromatic)) / (NIR + Red - γ(Red - Panchromatic)) - Göktürk-1: (NIR - Red - γ(Red - Panchromatic)) / (NIR + Red - γ(Red - Panchromatic)) - Jilin-1: (NIR - Red - γ(Red - Blue)) / (NIR + Red - γ(Red - Blue)) - Jilin-1 GF03D: (NIR - Red - γ(Red - Panchromatic)) / (NIR + Red - γ(Red - Panchromatic)) - KOMPSAT-3: (NIR - Red - γ(Red - Panchromatic)) / (NIR + Red - γ(Red - Panchromatic)) - KOMPSAT-3A: (NIR - Red - γ(Red - Panchromatic)) / (NIR + Red - γ(Red - Panchromatic)) - Landsat 8/9: (B5 - B4 - γ(B4 - B1)) / (B5 + B4 - γ(B4 - B1)) - NAIP: (NIR - Red - γ(Red - Blue)) / (NIR + Red - γ(Red - Blue)) - Sentinel-2: (B8 - B4 - γ(B4 - B1)) / (B8 + B4 - γ(B4 - B1)) - SuperView Neo: (NIR - Red - γ(Red - Blue)) / (NIR + Red - γ(Red - Blue)) - SuperView-1: (NIR - Red - γ(Red - Blue)) / (NIR + Red - γ(Red - Blue)) - SuperView-2: (NIR1 - Red - γ(Red - Blue)) / (NIR1 + Red - γ(Red - Blue)) - TripleSat: (NIR - Red - γ(Red - Blue)) / (NIR + Red - γ(Red - Blue)) - WorldView 2: (NIR1 - Red - γ(Red - Blue)) / (NIR1 + Red - γ(Red - Blue)) - WorldView 3: (NIR1 - Red - γ(Red - Blue)) / (NIR1 + Red - γ(Red - Blue)) - WorldView 4: (NIR - Red - γ(Red - Panchromatic)) / (NIR + Red - γ(Red - Panchromatic)) - WorldView Legion: (NIR1 - Red - γ(Red - Blue)) / (NIR1 + Red - γ(Red - Blue)) --- #### ARVI2 — Atmospherically Resistant Vegetation Index 2 URL: https://docs.geopera.com/spectral-indices/arvi2 Category: vegetation ARVI2 is an enhanced version of the Atmospherically Resistant Vegetation Index designed to be resistant to atmospheric effects while being more sensitive to a wide range of chlorophyll concentrations. It builds upon the original ARVI concept by Kaufman and Tanré, providing improved vegetation monitoring capabilities under varying atmospheric conditions. Formula: (NIR - (RED - γ * (RED - BLUE))) / (NIR + (RED - γ * (RED - BLUE))) Wavelengths: BLUE (450-520), RED (640-760), NIR (780-1400) Applications: atmospherically resistant vegetation monitoring, chlorophyll content estimation, vegetation health assessment, agricultural monitoring, vegetation fraction estimation, photosynthetic activity monitoring References: Gitelson, A.A., Kaufman, Y.J., and Merzlyak, M.N. (1996) - Use of a green channel in remote sensing of global vegetation from EOS-MODIS; Kaufman, Y.J. and Tanré, D. (1992) - Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS Sensor-specific formulas: - BJ3A: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - BJ3N: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - Dragonette-1: (Band 23 - (Band 16 - γ * (Band 16 - Band 1))) / (Band 23 + (Band 16 - γ * (Band 16 - Band 1))) - Dragonette-2/3: (Band31 - (Band20 - γ * (Band20 - Band4))) / (Band31 + (Band20 - γ * (Band20 - Band4))) - Gaofen-1: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - Gaofen-2: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - GeoEye-1: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - Göktürk-1: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - Jilin-1: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - Jilin-1 GF03D: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - KOMPSAT-3: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - KOMPSAT-3A: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - Landsat 8/9: (B5 - (B4 - γ * (B4 - B1))) / (B5 + (B4 - γ * (B4 - B1))) - NAIP: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - Sentinel-2: (B8 - (B4 - γ * (B4 - B1))) / (B8 + (B4 - γ * (B4 - B1))) - SuperView Neo: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - SuperView-1: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - SuperView-2: (NIR1 - (Red - γ * (Red - Blue))) / (NIR1 + (Red - γ * (Red - Blue))) - TripleSat: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - WorldView 2: (NIR1 - (Red - γ * (Red - Blue))) / (NIR1 + (Red - γ * (Red - Blue))) - WorldView 3: (NIR1 - (Red - γ * (Red - Blue))) / (NIR1 + (Red - γ * (Red - Blue))) - WorldView 4: (NIR - (Red - γ * (Red - Blue))) / (NIR + (Red - γ * (Red - Blue))) - WorldView Legion: (NIR1 - (Red - γ * (Red - Blue))) / (NIR1 + (Red - γ * (Red - Blue))) --- #### BGI — Blue Green Pigment Index URL: https://docs.geopera.com/spectral-indices/bgi Category: vegetation Blue Green Pigment Index (BGI) is a simple ratio index that compares reflectance in the blue (450nm) and green (550nm) regions of the spectrum. It is used to assess plant pigment content, particularly useful for detecting changes in chlorophyll and carotenoid concentrations that affect blue and green light absorption. Formula: 450nm / 550nm Wavelengths: 450 (450), 550 (550) Applications: plant pigment assessment, chlorophyll content estimation, carotenoid detection, vegetation stress monitoring, photosynthetic pigment analysis References: Simple Ratio vegetation indices for pigment assessment Sensor-specific formulas: - BJ3A: Blue / Green - BJ3N: Blue / Green - Dragonette-2/3: Band1 / Band9 - Gaofen-1: Blue / Green - Gaofen-2: Blue / Green - GeoEye-1: Blue / Green - Göktürk-1: Blue / Green - Jilin-1: Blue / Green - Jilin-1 GF03D: Blue / Green - KOMPSAT-3: Blue / Green - KOMPSAT-3A: Blue / Green - Landsat 8/9: B1 / B3 - NAIP: Blue / Green - Sentinel-2: B1 / B3 - SuperView Neo: Blue / Green - SuperView-1: Blue / Green - SuperView-2: Blue / Green - TripleSat: Blue / Green - WorldView 2: Coastal / Green - WorldView 3: Coastal / Green - WorldView 4: Blue / Green - WorldView Legion: Coastal / Green --- #### BNDVI — Blue Normalized Difference Vegetation Index URL: https://docs.geopera.com/spectral-indices/bndvi Category: vegetation A vegetation index that uses blue instead of red bands. BNDVI can be useful in situations where the red band is saturated or when assessing vegetation in water bodies where blue light penetrates better. Formula: (NIR - Blue) / (NIR + Blue) Wavelengths: blue (420-480), nir (780-900) Applications: Vegetation assessment in aquatic environments, LAI estimation, Crop yield prediction, Vegetation mapping with reduced soil effects, Agricultural monitoring References: Yang et al. (2004). Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability.; Hancock & Dougherty (2007). Relationships between blue- and red-based vegetation indices and leaf area and yield of alfalfa.; Wang et al. (2007). New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice. Sensor-specific formulas: - BJ3A: (NIR - Blue) / (NIR + Blue) - BJ3N: (NIR - Blue) / (NIR + Blue) - Dragonette-2/3: (Band29 - Band1) / (Band29 + Band1) - Gaofen-1: (NIR - Blue) / (NIR + Blue) - Gaofen-2: (NIR - Blue) / (NIR + Blue) - GeoEye-1: (NIR - Blue) / (NIR + Blue) - Göktürk-1: (NIR - Blue) / (NIR + Blue) - Jilin-1: (NIR - Blue) / (NIR + Blue) - Jilin-1 GF03D: (NIR - Blue) / (NIR + Blue) - KOMPSAT-3: (NIR - Blue) / (NIR + Blue) - KOMPSAT-3A: (NIR - Blue) / (NIR + Blue) - Landsat 8/9: (B5 - B1) / (B5 + B1) - NAIP: (NIR - Blue) / (NIR + Blue) - Sentinel-2: (B8 - B1) / (B8 + B1) - SuperView Neo: (NIR - Blue) / (NIR + Blue) - SuperView-1: (NIR - Blue) / (NIR + Blue) - SuperView-2: (NIR1 - Blue) / (NIR1 + Blue) - TripleSat: (NIR - Blue) / (NIR + Blue) - WorldView 2: (NIR1 - Blue) / (NIR1 + Blue) - WorldView 3: (NIR1 - Blue) / (NIR1 + Blue) - WorldView 4: (NIR - Blue) / (NIR + Blue) - WorldView Legion: (NIR1 - Blue) / (NIR1 + Blue) --- #### bNIRv — Blue Near-Infrared Reflectance of Vegetation URL: https://docs.geopera.com/spectral-indices/bnirv Category: vegetation Blue Near-Infrared Reflectance of Vegetation - A spectral index for vegetation applications. Formula: ((N - B)/(N + B)) * N Wavelengths: N (850), B (450) Applications: vegetation References: https://doi.org/10.1029/2024JG008240 Sensor-specific formulas: - BJ3A: ((NIR - Blue)/(NIR + Blue)) * NIR - BJ3N: ((NIR - Blue)/(NIR + Blue)) * NIR - Dragonette-2/3: ((Band30 - Band1)/(Band30 + Band1)) * Band30 - Gaofen-1: ((NIR - Blue)/(NIR + Blue)) * NIR - Gaofen-2: ((NIR - Blue)/(NIR + Blue)) * NIR - GeoEye-1: ((NIR - Blue)/(NIR + Blue)) * NIR - Göktürk-1: ((NIR - Blue)/(NIR + Blue)) * NIR - Jilin-1: ((NIR - Blue)/(NIR + Blue)) * NIR - Jilin-1 GF03D: ((NIR - Blue)/(NIR + Blue)) * NIR - KOMPSAT-3: ((NIR - Blue)/(NIR + Blue)) * NIR - KOMPSAT-3A: ((NIR - Blue)/(NIR + Blue)) * NIR - Landsat 8/9: ((B5 - B1)/(B5 + B1)) * B5 - NAIP: ((NIR - Blue)/(NIR + Blue)) * NIR - Sentinel-2: ((B8 - B1)/(B8 + B1)) * B8 - SuperView Neo: ((NIR - Blue)/(NIR + Blue)) * NIR - SuperView-1: ((NIR - Blue)/(NIR + Blue)) * NIR - SuperView-2: ((NIR1 - Blue)/(NIR1 + Blue)) * NIR1 - TripleSat: ((NIR - Blue)/(NIR + Blue)) * NIR - WorldView 2: ((NIR1 - Coastal)/(NIR1 + Coastal)) * NIR1 - WorldView 3: ((NIR1 - Coastal)/(NIR1 + Coastal)) * NIR1 - WorldView 4: ((NIR - Blue)/(NIR + Blue)) * NIR - WorldView Legion: ((NIR1 - Coastal)/(NIR1 + Coastal)) * NIR1 --- #### BRI — Blue Red Pigment Index URL: https://docs.geopera.com/spectral-indices/bri Category: vegetation Blue Red Pigment Index (BRI) is a simple ratio index that compares reflectance in the blue (450nm) and red (690nm) regions of the spectrum. It is designed to assess plant pigment content and is sensitive to changes in chlorophyll and carotenoid concentrations, making it useful for stress detection and pigment analysis. Formula: 450nm / 690nm Wavelengths: 450 (450), 690 (690) Applications: plant pigment assessment, chlorophyll content estimation, carotenoid detection, vegetation stress monitoring, leaf pigment analysis References: Simple Ratio indices for vegetation pigment assessment Sensor-specific formulas: - BJ3A: Blue / Red - BJ3N: Blue / Red - Dragonette-2/3: Band1 / Band19 - Gaofen-1: Blue / Panchromatic - Gaofen-2: Blue / Panchromatic - GeoEye-1: Blue / Red - Göktürk-1: Blue / Panchromatic - Jilin-1: Blue / Red - Jilin-1 GF03D: Blue / Panchromatic - KOMPSAT-3: Blue / Panchromatic - KOMPSAT-3A: Blue / Panchromatic - Sentinel-2: B1 / B5 - SuperView Neo: Blue / Red - SuperView-1: Blue / Red - SuperView-2: Blue / Red_Edge - TripleSat: Blue / Red - WorldView 2: Coastal / Red - WorldView 3: Coastal / Red - WorldView 4: Blue / Red - WorldView Legion: Coastal / Red --- #### BWDRVI — Blue Wide Dynamic Range Vegetation Index URL: https://docs.geopera.com/spectral-indices/bwdrvi Category: vegetation A blue-based variant of WDRVI that uses blue instead of red bands. This index maintains sensitivity at high biomass levels while using the blue spectral region, which can be advantageous in certain applications. Formula: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) Wavelengths: blue (420-480), nir (780-900) Applications: High biomass vegetation monitoring, LAI estimation in dense canopies, Agricultural yield prediction, Vegetation assessment with reduced atmospheric effects, Crop health monitoring References: Hancock & Dougherty (2007). Relationships between blue- and red-based vegetation indices and leaf area and yield of alfalfa. Sensor-specific formulas: - BJ3A: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - BJ3N: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - Dragonette-2/3: (0.1 * Band29 - Band1) / (0.1 * Band29 + Band1) - Gaofen-1: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - Gaofen-2: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - GeoEye-1: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - Göktürk-1: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - Jilin-1: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - Jilin-1 GF03D: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - KOMPSAT-3: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - KOMPSAT-3A: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - Landsat 8/9: (0.1 * B5 - B1) / (0.1 * B5 + B1) - NAIP: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - Sentinel-2: (0.1 * B8 - B1) / (0.1 * B8 + B1) - SuperView Neo: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - SuperView-1: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - SuperView-2: (0.1 * NIR1 - Blue) / (0.1 * NIR1 + Blue) - TripleSat: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - WorldView 2: (0.1 * NIR1 - Blue) / (0.1 * NIR1 + Blue) - WorldView 3: (0.1 * NIR1 - Blue) / (0.1 * NIR1 + Blue) - WorldView 4: (0.1 * NIR - Blue) / (0.1 * NIR + Blue) - WorldView Legion: (0.1 * NIR1 - Blue) / (0.1 * NIR1 + Blue) --- #### CIrededge — Chlorophyll Index Red Edge URL: https://docs.geopera.com/spectral-indices/ci_rededge Category: vegetation A simple chlorophyll index using the ratio of NIR to red edge reflectance minus 1. This index is sensitive to chlorophyll content variations and is useful for LAI estimation. Formula: (NIR / RE1) - 1 Wavelengths: re1 (690-730), nir (780-900) Applications: Chlorophyll content estimation, LAI assessment, Crop health monitoring, Red edge position analysis, Vegetation productivity estimation References: Gitelson et al. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies.; Hunt Jr. et al. (2011). Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index.; Ahamed et al. (2011). A review of remote sensing methods for biomass feedstock production. Sensor-specific formulas: - BJ3A: (NIR / Red) - 1 - BJ3N: (NIR / Red) - 1 - Dragonette-1: (Band 23 / Band 17) - 1 - Dragonette-2/3: (Band29 / Band21) - 1 - Gaofen-1: (NIR / Panchromatic) - 1 - Gaofen-2: (NIR / Panchromatic) - 1 - GeoEye-1: (NIR / Red) - 1 - Göktürk-1: (NIR / Panchromatic) - 1 - Jilin-1: (NIR / Red) - 1 - Jilin-1 GF03D: (NIR / Panchromatic) - 1 - KOMPSAT-3: (NIR / Panchromatic) - 1 - KOMPSAT-3A: (NIR / Panchromatic) - 1 - Sentinel-2: (B8 / B5) - 1 - SuperView Neo: (NIR / Red) - 1 - SuperView-1: (NIR / Red) - 1 - SuperView-2: (NIR1 / Red_Edge) - 1 - TripleSat: (NIR / Red) - 1 - WorldView 2: (NIR1 / Red_Edge) - 1 - WorldView 3: (NIR1 / Red Edge) - 1 - WorldView 4: (NIR / Red) - 1 - WorldView Legion: (NIR1 / Red_Edge) - 1 --- #### CIrededge710 — Chlorophyll Index RedEdge 710 URL: https://docs.geopera.com/spectral-indices/cirededge710 Category: vegetation Chlorophyll Index RedEdge 710 is a spectral index designed to assess chlorophyll content in vegetation, focusing on the red-edge spectral region. It utilizes the red-edge position to provide sensitive measurements of vegetation chlorophyll status. Formula: 750nm / 710nm - 1 Wavelengths: 710 (710), 750 (750) Applications: agriculture, hyperspectral remote sensing - red-edge position, vegetation analysis, vegetation chlorophyll assessment, crop monitoring, vegetation health monitoring References: Wu et al. (2009) - Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices Sensor-specific formulas: - Dragonette-1: Band 20 / Band 17 - 1 - Dragonette-2/3: Band24 / Band21 - 1 - GeoEye-1: NIR / Panchromatic - 1 - Sentinel-2: B6 / B5 - 1 --- #### CRI550 — Carotenoid Reflectance Index 550 URL: https://docs.geopera.com/spectral-indices/cri550 Category: vegetation The Carotenoid Reflectance Index 550 (CRI550) was developed by Gitelson et al. (2002) to assess carotenoid content in plant leaves. It uses reciprocal reflectance at 510nm (sensitive to carotenoids) and 550nm (to remove chlorophyll effects), providing a non-destructive method for carotenoid estimation. Formula: (1 / 510nm) - (1 / 550nm) Wavelengths: 510 (510), 550 (550) Applications: carotenoid content estimation, plant stress detection, photosynthetic efficiency assessment, leaf pigment analysis, plant health monitoring, phenological studies References: Gitelson, A.A., Zur, Y., Chivkunova, O.B., and Merzlyak, M.N. (2002) - Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75(3), 272-281 Sensor-specific formulas: - BJ3A: (1 / Blue) - (1 / Green) - BJ3N: (1 / Blue) - (1 / Green) - Dragonette-1: (1 / Band 2) - (1 / Band 5) - Dragonette-2/3: (1 / Band6) - (1 / Band9) - Gaofen-1: (1 / Blue) - (1 / Green) - Gaofen-2: (1 / Blue) - (1 / Green) - GeoEye-1: (1 / Blue) - (1 / Green) - Göktürk-1: (1 / Blue) - (1 / Green) - Jilin-1: (1 / Blue) - (1 / Green) - Jilin-1 GF03D: (1 / Blue) - (1 / Green) - KOMPSAT-3: (1 / Blue) - (1 / Green) - KOMPSAT-3A: (1 / Blue) - (1 / Green) - Landsat 8/9: (1 / B2) - (1 / B3) - Sentinel-2: (1 / B2) - (1 / B3) - SuperView Neo: (1 / Blue) - (1 / Green) - SuperView-1: (1 / Blue) - (1 / Green) - SuperView-2: (1 / Blue) - (1 / Green) - TripleSat: (1 / Blue) - (1 / Green) - WorldView 2: (1 / Blue) - (1 / Green) - WorldView 3: (1 / Blue) - (1 / Green) - WorldView 4: (1 / Blue) - (1 / Green) - WorldView Legion: (1 / Blue) - (1 / Green) --- #### CRI700 — Carotenoid Reflectance Index 700 URL: https://docs.geopera.com/spectral-indices/cri700 Category: vegetation The Carotenoid Reflectance Index 700 (CRI700) is an alternative formulation to CRI550 that uses the 700nm band instead of 550nm to minimize chlorophyll effects. This index provides better performance in leaves with high chlorophyll content and is particularly useful for mature vegetation. Formula: (1 / 510nm) - (1 / 700nm) Wavelengths: 510 (510), 700 (700) Applications: carotenoid content estimation, high chlorophyll vegetation assessment, plant stress detection, photosynthetic pigment analysis, mature leaf assessment, vegetation health monitoring References: Gitelson, A.A., Zur, Y., Chivkunova, O.B., and Merzlyak, M.N. (2002) - Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75(3), 272-281 Sensor-specific formulas: - BJ3A: (1 / Blue) - (1 / Red) - BJ3N: (1 / Blue) - (1 / Red) - Dragonette-1: (1 / Band 2) - (1 / Band 16) - Dragonette-2/3: (1 / Band6) - (1 / Band20) - Gaofen-1: (1 / Blue) - (1 / Panchromatic) - Gaofen-2: (1 / Blue) - (1 / Panchromatic) - GeoEye-1: (1 / Blue) - (1 / Red) - Göktürk-1: (1 / Blue) - (1 / Panchromatic) - Jilin-1: (1 / Blue) - (1 / Red) - Jilin-1 GF03D: (1 / Blue) - (1 / Panchromatic) - KOMPSAT-3: (1 / Blue) - (1 / Panchromatic) - KOMPSAT-3A: (1 / Blue) - (1 / Panchromatic) - Sentinel-2: (1 / B2) - (1 / B5) - SuperView Neo: (1 / Blue) - (1 / Red) - SuperView-1: (1 / Blue) - (1 / Red) - SuperView-2: (1 / Blue) - (1 / Red_Edge) - TripleSat: (1 / Blue) - (1 / Red) - WorldView 2: (1 / Blue) - (1 / Red_Edge) - WorldView 3: (1 / Blue) - (1 / Red Edge) - WorldView 4: (1 / Blue) - (1 / Red) - WorldView Legion: (1 / Blue) - (1 / Red_Edge) --- #### Ctr1 — Simple Ratio 695/420 Carter1 URL: https://docs.geopera.com/spectral-indices/carter1 Category: vegetation Carter1 (Ctr1) is one of the most effective plant stress indices developed by Gregory A. Carter. It uses the ratio of reflectance at 695nm (red-edge) to 420nm (blue) to detect various types of plant stress. This ratio was found to be significantly greater in stressed compared to non-stressed leaves for all stress agents tested. Formula: 695nm / 420nm Wavelengths: 420 (420), 695 (695) Applications: plant stress detection, early stress warning, water stress assessment, disease monitoring, nutrient deficiency detection, general plant health assessment References: Carter, G.A. (1994) - Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(3), 697-703 Sensor-specific formulas: - WorldView 2: Red_Edge / Coastal - WorldView 3: Red Edge / Coastal - WorldView Legion: Red_Edge / Coastal --- #### Ctr2 — Simple Ratio 695/760 Carter2 URL: https://docs.geopera.com/spectral-indices/carter2 Category: vegetation Carter2 (Ctr2) is a plant stress index that uses the ratio of reflectance at 695nm (red-edge) to 760nm (near-infrared). This index is particularly effective at detecting stress because it combines the stress-sensitive red-edge region with the NIR region where healthy vegetation shows high reflectance. Formula: 695nm / 760nm Wavelengths: 695 (695), 760 (760) Applications: plant stress detection, vegetation health monitoring, water stress assessment, disease detection, chlorophyll content changes, early stress warning systems References: Carter, G.A. (1994) - Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(3), 697-703 Sensor-specific formulas: - BJ3A: Red / NIR - BJ3N: Red / NIR - Dragonette-1: Band 16 / Band 21 - Dragonette-2/3: Band19 / Band25 - Gaofen-1: Panchromatic / NIR - Gaofen-2: Panchromatic / NIR - GeoEye-1: Red / NIR - Göktürk-1: Panchromatic / NIR - Jilin-1: Red / NIR - Jilin-1 GF03D: Panchromatic / NIR - KOMPSAT-3: Panchromatic / NIR - KOMPSAT-3A: Panchromatic / NIR - Sentinel-2: B5 / B6 - SuperView Neo: Red / NIR - SuperView-1: Red / NIR - SuperView-2: Red_Edge / NIR1 - TripleSat: Red / NIR - WorldView 2: Red_Edge / NIR1 - WorldView 3: Red Edge / NIR1 - WorldView 4: Red / NIR - WorldView Legion: Red_Edge / NIR1 --- #### Ctr3 — Carter Stress Index 3 URL: https://docs.geopera.com/spectral-indices/ctr3 Category: vegetation A vegetation stress index that uses the ratio of reflectance at 605nm to 760nm. This index is sensitive to plant stress conditions and can detect early signs of vegetation health decline. Formula: orange / nir Wavelengths: orange (605), nir (760) Applications: Plant stress detection, Early disease detection, Drought stress monitoring, Vegetation health assessment, Forest decline monitoring References: Carter (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress.; le Maire et al. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements.; Main et al. (2011). An investigation into robust spectral indices for leaf chlorophyll estimation. Sensor-specific formulas: - Dragonette-1: Band 8 / Band 21 - Dragonette-2/3: Band12 / Band25 - Gaofen-1: Panchromatic / NIR - Gaofen-2: Panchromatic / NIR - GeoEye-1: Panchromatic / NIR - Göktürk-1: Green / NIR - Jilin-1 GF03D: Panchromatic / NIR - KOMPSAT-3: Green / NIR - KOMPSAT-3A: Green / NIR - WorldView 2: Yellow / NIR1 - WorldView 3: Yellow / NIR1 - WorldView 4: Panchromatic / NIR - WorldView Legion: Yellow / NIR1 --- #### Ctr4 — Carter Stress Index 4 URL: https://docs.geopera.com/spectral-indices/ctr4 Category: vegetation A vegetation stress index using the ratio of red edge (710nm) to NIR (760nm) reflectance. This index is particularly sensitive to changes in chlorophyll content and plant stress. Formula: re1 / nir Wavelengths: re1 (710), nir (760) Applications: Plant stress monitoring, Chlorophyll-related stress detection, Vegetation health assessment, Early warning of plant decline, Precision agriculture References: Carter (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress.; le Maire et al. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements.; Main et al. (2011). An investigation into robust spectral indices for leaf chlorophyll estimation. Sensor-specific formulas: - Dragonette-1: Band 17 / Band 21 - Dragonette-2/3: Band21 / Band25 - Gaofen-1: Panchromatic / NIR - Gaofen-2: Panchromatic / NIR - GeoEye-1: Panchromatic / NIR - Göktürk-1: Panchromatic / NIR - Jilin-1 GF03D: Panchromatic / NIR - KOMPSAT-3: Panchromatic / NIR - KOMPSAT-3A: Panchromatic / NIR - Sentinel-2: B5 / B6 - SuperView-2: Red_Edge / NIR1 - WorldView 2: Red_Edge / NIR1 - WorldView 3: Red Edge / NIR1 - WorldView 4: Panchromatic / NIR - WorldView Legion: Red_Edge / NIR1 --- #### Ctr6 — Simple Ratio 550/420 Carter6 URL: https://docs.geopera.com/spectral-indices/carter6 Category: vegetation Carter6 (Ctr6) is a simple ratio vegetation stress index developed by Gregory A. Carter. It uses the ratio of reflectance at 550nm (green) to 420nm (blue) to detect plant stress. The index is based on the principle that stress factors interfere with photosynthesis and alter the reflectance spectrum, particularly in the blue and green regions. Formula: 550nm / 420nm Wavelengths: 420 (420), 550 (550) Applications: vegetation stress detection, plant health monitoring, water stress assessment, disease detection, environmental stress monitoring, chlorophyll content changes References: Carter, G.A. (1994) - Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(3), 697-703 Sensor-specific formulas: - NAIP: Green / Blue - WorldView 2: Green / Coastal - WorldView 3: Green / Coastal - WorldView Legion: Green / Coastal --- #### DVI — Simple Ratio NIR/RED Difference Vegetation Index URL: https://docs.geopera.com/spectral-indices/dvi Category: vegetation Simple vegetation index calculating the ratio of near-infrared to red reflectance. One of the earliest vegetation indices, useful for biomass estimation and vegetation analysis. Formula: NIR / Red Wavelengths: Red (630-690 nm), NIR (770-900 nm) Applications: Vegetation Analysis, Biomass Estimation, Chlorophyll Measurement, Leaf Area Index Assessment, Vegetation Water Content Analysis References: Jordan (1969) Sensor-specific formulas: - BJ3A: NIR / Red - BJ3N: NIR / Red - Dragonette-1: Band 22 / Band 10 - Dragonette-2/3: Band26 / Band14 - Gaofen-1: NIR / Red - Gaofen-2: NIR / Red - GeoEye-1: NIR / Red - Göktürk-1: NIR / Red - Jilin-1: NIR / Red - Jilin-1 GF03D: NIR / Red - KOMPSAT-3: NIR / Red - KOMPSAT-3A: NIR / Red - Landsat 8/9: B5 / B4 - NAIP: NIR / Red - Sentinel-2: B8 / B4 - SuperView Neo: NIR / Red - SuperView-1: NIR / Red - SuperView-2: NIR1 / Red - TripleSat: NIR / Red - WorldView 2: NIR1 / Red - WorldView 3: NIR1 / Red - WorldView 4: NIR / Red - WorldView Legion: NIR1 / Red --- #### EVI — Enhanced Vegetation Index URL: https://docs.geopera.com/spectral-indices/evi Category: vegetation Improved vegetation index that reduces atmospheric and soil background effects. More sensitive to vegetation changes than NDVI. Formula: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Blue + 1)) Wavelengths: Blue (450-520 nm), Red (630-690 nm), NIR (770-900 nm) Applications: Agriculture, Forest Health Assessment, Phenology Studies, Carbon Cycle Research References: Huete et al. (2002) Sensor-specific formulas: - BJ3A: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Blue + 1)) - BJ3N: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Blue + 1)) - Dragonette-1: 2.5 * ((Band 22 - Band 10) / (Band 22 + 6 * Band 10 - 7.5 * Band 1 + 1)) - Dragonette-2/3: 2.5 * ((Band26 - Band14) / (Band26 + 6 * Band14 - 7.5 * Band2 + 1)) - Gaofen-1: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Panchromatic + 1)) - Gaofen-2: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Panchromatic + 1)) - GeoEye-1: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Panchromatic + 1)) - Göktürk-1: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Panchromatic + 1)) - Jilin-1: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Blue + 1)) - Jilin-1 GF03D: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Panchromatic + 1)) - KOMPSAT-3: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Panchromatic + 1)) - KOMPSAT-3A: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Panchromatic + 1)) - Landsat 8/9: 2.5 * ((B5 - B4) / (B5 + 6 * B4 - 7.5 * B1 + 1)) - NAIP: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Blue + 1)) - Sentinel-2: 2.5 * ((B8 - B4) / (B8 + 6 * B4 - 7.5 * B1 + 1)) - SuperView Neo: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Blue + 1)) - SuperView-1: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Blue + 1)) - SuperView-2: 2.5 * ((NIR1 - Red) / (NIR1 + 6 * Red - 7.5 * Blue + 1)) - TripleSat: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Blue + 1)) - WorldView 2: 2.5 * ((NIR1 - Red) / (NIR1 + 6 * Red - 7.5 * Blue + 1)) - WorldView 3: 2.5 * ((NIR1 - Red) / (NIR1 + 6 * Red - 7.5 * Blue + 1)) - WorldView 4: 2.5 * ((NIR - Red) / (NIR + 6 * Red - 7.5 * Panchromatic + 1)) - WorldView Legion: 2.5 * ((NIR1 - Red) / (NIR1 + 6 * Red - 7.5 * Blue + 1)) --- #### GARI — Green Atmospherically Resistant Vegetation Index URL: https://docs.geopera.com/spectral-indices/gari Category: vegetation The Green Atmospherically Resistant Vegetation Index (GARI) is tailored on the concept of ARVI but uses the green band instead of red. It is expected to be as resistant to atmospheric effects as ARVI but more sensitive to a wide range of chlorophyll-a concentrations. GARI has a wider dynamic range than NDVI and is, on average, at least five times more sensitive to chlorophyll concentration. Formula: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) Wavelengths: BLUE (450-520), GREEN (520-600), RED (640-760), NIR (780-1400) Applications: chlorophyll content estimation, photosynthesis rate monitoring, plant stress detection, atmospherically resistant vegetation monitoring, precision agriculture, vegetation health assessment References: Gitelson, A.A., Kaufman, Y.J., and Merzlyak, M.N. (1996) - Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289-298 Sensor-specific formulas: - BJ3A: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - BJ3N: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - Dragonette-1: (Band 23 - (Band 6 - γ * (Band 1 - Band 16))) / (Band 23 + (Band 6 - γ * (Band 1 - Band 16))) - Dragonette-2/3: (Band31 - (Band10 - γ * (Band4 - Band20))) / (Band31 + (Band10 - γ * (Band4 - Band20))) - Gaofen-1: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - Gaofen-2: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - GeoEye-1: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - Göktürk-1: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - Jilin-1: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - Jilin-1 GF03D: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - KOMPSAT-3: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - KOMPSAT-3A: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - Landsat 8/9: (B5 - (B3 - γ * (B1 - B4))) / (B5 + (B3 - γ * (B1 - B4))) - NAIP: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - Sentinel-2: (B8 - (B3 - γ * (B1 - B4))) / (B8 + (B3 - γ * (B1 - B4))) - SuperView Neo: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - SuperView-1: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - SuperView-2: (NIR1 - (Green - γ * (Blue - Red))) / (NIR1 + (Green - γ * (Blue - Red))) - TripleSat: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - WorldView 2: (NIR1 - (Green - γ * (Blue - Red))) / (NIR1 + (Green - γ * (Blue - Red))) - WorldView 3: (NIR1 - (Green - γ * (Blue - Red))) / (NIR1 + (Green - γ * (Blue - Red))) - WorldView 4: (NIR - (Green - γ * (Blue - Red))) / (NIR + (Green - γ * (Blue - Red))) - WorldView Legion: (NIR1 - (Green - γ * (Blue - Red))) / (NIR1 + (Green - γ * (Blue - Red))) --- #### GDVI — Green Difference Vegetation Index URL: https://docs.geopera.com/spectral-indices/gdvi Category: vegetation Simple vegetation index that calculates the difference between near-infrared and green spectral bands. Useful for assessing vegetation health and density with straightforward band arithmetic. Formula: NIR - Green Wavelengths: Green (510-580 nm), NIR (770-900 nm) Applications: Vegetation Monitoring, Agriculture, Crop Development Monitoring, Forest Health Assessment, Environmental Monitoring References: Tucker et al. (1979) Sensor-specific formulas: - BJ3A: NIR - Green - BJ3N: NIR - Green - Dragonette-1: Band 22 - Band 2 - Dragonette-2/3: Band26 - Band7 - Gaofen-1: NIR - Green - Gaofen-2: NIR - Green - GeoEye-1: NIR - Green - Göktürk-1: NIR - Green - Jilin-1: NIR - Green - Jilin-1 GF03D: NIR - Green - KOMPSAT-3: NIR - Green - KOMPSAT-3A: NIR - Green - Landsat 8/9: B5 - B3 - NAIP: NIR - Green - Sentinel-2: B8 - B3 - SuperView Neo: NIR - Green - SuperView-1: NIR - Green - SuperView-2: NIR1 - Green - TripleSat: NIR - Green - WorldView 2: NIR1 - Green - WorldView 3: NIR1 - Green - WorldView 4: NIR - Green - WorldView Legion: NIR1 - Green --- #### GEMI — Global Environment Monitoring Index URL: https://docs.geopera.com/spectral-indices/gemi Category: vegetation Non-linear vegetation index designed for global vegetation monitoring from satellites. Less sensitive to soil background variations compared to NDVI and provides enhanced discrimination of vegetation states. Formula: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) Wavelengths: Red (630-690 nm), NIR (770-900 nm) Applications: Global Vegetation Monitoring, Environmental Monitoring, Agriculture, Forestry, Land Cover Classification References: Pinty & Verstraete (1992); Bannari et al. (1996) Sensor-specific formulas: - BJ3A: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - BJ3N: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - Dragonette-1: n = (2*(Band 22²-Band 10²) + 1.5*Band 22 + 0.5*Band 10)/(Band 22+Band 10+0.5); GEMI = (n*(1-0.25*n) - Band 10 - 0.125)/(1-Band 10) - Dragonette-2/3: n = (2*(Band26²-Band14²) + 1.5*Band26 + 0.5*Band14)/(Band26+Band14+0.5); GEMI = (n*(1-0.25*n) - Band14 - 0.125)/(1-Band14) - Gaofen-1: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - Gaofen-2: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - GeoEye-1: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - Göktürk-1: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - Jilin-1: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - Jilin-1 GF03D: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - KOMPSAT-3: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - KOMPSAT-3A: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - Landsat 8/9: n = (2*(B5²-B4²) + 1.5*B5 + 0.5*B4)/(B5+B4+0.5); GEMI = (n*(1-0.25*n) - B4 - 0.125)/(1-B4) - NAIP: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - Sentinel-2: n = (2*(B8²-B4²) + 1.5*B8 + 0.5*B4)/(B8+B4+0.5); GEMI = (n*(1-0.25*n) - B4 - 0.125)/(1-B4) - SuperView Neo: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - SuperView-1: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - SuperView-2: n = (2*(NIR1²-Red²) + 1.5*NIR1 + 0.5*Red)/(NIR1+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - TripleSat: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - WorldView 2: n = (2*(NIR1²-Red²) + 1.5*NIR1 + 0.5*Red)/(NIR1+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - WorldView 3: n = (2*(NIR1²-Red²) + 1.5*NIR1 + 0.5*Red)/(NIR1+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - WorldView 4: n = (2*(NIR²-Red²) + 1.5*NIR + 0.5*Red)/(NIR+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) - WorldView Legion: n = (2*(NIR1²-Red²) + 1.5*NIR1 + 0.5*Red)/(NIR1+Red+0.5); GEMI = (n*(1-0.25*n) - Red - 0.125)/(1-Red) --- #### GEMI — Global Environmental Monitoring Index URL: https://docs.geopera.com/spectral-indices/gemi2 Category: vegetation The Global Environmental Monitoring Index (GEMI) is a non-linear vegetation index developed by Pinty and Verstraete (1992) specifically designed to reduce atmospheric effects without requiring detailed atmospheric correction. Unlike NDVI, GEMI maintains information about vegetation cover while being more resistant to atmospheric perturbations. Formula: eta * (1 - 0.25 * eta) - ((RED - 0.125) / (1 - RED)) where eta = (2 * (NIR^2 - RED^2) + 1.5 * NIR + 0.5 * RED) / (NIR + RED + 0.5) Wavelengths: RED (640-760), NIR (780-1400) Applications: global vegetation monitoring, atmospheric effect mitigation, vegetation cover assessment, biomass estimation, environmental monitoring, satellite-based vegetation analysis References: Pinty, B. and Verstraete, M.M. (1992) - GEMI: A non-linear index to monitor global vegetation from satellites. Vegetatio, 101, 15-20 Sensor-specific formulas: - BJ3A: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - BJ3N: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - Dragonette-1: eta * (1 - 0.25 * eta) - ((Band 16 - 0.125) / (1 - Band 16)) where eta = (2 * (Band 23^2 - Band 16^2) + 1.5 * Band 23 + 0.5 * Band 16) / (Band 23 + Band 16 + 0.5) - Dragonette-2/3: eta * (1 - 0.25 * eta) - ((Band20 - 0.125) / (1 - Band20)) where eta = (2 * (Band31^2 - Band20^2) + 1.5 * Band31 + 0.5 * Band20) / (Band31 + Band20 + 0.5) - Gaofen-1: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - Gaofen-2: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - GeoEye-1: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - Göktürk-1: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - Jilin-1: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - Jilin-1 GF03D: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - KOMPSAT-3: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - KOMPSAT-3A: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - Landsat 8/9: eta * (1 - 0.25 * eta) - ((B4 - 0.125) / (1 - B4)) where eta = (2 * (B5^2 - B4^2) + 1.5 * B5 + 0.5 * B4) / (B5 + B4 + 0.5) - NAIP: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - Sentinel-2: eta * (1 - 0.25 * eta) - ((B4 - 0.125) / (1 - B4)) where eta = (2 * (B8^2 - B4^2) + 1.5 * B8 + 0.5 * B4) / (B8 + B4 + 0.5) - SuperView Neo: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - SuperView-1: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - SuperView-2: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR1^2 - Red^2) + 1.5 * NIR1 + 0.5 * Red) / (NIR1 + Red + 0.5) - TripleSat: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - WorldView 2: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR1^2 - Red^2) + 1.5 * NIR1 + 0.5 * Red) / (NIR1 + Red + 0.5) - WorldView 3: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR1^2 - Red^2) + 1.5 * NIR1 + 0.5 * Red) / (NIR1 + Red + 0.5) - WorldView 4: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR^2 - Red^2) + 1.5 * NIR + 0.5 * Red) / (NIR + Red + 0.5) - WorldView Legion: eta * (1 - 0.25 * eta) - ((Red - 0.125) / (1 - Red)) where eta = (2 * (NIR1^2 - Red^2) + 1.5 * NIR1 + 0.5 * Red) / (NIR1 + Red + 0.5) --- #### GI — Greenness Index URL: https://docs.geopera.com/spectral-indices/gi Category: vegetation Simple ratio index measuring vegetation greenness and chlorophyll content using specific wavelengths optimized for vegetation assessment. Useful for monitoring plant health and biomass. Formula: Green / Red Wavelengths: Applications: Vegetation Analysis, Chlorophyll Assessment, Wheat Yield Forecasting, Forest Canopy Analysis, Biomass Estimation References: Zarco-Tejada & Berjon (2005) --- #### GNDVI — Green Normalized Difference Vegetation Index URL: https://docs.geopera.com/spectral-indices/gndvi Category: vegetation Vegetation index that uses green wavelengths instead of red to assess vegetation characteristics. More sensitive to chlorophyll content and can be useful for detecting stress in dense vegetation canopies. Formula: (NIR - Green) / (NIR + Green) Wavelengths: Green (540-570 nm), NIR (770-900 nm) Applications: Vegetation Analysis, Chlorophyll Content Estimation, Biomass Assessment, Agriculture, Forest Health Monitoring References: Gitelson et al. (1996) Sensor-specific formulas: - BJ3A: (NIR - Green) / (NIR + Green) - BJ3N: (NIR - Green) / (NIR + Green) - Dragonette-1: (Band 22 - Band 5) / (Band 22 + Band 5) - Dragonette-2/3: (Band26 - Band9) / (Band26 + Band9) - Gaofen-1: (NIR - Green) / (NIR + Green) - Gaofen-2: (NIR - Green) / (NIR + Green) - GeoEye-1: (NIR - Green) / (NIR + Green) - Göktürk-1: (NIR - Green) / (NIR + Green) - Jilin-1: (NIR - Green) / (NIR + Green) - Jilin-1 GF03D: (NIR - Green) / (NIR + Green) - KOMPSAT-3: (NIR - Green) / (NIR + Green) - KOMPSAT-3A: (NIR - Green) / (NIR + Green) - Landsat 8/9: (B5 - B3) / (B5 + B3) - NAIP: (NIR - Green) / (NIR + Green) - Sentinel-2: (B8 - B3) / (B8 + B3) - SuperView Neo: (NIR - Green) / (NIR + Green) - SuperView-1: (NIR - Green) / (NIR + Green) - SuperView-2: (NIR1 - Green) / (NIR1 + Green) - TripleSat: (NIR - Green) / (NIR + Green) - WorldView 2: (NIR1 - Green) / (NIR1 + Green) - WorldView 3: (NIR1 - Green) / (NIR1 + Green) - WorldView 4: (NIR - Green) / (NIR + Green) - WorldView Legion: (NIR1 - Green) / (NIR1 + Green) --- #### GOSAVI — Green Optimized Soil Adjusted Vegetation Index URL: https://docs.geopera.com/spectral-indices/gosavi Category: vegetation Soil-adjusted vegetation index that uses green wavelengths and incorporates a soil adjustment factor to minimize soil background influences. Optimized for vegetation monitoring in areas with varying soil conditions. Formula: (NIR - Green) / (NIR + Green + 0.16) Wavelengths: Green (510-580 nm), NIR (770-900 nm) Applications: Soil Analysis, Vegetation Monitoring, Agriculture, Sparse Vegetation Assessment, Soil-Vegetation Studies References: Gilabert et al. (2002) Sensor-specific formulas: - BJ3A: (NIR - Green) / (NIR + Green + 0.16) - BJ3N: (NIR - Green) / (NIR + Green + 0.16) - Dragonette-1: (Band 22 - Band 2) / (Band 22 + Band 2 + 0.16) - Dragonette-2/3: (Band26 - Band7) / (Band26 + Band7 + 0.16) - Gaofen-1: (NIR - Green) / (NIR + Green + 0.16) - Gaofen-2: (NIR - Green) / (NIR + Green + 0.16) - GeoEye-1: (NIR - Green) / (NIR + Green + 0.16) - Göktürk-1: (NIR - Green) / (NIR + Green + 0.16) - Jilin-1: (NIR - Green) / (NIR + Green + 0.16) - Jilin-1 GF03D: (NIR - Green) / (NIR + Green + 0.16) - KOMPSAT-3: (NIR - Green) / (NIR + Green + 0.16) - KOMPSAT-3A: (NIR - Green) / (NIR + Green + 0.16) - Landsat 8/9: (B5 - B3) / (B5 + B3 + 0.16) - NAIP: (NIR - Green) / (NIR + Green + 0.16) - Sentinel-2: (B8 - B3) / (B8 + B3 + 0.16) - SuperView Neo: (NIR - Green) / (NIR + Green + 0.16) - SuperView-1: (NIR - Green) / (NIR + Green + 0.16) - SuperView-2: (NIR1 - Green) / (NIR1 + Green + 0.16) - TripleSat: (NIR - Green) / (NIR + Green + 0.16) - WorldView 2: (NIR1 - Green) / (NIR1 + Green + 0.16) - WorldView 3: (NIR1 - Green) / (NIR1 + Green + 0.16) - WorldView 4: (NIR - Green) / (NIR + Green + 0.16) - WorldView Legion: (NIR1 - Green) / (NIR1 + Green + 0.16) --- #### GRVI — Green Ratio Vegetation Index URL: https://docs.geopera.com/spectral-indices/grvi Category: vegetation Simple vegetation index that calculates the ratio of near-infrared to green band reflectance. Useful for vegetation fraction estimation and assessing plant health using green wavelengths. Formula: NIR / Green Wavelengths: Green (510-580 nm), NIR (770-900 nm) Applications: Vegetation Analysis, Vegetation Fraction Estimation, Agriculture, Plant Health Assessment, Environmental Monitoring References: Gitelson et al. (2002) Sensor-specific formulas: - BJ3A: NIR / Green - BJ3N: NIR / Green - Dragonette-1: Band 22 / Band 2 - Dragonette-2/3: Band26 / Band7 - Gaofen-1: NIR / Green - Gaofen-2: NIR / Green - GeoEye-1: NIR / Green - Göktürk-1: NIR / Green - Jilin-1: NIR / Green - Jilin-1 GF03D: NIR / Green - KOMPSAT-3: NIR / Green - KOMPSAT-3A: NIR / Green - Landsat 8/9: B5 / B3 - NAIP: NIR / Green - Sentinel-2: B8 / B3 - SuperView Neo: NIR / Green - SuperView-1: NIR / Green - SuperView-2: NIR1 / Green - TripleSat: NIR / Green - WorldView 2: NIR1 / Green - WorldView 3: NIR1 / Green - WorldView 4: NIR / Green - WorldView Legion: NIR1 / Green --- #### GSAVI — Green Soil Adjusted Vegetation Index URL: https://docs.geopera.com/spectral-indices/gsavi Category: vegetation Soil-adjusted vegetation index using green wavelengths to minimize soil background interference. Incorporates a soil adjustment factor (L=0.5) to improve vegetation signal extraction. Formula: (NIR - Green) / (NIR + Green + L * (1 + L)) Wavelengths: Green (510-580 nm), NIR (770-900 nm) Applications: Soil Analysis, Vegetation Monitoring, Agriculture, Sparse Vegetation Assessment, Soil Background Correction References: Gilabert et al. (2002) Sensor-specific formulas: - BJ3A: (NIR - Green) / (NIR + Green + L * (1 + L)) - BJ3N: (NIR - Green) / (NIR + Green + L * (1 + L)) - Dragonette-1: (Band 22 - Band 2) / (Band 22 + Band 2 + L * (1 + L)) - Dragonette-2/3: (Band26 - Band7) / (Band26 + Band7 + L * (1 + L)) - Gaofen-1: (NIR - Green) / (NIR + Green + L * (1 + L)) - Gaofen-2: (NIR - Green) / (NIR + Green + L * (1 + L)) - GeoEye-1: (NIR - Green) / (NIR + Green + L * (1 + L)) - Göktürk-1: (NIR - Green) / (NIR + Green + L * (1 + L)) - Jilin-1: (NIR - Green) / (NIR + Green + L * (1 + L)) - Jilin-1 GF03D: (NIR - Green) / (NIR + Green + L * (1 + L)) - KOMPSAT-3: (NIR - Green) / (NIR + Green + L * (1 + L)) - KOMPSAT-3A: (NIR - Green) / (NIR + Green + L * (1 + L)) - Landsat 8/9: (B5 - B3) / (B5 + B3 + L * (1 + L)) - NAIP: (NIR - Green) / (NIR + Green + L * (1 + L)) - Sentinel-2: (B8 - B3) / (B8 + B3 + L * (1 + L)) - SuperView Neo: (NIR - Green) / (NIR + Green + L * (1 + L)) - SuperView-1: (NIR - Green) / (NIR + Green + L * (1 + L)) - SuperView-2: (NIR1 - Green) / (NIR1 + Green + L * (1 + L)) - TripleSat: (NIR - Green) / (NIR + Green + L * (1 + L)) - WorldView 2: (NIR1 - Green) / (NIR1 + Green + L * (1 + L)) - WorldView 3: (NIR1 - Green) / (NIR1 + Green + L * (1 + L)) - WorldView 4: (NIR - Green) / (NIR + Green + L * (1 + L)) - WorldView Legion: (NIR1 - Green) / (NIR1 + Green + L * (1 + L)) --- #### GVI — Tasselled Cap - vegetation URL: https://docs.geopera.com/spectral-indices/gvi_tc Category: vegetation The vegetation component of the Tasselled Cap transformation, which measures the amount of green vegetation present. High values indicate dense, healthy vegetation. Formula: -0.2848 * Blue - 0.2435 * Green - 0.5436 * Red + 0.7243 * NIR + 0.0840 * SWIR1 - 0.1800 * SWIR2 Wavelengths: blue (450-520), green (520-600), red (630-690), nir (760-900), swir1 (1550-1750), swir2 (2080-2350) Applications: Vegetation density assessment, Forest monitoring, Agricultural crop analysis, Land cover classification References: Bannari et al. (1995). A review of vegetation indices.; Crist & Cicone (1984). A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap. Sensor-specific formulas: - Landsat 8/9: -0.2848 * B1 - 0.2435 * B3 - 0.5436 * B4 + 0.7243 * B5 + 0.0840 * B6 - 0.1800 * B7 - Sentinel-2: -0.2848 * B1 - 0.2435 * B3 - 0.5436 * B4 + 0.7243 * B8 + 0.0840 * B11 - 0.1800 * B12 - WorldView 3: -0.2848 * Blue - 0.2435 * Green - 0.5436 * Red + 0.7243 * NIR1 + 0.0840 * SWIR2 - 0.1800 * SWIR6 --- #### IPVI — Infrared Percentage Vegetation Index URL: https://docs.geopera.com/spectral-indices/ipvi Category: vegetation Enhanced vegetation index that combines simple ratio and NDVI approaches. Provides improved sensitivity to vegetation changes and reduced soil background effects. Formula: NIR / (NIR + Red) * 2 * (NDVI + 1) Wavelengths: NIR (770-900 nm), Red (630-690 nm) Applications: Vegetation Monitoring, Agricultural Assessment, Forest Health Analysis, Biomass Estimation References: Crippen (1990) Sensor-specific formulas: - BJ3A: NIR / (NIR + Red) * 2 * (NDVI + 1) - BJ3N: NIR / (NIR + Red) * 2 * (NDVI + 1) - Dragonette-1: Band 22 / (Band 22 + Band 10) * 2 * (NDVI + 1) - Dragonette-2/3: Band26 / (Band26 + Band14) * 2 * (NDVI + 1) - Gaofen-1: NIR / (NIR + Red) * 2 * (NDVI + 1) - Gaofen-2: NIR / (NIR + Red) * 2 * (NDVI + 1) - GeoEye-1: NIR / (NIR + Red) * 2 * (NDVI + 1) - Göktürk-1: NIR / (NIR + Red) * 2 * (NDVI + 1) - Jilin-1: NIR / (NIR + Red) * 2 * (NDVI + 1) - Jilin-1 GF03D: NIR / (NIR + Red) * 2 * (NDVI + 1) - KOMPSAT-3: NIR / (NIR + Red) * 2 * (NDVI + 1) - KOMPSAT-3A: NIR / (NIR + Red) * 2 * (NDVI + 1) - Landsat 8/9: B5 / (B5 + B4) * 2 * (NDVI + 1) - NAIP: NIR / (NIR + Red) * 2 * (NDVI + 1) - Sentinel-2: B8 / (B8 + B4) * 2 * (NDVI + 1) - SuperView Neo: NIR / (NIR + Red) * 2 * (NDVI + 1) - SuperView-1: NIR / (NIR + Red) * 2 * (NDVI + 1) - SuperView-2: NIR1 / (NIR1 + Red) * 2 * (NDVI + 1) - TripleSat: NIR / (NIR + Red) * 2 * (NDVI + 1) - WorldView 2: NIR1 / (NIR1 + Red) * 2 * (NDVI + 1) - WorldView 3: NIR1 / (NIR1 + Red) * 2 * (NDVI + 1) - WorldView 4: NIR / (NIR + Red) * 2 * (NDVI + 1) - WorldView Legion: NIR1 / (NIR1 + Red) * 2 * (NDVI + 1) --- #### LCI — Leaf Chlorophyll Index URL: https://docs.geopera.com/spectral-indices/lci Category: vegetation An index specifically designed to estimate leaf chlorophyll content using red edge bands. LCI is sensitive to chlorophyll variations while being less affected by leaf structure and canopy architecture. Formula: (nir - re1) / (nir + red) Wavelengths: red (680), re1 (710), nir (850) Applications: Chlorophyll content estimation, Crop nutrient status assessment, Forest health monitoring, Precision agriculture, Vegetation stress detection References: Datt (1999). Remote Sensing of Water Content in Eucalyptus Leaves.; Pu et al. (2008). Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index. Sensor-specific formulas: - Dragonette-2/3: (Band30 - Band21) / (Band30 + Band18) - Gaofen-1: (NIR - Panchromatic) / (NIR + Red) - Gaofen-2: (NIR - Panchromatic) / (NIR + Red) - GeoEye-1: (NIR - Panchromatic) / (NIR + Red) - Göktürk-1: (NIR - Panchromatic) / (NIR + Red) - Jilin-1 GF03D: (NIR - Panchromatic) / (NIR + Red) - KOMPSAT-3: (NIR - Panchromatic) / (NIR + Red) - KOMPSAT-3A: (NIR - Panchromatic) / (NIR + Red) - Sentinel-2: (B8 - B5) / (B8 + B4) - SuperView-2: (NIR1 - Red_Edge) / (NIR1 + Red) - WorldView 2: (NIR1 - Red_Edge) / (NIR1 + Red) - WorldView 3: (NIR1 - Red Edge) / (NIR1 + Red) - WorldView 4: (NIR - Panchromatic) / (NIR + Red) - WorldView Legion: (NIR1 - Red_Edge) / (NIR1 + Red) --- #### mARI — Modified Anthocyanin Reflectance Index URL: https://docs.geopera.com/spectral-indices/ari2 Category: vegetation The Modified Anthocyanin Reflectance Index (mARI or ARI2) is an enhanced version of ARI that corrects for leaf density and thickness by incorporating a near-infrared band. This modification improves the accuracy of anthocyanin estimation by accounting for leaf scattering properties. Formula: ((1 / 550nm) - (1 / 700nm)) * NIR Wavelengths: 550 (550), 700 (700), NIR (760-800) Applications: anthocyanin content estimation, leaf density correction, plant stress detection, senescence monitoring, fruit ripeness assessment, improved physiological status monitoring References: Gitelson, A.A., Keydan, G.P., and Merzlyak, M.N. (2006) - Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves Sensor-specific formulas: - BJ3A: ((1 / Green) - (1 / Red)) * NIR - BJ3N: ((1 / Green) - (1 / Red)) * NIR - Dragonette-1: ((1 / Band 5) - (1 / Band 16)) * Band 22 - Dragonette-2/3: ((1 / Band9) - (1 / Band20)) * Band26 - Gaofen-1: ((1 / Green) - (1 / Panchromatic)) * NIR - Gaofen-2: ((1 / Green) - (1 / Panchromatic)) * NIR - GeoEye-1: ((1 / Green) - (1 / Red)) * NIR - Göktürk-1: ((1 / Green) - (1 / Panchromatic)) * NIR - Jilin-1: ((1 / Green) - (1 / Red)) * NIR - Jilin-1 GF03D: ((1 / Green) - (1 / Panchromatic)) * NIR - KOMPSAT-3: ((1 / Green) - (1 / Panchromatic)) * NIR - KOMPSAT-3A: ((1 / Green) - (1 / Panchromatic)) * NIR - Sentinel-2: ((1 / B3) - (1 / B5)) * B7 - SuperView Neo: ((1 / Green) - (1 / Red)) * NIR - SuperView-1: ((1 / Green) - (1 / Red)) * NIR - SuperView-2: ((1 / Green) - (1 / Red_Edge)) * NIR1 - TripleSat: ((1 / Green) - (1 / Red)) * NIR - WorldView 2: ((1 / Green) - (1 / Red_Edge)) * NIR1 - WorldView 3: ((1 / Green) - (1 / Red Edge)) * NIR1 - WorldView 4: ((1 / Green) - (1 / Red)) * NIR - WorldView Legion: ((1 / Green) - (1 / Red_Edge)) * NIR1 --- #### MCARI — Modified Chlorophyll Absorption in Reflectance Index URL: https://docs.geopera.com/spectral-indices/mcari Category: vegetation Vegetation index designed to estimate chlorophyll content with reduced sensitivity to non-photosynthetic vegetation and soil background effects. Formula: ((700nm - 670nm) - 0.2 * (700nm - 550nm)) * (700nm / 670nm) Wavelengths: Green (550 nm), Red (670 nm), RedEdge (700 nm) Applications: Vegetation Analysis, Chlorophyll Assessment, Crop Health Monitoring, Precision Agriculture, Forest Health Analysis References: Daughtry et al. (2000) Sensor-specific formulas: - Dragonette-1: ((Band 16 - Band 13) - 0.2 * (Band 16 - Band 5)) * (Band 16 / Band 13) - Dragonette-2/3: ((Band20 - Band17) - 0.2 * (Band20 - Band9)) * (Band20 / Band17) - Sentinel-2: ((B5 - B4) - 0.2 * (B5 - B3)) * (B5 / B4) --- #### MCARI/MTVI2 — MCARI/MTVI2 URL: https://docs.geopera.com/spectral-indices/mcari_mtvi2 Category: vegetation MCARI/MTVI2 is a ratio index that combines the Modified Chlorophyll Absorption Ratio Index (MCARI) with the Modified Triangular Vegetation Index 2 (MTVI2). This combination provides improved sensitivity to leaf chlorophyll content while reducing the influence of leaf area index variations, making it particularly useful for agricultural applications. Formula: ((700nm - 670nm) - 0.2 * (700nm - 550nm)) * (700nm / 670nm) / (1.5 * (1.2 * (800nm - 550nm) - 2.5 * (670nm - 550nm)) / sqrt((2 * 800nm + 1)^2 - (6 * 800nm - 5 * sqrt(670nm)) - 0.5)) Wavelengths: 550 (550), 670 (670), 700 (700), 800 (800) Applications: agriculture, vegetation chlorophyll assessment, crop nitrogen status prediction, leaf chlorophyll content estimation, precision agriculture, crop health monitoring References: Eitel et al. (2007) - Using in-situ measurements to evaluate the new RapidEye satellite series for prediction of wheat nitrogen status; Hunt Jr. et al. (2011) - Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index Sensor-specific formulas: - Dragonette-1: ((Band 16 - Band 13) - 0.2 * (Band 16 - Band 5)) * (Band 16 / Band 13) / (1.5 * (1.2 * (Band 23 - Band 5) - 2.5 * (Band 13 - Band 5)) / sqrt((2 * Band 23 + 1)^2 - (6 * Band 23 - 5 * sqrt(Band 13)) - 0.5)) - Dragonette-2/3: ((Band20 - Band17) - 0.2 * (Band20 - Band9)) * (Band20 / Band17) / (1.5 * (1.2 * (Band27 - Band9) - 2.5 * (Band17 - Band9)) / sqrt((2 * Band27 + 1)^2 - (6 * Band27 - 5 * sqrt(Band17)) - 0.5)) - Sentinel-2: ((B5 - B4) - 0.2 * (B5 - B3)) * (B5 / B4) / (1.5 * (1.2 * (B7 - B3) - 2.5 * (B4 - B3)) / sqrt((2 * B7 + 1)^2 - (6 * B7 - 5 * sqrt(B4)) - 0.5)) - SuperView-2: ((Red_Edge - Red) - 0.2 * (Red_Edge - Green)) * (Red_Edge / Red) / (1.5 * (1.2 * (NIR1 - Green) - 2.5 * (Red - Green)) / sqrt((2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * sqrt(Red)) - 0.5)) - WorldView 2: ((Red_Edge - Red) - 0.2 * (Red_Edge - Green)) * (Red_Edge / Red) / (1.5 * (1.2 * (NIR1 - Green) - 2.5 * (Red - Green)) / sqrt((2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * sqrt(Red)) - 0.5)) - WorldView 3: ((Red Edge - Red) - 0.2 * (Red Edge - Green)) * (Red Edge / Red) / (1.5 * (1.2 * (NIR1 - Green) - 2.5 * (Red - Green)) / sqrt((2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * sqrt(Red)) - 0.5)) - WorldView Legion: ((Red_Edge - Red) - 0.2 * (Red_Edge - Green)) * (Red_Edge / Red) / (1.5 * (1.2 * (NIR1 - Green) - 2.5 * (Red - Green)) / sqrt((2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * sqrt(Red)) - 0.5)) --- #### MCARI/OSAVI — MCARI/OSAVI URL: https://docs.geopera.com/spectral-indices/mcari_osavi Category: vegetation MCARI/OSAVI combines the Modified Chlorophyll Absorption Ratio Index (MCARI) with the Optimized Soil-Adjusted Vegetation Index (OSAVI). This ratio index is designed to estimate leaf chlorophyll content while minimizing the confounding effects of leaf area index and soil background reflectance. Formula: ((700nm - 670nm) - 0.2 * (700nm - 550nm) * (700nm / 670nm)) / ((1 + 0.16) * (800nm - 670nm) / (800nm + 670nm + 0.16)) Wavelengths: 550 (550), 670 (670), 700 (700), 800 (800) Applications: leaf chlorophyll estimation, vegetation health monitoring, precision agriculture, crop monitoring, vegetation biophysical parameter estimation, stress detection in vegetation References: Main et al. (2011) - Investigation into spectral indices for leaf chlorophyll estimation; Rondeaux et al. (1996) - Optimization of soil-adjusted vegetation indices; Wu et al. (2008) - Estimating chlorophyll content from hyperspectral vegetation indices; Zarco-Tejada et al. (2007) - Remote sensing of vegetation biophysical parameters Sensor-specific formulas: - Dragonette-1: ((Band 16 - Band 13) - 0.2 * (Band 16 - Band 5) * (Band 16 / Band 13)) / ((1 + 0.16) * (Band 23 - Band 13) / (Band 23 + Band 13 + 0.16)) - Dragonette-2/3: ((Band20 - Band17) - 0.2 * (Band20 - Band9) * (Band20 / Band17)) / ((1 + 0.16) * (Band27 - Band17) / (Band27 + Band17 + 0.16)) - Sentinel-2: ((B5 - B4) - 0.2 * (B5 - B3) * (B5 / B4)) / ((1 + 0.16) * (B7 - B4) / (B7 + B4 + 0.16)) - SuperView-2: ((Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge / Red)) / ((1 + 0.16) * (NIR1 - Red) / (NIR1 + Red + 0.16)) - WorldView 2: ((Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge / Red)) / ((1 + 0.16) * (NIR1 - Red) / (NIR1 + Red + 0.16)) - WorldView 3: ((Red Edge - Red) - 0.2 * (Red Edge - Green) * (Red Edge / Red)) / ((1 + 0.16) * (NIR1 - Red) / (NIR1 + Red + 0.16)) - WorldView Legion: ((Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge / Red)) / ((1 + 0.16) * (NIR1 - Red) / (NIR1 + Red + 0.16)) --- #### MCARI/OSAVI750 — MCARI/OSAVI750 URL: https://docs.geopera.com/spectral-indices/mcari_osavi750 Category: vegetation MCARI/OSAVI750 is a vegetation index that combines the Modified Chlorophyll Absorption Ratio Index with the Optimized Soil-Adjusted Vegetation Index using red-edge bands. It is specifically designed to estimate chlorophyll content using the 750nm band instead of traditional NIR bands. Formula: ((750nm - 705nm) - 0.2 * (750nm - 550nm) * (750nm / 705nm)) / ((1 + 0.16) * (750nm - 705nm) / (750nm + 705nm + 0.16)) Wavelengths: 550 (550), 705 (705), 750 (750) Applications: chlorophyll content estimation, vegetation health assessment, red-edge analysis, precision agriculture, crop monitoring, vegetation stress detection References: Wu et al. (2008) - Estimating chlorophyll content from hyperspectral vegetation indices Sensor-specific formulas: - Dragonette-1: ((Band 20 - Band 16) - 0.2 * (Band 20 - Band 5) * (Band 20 / Band 16)) / ((1 + 0.16) * (Band 20 - Band 16) / (Band 20 + Band 16 + 0.16)) - Dragonette-2/3: ((Band24 - Band20) - 0.2 * (Band24 - Band9) * (Band24 / Band20)) / ((1 + 0.16) * (Band24 - Band20) / (Band24 + Band20 + 0.16)) - GeoEye-1: ((NIR - Red) - 0.2 * (NIR - Green) * (NIR / Red)) / ((1 + 0.16) * (NIR - Red) / (NIR + Red + 0.16)) - Sentinel-2: ((B6 - B5) - 0.2 * (B6 - B3) * (B6 / B5)) / ((1 + 0.16) * (B6 - B5) / (B6 + B5 + 0.16)) --- #### MCARI1 — Modified Chlorophyll Absorption in Reflectance Index 1 URL: https://docs.geopera.com/spectral-indices/mcari1 Category: vegetation Enhanced vegetation chlorophyll index with improved sensitivity and reduced soil background effects. Modified version of MCARI using standard satellite bands. Formula: 1.2 * (2.5 * (800nm - 670nm) - 1.3 * (800nm - 550nm)) Wavelengths: Green (550 nm), Red (670 nm), NIR (800 nm) Applications: Vegetation Analysis, Chlorophyll Assessment, Agricultural Monitoring, Forest Health Analysis, Biomass Estimation References: Haboudane et al. (2004) Sensor-specific formulas: - BJ3A: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - BJ3N: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - Dragonette-1: 1.2 * (2.5 * (Band 23 - Band 13) - 1.3 * (Band 23 - Band 5)) - Dragonette-2/3: 1.2 * (2.5 * (Band27 - Band17) - 1.3 * (Band27 - Band9)) - Gaofen-1: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - Gaofen-2: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - GeoEye-1: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - Göktürk-1: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - Jilin-1: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - Jilin-1 GF03D: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - KOMPSAT-3: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - KOMPSAT-3A: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - Sentinel-2: 1.2 * (2.5 * (B7 - B4) - 1.3 * (B7 - B3)) - SuperView Neo: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - SuperView-1: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - SuperView-2: 1.2 * (2.5 * (NIR1 - Red) - 1.3 * (NIR1 - Green)) - TripleSat: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - WorldView 2: 1.2 * (2.5 * (NIR1 - Red) - 1.3 * (NIR1 - Green)) - WorldView 3: 1.2 * (2.5 * (NIR1 - Red) - 1.3 * (NIR1 - Green)) - WorldView 4: 1.2 * (2.5 * (NIR - Red) - 1.3 * (NIR - Green)) - WorldView Legion: 1.2 * (2.5 * (NIR1 - Red) - 1.3 * (NIR1 - Green)) --- #### MCARI2 — Modified Chlorophyll Absorption in Reflectance Index 2 URL: https://docs.geopera.com/spectral-indices/mcari2 Category: vegetation Advanced vegetation chlorophyll index with enhanced sensitivity and reduced soil background effects. Improved version of MCARI for better leaf area index prediction. Formula: (1.5 * 2.5 * (800nm - 670nm) - 1.3 * (800nm - 550nm) * (2 * 800nm + 1)^2 - (6 * 800nm - 5 * 670nm) - 0.5) Wavelengths: Green (550 nm), Red (670 nm), NIR (800 nm) Applications: Vegetation Analysis, Chlorophyll Estimation, Leaf Area Index Prediction, Crop Health Monitoring, Precision Agriculture References: Haboudane et al. (2004); Main et al. (2011) Sensor-specific formulas: - BJ3A: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - BJ3N: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Dragonette-1: (1.5 * 2.5 * (Band 23 - Band 13) - 1.3 * (Band 23 - Band 5) * (2 * Band 23 + 1)^2 - (6 * Band 23 - 5 * Band 13) - 0.5) - Dragonette-2/3: (1.5 * 2.5 * (Band27 - Band17) - 1.3 * (Band27 - Band9) * (2 * Band27 + 1)^2 - (6 * Band27 - 5 * Band17) - 0.5) - Gaofen-1: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Gaofen-2: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - GeoEye-1: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Göktürk-1: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Jilin-1: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Jilin-1 GF03D: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - KOMPSAT-3: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - KOMPSAT-3A: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Sentinel-2: (1.5 * 2.5 * (B7 - B4) - 1.3 * (B7 - B3) * (2 * B7 + 1)^2 - (6 * B7 - 5 * B4) - 0.5) - SuperView Neo: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - SuperView-1: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - SuperView-2: (1.5 * 2.5 * (NIR1 - Red) - 1.3 * (NIR1 - Green) * (2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * Red) - 0.5) - TripleSat: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - WorldView 2: (1.5 * 2.5 * (NIR1 - Red) - 1.3 * (NIR1 - Green) * (2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * Red) - 0.5) - WorldView 3: (1.5 * 2.5 * (NIR1 - Red) - 1.3 * (NIR1 - Green) * (2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * Red) - 0.5) - WorldView 4: (1.5 * 2.5 * (NIR - Red) - 1.3 * (NIR - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - WorldView Legion: (1.5 * 2.5 * (NIR1 - Red) - 1.3 * (NIR1 - Green) * (2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * Red) - 0.5) --- #### MCARI710 — Modified Chlorophyll Absorption Ratio Index 710 URL: https://docs.geopera.com/spectral-indices/mcari710 Category: vegetation Modified Chlorophyll Absorption Ratio Index 710 is designed to measure chlorophyll content in vegetation. It provides a method to estimate chlorophyll content by comparing reflectance at different wavelengths, particularly in the red and near-infrared regions of the spectrum. Formula: ((750nm - 710nm) - 0.2 * (750nm - 550nm)) * (750nm / 710nm) Wavelengths: 550 (550), 710 (710), 750 (750) Applications: vegetation analysis, vegetation chlorophyll assessment, chlorophyll content estimation, vegetation health monitoring, gross primary production estimation References: Wu et al. (2009) - Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices Sensor-specific formulas: - Dragonette-1: ((Band 20 - Band 17) - 0.2 * (Band 20 - Band 5)) * (Band 20 / Band 17) - Dragonette-2/3: ((Band24 - Band21) - 0.2 * (Band24 - Band9)) * (Band24 / Band21) - GeoEye-1: ((NIR - Panchromatic) - 0.2 * (NIR - Green)) * (NIR / Panchromatic) - Sentinel-2: ((B6 - B5) - 0.2 * (B6 - B3)) * (B6 / B5) --- #### mND705 — Modified Normalized Difference (705, 750 and 445 nm) URL: https://docs.geopera.com/spectral-indices/mnd705 Category: vegetation Modified Normalized Difference (705, 750 and 445 nm) - A spectral index for vegetation applications. Formula: (RE2 - RE1)/(RE2 + RE1 - A) Wavelengths: RE2 (740), RE1 (705), A (443) Applications: vegetation References: https://doi.org/10.1016/S0034-4257(02)00010-X Sensor-specific formulas: - Dragonette-2/3: (Band23 - Band20)/(Band23 + Band20 - Band1) - GeoEye-1: (Panchromatic - Red)/(Panchromatic + Red - Blue) - Sentinel-2: (B6 - B5)/(B6 + B5 - B1) --- #### MNLI — Modified Nonlinear Index URL: https://docs.geopera.com/spectral-indices/mnli Category: vegetation A modified version of the Nonlinear vegetation index that uses SWIR bands. This index is designed to improve vegetation monitoring in areas with high biomass by incorporating SWIR reflectance. Formula: ((swir² - nir) * 1.5) / (swir² + nir + 0.5) Wavelengths: nir (824), swir (1760) Applications: Forest crown closure mapping, LAI estimation, High biomass vegetation monitoring, Forest canopy analysis, Vegetation structure assessment References: Pu et al. (2008). Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index. Sensor-specific formulas: - SuperView-2: ((SWIR² - NIR1) * 1.5) / (SWIR² + NIR1 + 0.5) - WorldView 3: ((SWIR4² - NIR1) * 1.5) / (SWIR4² + NIR1 + 0.5) --- #### MSAVI — Modified Soil Adjusted Vegetation Index URL: https://docs.geopera.com/spectral-indices/msavi Category: vegetation Self-adjusting vegetation index that minimizes soil background influence without requiring soil line parameters. Automatically adjusts for varying soil conditions. Formula: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) Wavelengths: Red (640-760 nm), NIR (780-1400 nm) Applications: Vegetation Analysis, Agricultural Monitoring, Soil Background Correction, Arid Environment Vegetation, Crop Health Assessment References: Qi et al. (1994) Sensor-specific formulas: - BJ3A: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - BJ3N: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Dragonette-1: 0.5 * (2 * Band 22 + 1 - sqrt((2 * Band 22 + 1)^2 - 8 * (Band 22 - Band 11))) - Dragonette-2/3: 0.5 * (2 * Band26 + 1 - sqrt((2 * Band26 + 1)^2 - 8 * (Band26 - Band15))) - Gaofen-1: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Gaofen-2: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - GeoEye-1: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Göktürk-1: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Jilin-1: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Jilin-1 GF03D: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - KOMPSAT-3: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - KOMPSAT-3A: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Landsat 8/9: 0.5 * (2 * B5 + 1 - sqrt((2 * B5 + 1)^2 - 8 * (B5 - B4))) - NAIP: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Sentinel-2: 0.5 * (2 * B8 + 1 - sqrt((2 * B8 + 1)^2 - 8 * (B8 - B4))) - SuperView Neo: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - SuperView-1: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - SuperView-2: 0.5 * (2 * NIR1 + 1 - sqrt((2 * NIR1 + 1)^2 - 8 * (NIR1 - Red))) - TripleSat: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - WorldView 2: 0.5 * (2 * NIR1 + 1 - sqrt((2 * NIR1 + 1)^2 - 8 * (NIR1 - Red))) - WorldView 3: 0.5 * (2 * NIR1 + 1 - sqrt((2 * NIR1 + 1)^2 - 8 * (NIR1 - Red))) - WorldView 4: 0.5 * (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - WorldView Legion: 0.5 * (2 * NIR1 + 1 - sqrt((2 * NIR1 + 1)^2 - 8 * (NIR1 - Red))) --- #### MSAVIhyper — Modified Soil Adjusted Vegetation Index Hyper URL: https://docs.geopera.com/spectral-indices/msavi_hyper Category: vegetation Hyperspectral version of MSAVI optimized for precise wavelength bands. Provides enhanced vegetation monitoring with reduced soil background effects. Formula: 0.5 * ((2 * 800nm + 1) - sqrt((2 * 800nm + 1)^2 - 8 * (800nm - 670nm))) Wavelengths: Red (670 nm), NIR (800 nm) Applications: Vegetation Analysis, Leaf Area Index Estimation, Crop Canopy Assessment, Biomass Monitoring, Vegetation Health Analysis References: Haboudane et al. (2004) Sensor-specific formulas: - BJ3A: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - BJ3N: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Dragonette-1: 0.5 * ((2 * Band 23 + 1) - sqrt((2 * Band 23 + 1)^2 - 8 * (Band 23 - Band 13))) - Dragonette-2/3: 0.5 * ((2 * Band27 + 1) - sqrt((2 * Band27 + 1)^2 - 8 * (Band27 - Band17))) - Gaofen-1: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Gaofen-2: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - GeoEye-1: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Göktürk-1: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Jilin-1: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Jilin-1 GF03D: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - KOMPSAT-3: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - KOMPSAT-3A: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - Sentinel-2: 0.5 * ((2 * B7 + 1) - sqrt((2 * B7 + 1)^2 - 8 * (B7 - B4))) - SuperView Neo: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - SuperView-1: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - SuperView-2: 0.5 * ((2 * NIR1 + 1) - sqrt((2 * NIR1 + 1)^2 - 8 * (NIR1 - Red))) - TripleSat: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - WorldView 2: 0.5 * ((2 * NIR1 + 1) - sqrt((2 * NIR1 + 1)^2 - 8 * (NIR1 - Red))) - WorldView 3: 0.5 * ((2 * NIR1 + 1) - sqrt((2 * NIR1 + 1)^2 - 8 * (NIR1 - Red))) - WorldView 4: 0.5 * ((2 * NIR + 1) - sqrt((2 * NIR + 1)^2 - 8 * (NIR - Red))) - WorldView Legion: 0.5 * ((2 * NIR1 + 1) - sqrt((2 * NIR1 + 1)^2 - 8 * (NIR1 - Red))) --- #### MSI — Moisture Stress Index URL: https://docs.geopera.com/spectral-indices/msi Category: vegetation Simple ratio index for detecting vegetation water stress and moisture content. Higher values indicate greater water stress in vegetation. Formula: 1600nm / 820nm Wavelengths: NIR (820 nm), SWIR1 (1600 nm) Applications: Vegetation Water Stress, Drought Monitoring, Irrigation Management, Forest Fire Risk Assessment, Agricultural Water Management References: Hunt & Rock (1989) Sensor-specific formulas: - SuperView-2: SWIR / NIR1 - WorldView 3: SWIR2 / NIR1 --- #### MSR670 — Modified Simple Ratio 670,800 URL: https://docs.geopera.com/spectral-indices/msr670 Category: vegetation A modified simple ratio vegetation index optimized for boreal forest applications. It normalizes the difference between NIR and red reflectance, providing improved LAI estimation in forest environments. Formula: (nir - red) / (nir + red) Wavelengths: red (670), nir (800) Applications: Boreal forest monitoring, LAI estimation in forests, Forest canopy analysis, Vegetation density assessment, Forest health monitoring References: Chen (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications.; Haboudane et al. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies.; Wu et al. (2008). Estimating chlorophyll content from hyperspectral vegetation indices. Sensor-specific formulas: - BJ3A: (NIR - Red) / (NIR + Red) - BJ3N: (NIR - Red) / (NIR + Red) - Dragonette-1: (Band 23 - Band 13) / (Band 23 + Band 13) - Dragonette-2/3: (Band27 - Band17) / (Band27 + Band17) - Gaofen-1: (NIR - Red) / (NIR + Red) - Gaofen-2: (NIR - Red) / (NIR + Red) - GeoEye-1: (NIR - Red) / (NIR + Red) - Göktürk-1: (NIR - Red) / (NIR + Red) - Jilin-1: (NIR - Red) / (NIR + Red) - Jilin-1 GF03D: (NIR - Red) / (NIR + Red) - KOMPSAT-3: (NIR - Red) / (NIR + Red) - KOMPSAT-3A: (NIR - Red) / (NIR + Red) - Sentinel-2: (B7 - B4) / (B7 + B4) - SuperView Neo: (NIR - Red) / (NIR + Red) - SuperView-1: (NIR - Red) / (NIR + Red) - SuperView-2: (NIR1 - Red) / (NIR1 + Red) - TripleSat: (NIR - Red) / (NIR + Red) - WorldView 2: (NIR1 - Red) / (NIR1 + Red) - WorldView 3: (NIR1 - Red) / (NIR1 + Red) - WorldView 4: (NIR - Red) / (NIR + Red) - WorldView Legion: (NIR1 - Red) / (NIR1 + Red) --- #### mSR705 — Modified Simple Ratio (705 and 445 nm) URL: https://docs.geopera.com/spectral-indices/msr705 Category: vegetation Modified Simple Ratio (705 and 445 nm) - A spectral index for vegetation applications. Formula: (RE2 - A)/(RE2 + A) Wavelengths: RE2 (740), A (443) Applications: vegetation References: https://doi.org/10.1016/S0034-4257(02)00010-X Sensor-specific formulas: - Dragonette-2/3: (Band23 - Band1)/(Band23 + Band1) - Gaofen-1: (Panchromatic - Blue)/(Panchromatic + Blue) - Gaofen-2: (Panchromatic - Blue)/(Panchromatic + Blue) - GeoEye-1: (Panchromatic - Blue)/(Panchromatic + Blue) - Göktürk-1: (Panchromatic - Blue)/(Panchromatic + Blue) - Jilin-1 GF03D: (Panchromatic - Blue)/(Panchromatic + Blue) - KOMPSAT-3: (Panchromatic - Blue)/(Panchromatic + Blue) - KOMPSAT-3A: (Panchromatic - Blue)/(Panchromatic + Blue) - Sentinel-2: (B6 - B1)/(B6 + B1) - SuperView-2: (Red_Edge - Blue)/(Red_Edge + Blue) - WorldView 2: (Red_Edge - Coastal)/(Red_Edge + Coastal) - WorldView 3: (Red Edge - Coastal)/(Red Edge + Coastal) - WorldView 4: (Panchromatic - Blue)/(Panchromatic + Blue) - WorldView Legion: (Red_Edge - Coastal)/(Red_Edge + Coastal) --- #### MSR705 — Modified Simple Ratio 705,750 URL: https://docs.geopera.com/spectral-indices/msr705_750 Category: vegetation Modified Simple Ratio 705,750 is a vegetation index that uses the red-edge spectral region to assess vegetation characteristics, particularly chlorophyll content. It calculates the normalized difference between reflectance at 750nm and 705nm wavelengths. Formula: (750nm - 705nm) / (750nm + 705nm) Wavelengths: 705 (705), 750 (750) Applications: chlorophyll content estimation, vegetation health monitoring, red-edge analysis, crop monitoring, vegetation stress detection References: Wu et al. (2008) - Estimating chlorophyll content from hyperspectral vegetation indices Sensor-specific formulas: - Dragonette-1: (Band 20 - Band 16) / (Band 20 + Band 16) - Dragonette-2/3: (Band24 - Band20) / (Band24 + Band20) - GeoEye-1: (NIR - Red) / (NIR + Red) - Sentinel-2: (B6 - B5) / (B6 + B5) --- #### MTVI1 — Modified Triangular Vegetation Index 1 URL: https://docs.geopera.com/spectral-indices/mtvi1 Category: vegetation Enhanced vegetation index combining green, red, and near-infrared reflectance for improved leaf area index estimation and vegetation monitoring. Formula: 1.2 * (1.2 * (800nm - 550nm) - 2.5 * (670nm - 550nm)) Wavelengths: Green (550 nm), Red (670 nm), NIR (800 nm) Applications: Vegetation Analysis, Leaf Area Index Prediction, Crop Monitoring, Forest Health Assessment, Agricultural Management References: Haboudane et al. (2004) Sensor-specific formulas: - BJ3A: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - BJ3N: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - Dragonette-1: 1.2 * (1.2 * (Band 23 - Band 5) - 2.5 * (Band 13 - Band 5)) - Dragonette-2/3: 1.2 * (1.2 * (Band27 - Band9) - 2.5 * (Band17 - Band9)) - Gaofen-1: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - Gaofen-2: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - GeoEye-1: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - Göktürk-1: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - Jilin-1: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - Jilin-1 GF03D: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - KOMPSAT-3: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - KOMPSAT-3A: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - Sentinel-2: 1.2 * (1.2 * (B7 - B3) - 2.5 * (B4 - B3)) - SuperView Neo: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - SuperView-1: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - SuperView-2: 1.2 * (1.2 * (NIR1 - Green) - 2.5 * (Red - Green)) - TripleSat: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - WorldView 2: 1.2 * (1.2 * (NIR1 - Green) - 2.5 * (Red - Green)) - WorldView 3: 1.2 * (1.2 * (NIR1 - Green) - 2.5 * (Red - Green)) - WorldView 4: 1.2 * (1.2 * (NIR - Green) - 2.5 * (Red - Green)) - WorldView Legion: 1.2 * (1.2 * (NIR1 - Green) - 2.5 * (Red - Green)) --- #### MTVI2 — Modified Triangular Vegetation Index 2 URL: https://docs.geopera.com/spectral-indices/mtvi2 Category: vegetation Advanced triangular vegetation index with enhanced sensitivity to vegetation chlorophyll content and reduced soil background interference. Formula: (1.5 * 1.2 * (800nm - 550nm) - 2.5 * (670nm - 550nm) * (2 * 800nm + 1)^2 - (6 * 800nm - 5 * 670nm) - 0.5) Wavelengths: Green (550 nm), Red (670 nm), NIR (800 nm) Applications: Vegetation Analysis, Chlorophyll Assessment, Leaf Area Index Estimation, Crop Health Monitoring, Forest Canopy Analysis References: Haboudane et al. (2004) Sensor-specific formulas: - BJ3A: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - BJ3N: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Dragonette-1: (1.5 * 1.2 * (Band 23 - Band 5) - 2.5 * (Band 13 - Band 5) * (2 * Band 23 + 1)^2 - (6 * Band 23 - 5 * Band 13) - 0.5) - Dragonette-2/3: (1.5 * 1.2 * (Band27 - Band9) - 2.5 * (Band17 - Band9) * (2 * Band27 + 1)^2 - (6 * Band27 - 5 * Band17) - 0.5) - Gaofen-1: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Gaofen-2: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - GeoEye-1: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Göktürk-1: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Jilin-1: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Jilin-1 GF03D: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - KOMPSAT-3: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - KOMPSAT-3A: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - Sentinel-2: (1.5 * 1.2 * (B7 - B3) - 2.5 * (B4 - B3) * (2 * B7 + 1)^2 - (6 * B7 - 5 * B4) - 0.5) - SuperView Neo: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - SuperView-1: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - SuperView-2: (1.5 * 1.2 * (NIR1 - Green) - 2.5 * (Red - Green) * (2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * Red) - 0.5) - TripleSat: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - WorldView 2: (1.5 * 1.2 * (NIR1 - Green) - 2.5 * (Red - Green) * (2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * Red) - 0.5) - WorldView 3: (1.5 * 1.2 * (NIR1 - Green) - 2.5 * (Red - Green) * (2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * Red) - 0.5) - WorldView 4: (1.5 * 1.2 * (NIR - Green) - 2.5 * (Red - Green) * (2 * NIR + 1)^2 - (6 * NIR - 5 * Red) - 0.5) - WorldView Legion: (1.5 * 1.2 * (NIR1 - Green) - 2.5 * (Red - Green) * (2 * NIR1 + 1)^2 - (6 * NIR1 - 5 * Red) - 0.5) --- #### NBR — Normalized Burn Ratio URL: https://docs.geopera.com/spectral-indices/nbr Category: vegetation Burn severity index for detecting and monitoring fire damage in vegetation. Higher values indicate healthy vegetation, lower values indicate burned areas. Formula: (NIR - SWIR) / (NIR + SWIR) Wavelengths: NIR (780-1400 nm), SWIR (1400-3000 nm) Applications: Fire Damage Assessment, Burn Severity Mapping, Post-fire Recovery Monitoring, Forest Management, Disaster Response References: Key & Benson (2006) Sensor-specific formulas: - Landsat 8/9: (B5 - B7) / (B5 + B7) - Sentinel-2: (B8 - B11) / (B8 + B11) - SuperView-2: (NIR1 - SWIR) / (NIR1 + SWIR) - WorldView 3: (NIR1 - SWIR2) / (NIR1 + SWIR2) --- #### ND MIR/NIR — Normalized Difference MIR/NIR Vegetation Index URL: https://docs.geopera.com/spectral-indices/nd_mir_nir Category: vegetation Specialized vegetation index using mid-infrared and near-infrared bands. Particularly effective during strong atmospheric disturbances and for vegetation vitality assessment. Formula: (MIR - NIR) / (MIR + NIR) Wavelengths: NIR (800 nm), MIR (1300-3000 nm) Applications: Vegetation Vitality Assessment, Atmospheric Disturbance Correction, Forest Health Monitoring, Agricultural Assessment, Environmental Stress Detection References: Rock et al. (1988) Sensor-specific formulas: - Sentinel-2: (B12 - B7) / (B12 + B7) - SuperView-2: (SWIR - NIR1) / (SWIR + NIR1) - WorldView 3: (SWIR3 - NIR1) / (SWIR3 + NIR1) --- #### NDII — Normalized Difference Infrared Index URL: https://docs.geopera.com/spectral-indices/ndii Category: vegetation Vegetation water content index sensitive to changes in vegetation moisture and canopy water stress. Useful for drought monitoring and irrigation management. Formula: (850nm - 1650nm) / (850nm + 1650nm) Wavelengths: NIR (850 nm), SWIR (1650 nm) Applications: Vegetation Water Content, Drought Monitoring, Irrigation Management, Forest Fire Risk Assessment, Agricultural Water Stress References: Hardisky et al. (1983) Sensor-specific formulas: - Landsat 8/9: (B5 - B6) / (B5 + B6) - SuperView-2: (NIR1 - SWIR) / (NIR1 + SWIR) - WorldView 3: (NIR1 - SWIR3) / (NIR1 + SWIR3) --- #### NDLI — Normalized Difference Lignin Index URL: https://docs.geopera.com/spectral-indices/ndli Category: vegetation Vegetation index for detecting lignin content in plant tissues. Uses logarithmic transformation of shortwave infrared reflectance to quantify structural components of vegetation. Formula: (log(1/1754nm) - log(1/1680nm)) / (log(1/1754nm) + log(1/1680nm)) Wavelengths: SWIR1 (1680 nm), SWIR2 (1754 nm) Applications: Lignin Content Analysis, Forest Health Assessment, Plant Biochemistry, Vegetation Structure Analysis, Wood Quality Assessment References: Serrano et al. (2002) Sensor-specific formulas: - WorldView 3: (log(1/SWIR4) - log(1/SWIR3)) / (log(1/SWIR4) + log(1/SWIR3)) --- #### NDMI — Normalized Difference Moisture Index URL: https://docs.geopera.com/spectral-indices/ndmi Category: vegetation Vegetation moisture index sensitive to changes in vegetation water content. Useful for drought monitoring, irrigation management, and fire risk assessment. Formula: (820nm - 1600nm) / (820nm + 1600nm) Wavelengths: NIR (820 nm), SWIR (1600 nm) Applications: Vegetation Moisture Content, Drought Monitoring, Fire Risk Assessment, Irrigation Management, Agricultural Water Stress References: Wilson & Sader (2002) Sensor-specific formulas: - SuperView-2: (NIR1 - SWIR) / (NIR1 + SWIR) - WorldView 3: (NIR1 - SWIR2) / (NIR1 + SWIR2) --- #### NDNI — Normalized Difference Nitrogen Index URL: https://docs.geopera.com/spectral-indices/ndni Category: vegetation Normalized Difference Nitrogen Index (NDNI) is designed to estimate nitrogen concentration in vegetation canopies. It uses SWIR wavelengths that are sensitive to nitrogen absorption features, making it particularly useful for assessing crop nitrogen status and optimizing fertilizer applications in precision agriculture. Formula: (log(1/1510nm) - log(1/1680nm)) / (log(1/1510nm) + log(1/1680nm)) Wavelengths: 1510 (1510), 1680 (1680) Applications: nitrogen content estimation, fertilizer management, precision agriculture, crop nutrition monitoring, yield optimization References: Serrano et al. (2002) - Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data --- #### NDRE — Normalized Difference Red-Edge URL: https://docs.geopera.com/spectral-indices/ndre Category: vegetation Normalized Difference Red-Edge (NDRE) is a vegetation index that uses the red-edge band instead of the red band used in NDVI. It is particularly sensitive to chlorophyll content in leaves and can detect variations in crop health and nitrogen status more effectively than NDVI, especially in moderate to high biomass conditions. Formula: (NIR - RedEdge) / (NIR + RedEdge) Wavelengths: RedEdge (720-730), NIR (780-1400) Applications: crop health monitoring, nitrogen status assessment, chlorophyll content estimation, precision agriculture, yield prediction, vegetation stress detection References: Barnes et al. - Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data Sensor-specific formulas: - Dragonette-1: (Band 23 - Band 18) / (Band 23 + Band 18) - Dragonette-2/3: (Band31 - Band22) / (Band31 + Band22) - Gaofen-1: (NIR - Panchromatic) / (NIR + Panchromatic) - Gaofen-2: (NIR - Panchromatic) / (NIR + Panchromatic) - GeoEye-1: (NIR - Panchromatic) / (NIR + Panchromatic) - Göktürk-1: (NIR - Panchromatic) / (NIR + Panchromatic) - Jilin-1 GF03D: (NIR - Panchromatic) / (NIR + Panchromatic) - KOMPSAT-3: (NIR - Panchromatic) / (NIR + Panchromatic) - KOMPSAT-3A: (NIR - Panchromatic) / (NIR + Panchromatic) - Sentinel-2: (B8 - B6) / (B8 + B6) - SuperView-2: (NIR1 - Red_Edge) / (NIR1 + Red_Edge) - WorldView 2: (NIR1 - Red_Edge) / (NIR1 + Red_Edge) - WorldView 3: (NIR1 - Red Edge) / (NIR1 + Red Edge) - WorldView 4: (NIR - Panchromatic) / (NIR + Panchromatic) - WorldView Legion: (NIR1 - Red_Edge) / (NIR1 + Red_Edge) --- #### NDVI — Normalized Difference Vegetation Index URL: https://docs.geopera.com/spectral-indices/ndvi Category: vegetation Most commonly used vegetation index to assess plant health and density. Values range from -1 to 1, with higher values indicating healthier vegetation. Formula: (NIR - Red) / (NIR + Red) Wavelengths: Red (630-690 nm), NIR (770-900 nm) Applications: Agriculture, Forestry, Environmental Monitoring, Land Cover Classification References: Rouse et al. (1974) Sensor-specific formulas: - BJ3A: (NIR - Red) / (NIR + Red) - BJ3N: (NIR - Red) / (NIR + Red) - Dragonette-1: (Band 22 - Band 10) / (Band 22 + Band 10) - Dragonette-2/3: (Band26 - Band14) / (Band26 + Band14) - Gaofen-1: (NIR - Red) / (NIR + Red) - Gaofen-2: (NIR - Red) / (NIR + Red) - GeoEye-1: (NIR - Red) / (NIR + Red) - Göktürk-1: (NIR - Red) / (NIR + Red) - Jilin-1: (NIR - Red) / (NIR + Red) - Jilin-1 GF03D: (NIR - Red) / (NIR + Red) - KOMPSAT-3: (NIR - Red) / (NIR + Red) - KOMPSAT-3A: (NIR - Red) / (NIR + Red) - Landsat 8/9: (B5 - B4) / (B5 + B4) - NAIP: (NIR - Red) / (NIR + Red) - Sentinel-2: (B8 - B4) / (B8 + B4) - SuperView Neo: (NIR - Red) / (NIR + Red) - SuperView-1: (NIR - Red) / (NIR + Red) - SuperView-2: (NIR1 - Red) / (NIR1 + Red) - TripleSat: (NIR - Red) / (NIR + Red) - WorldView 2: (NIR1 - Red) / (NIR1 + Red) - WorldView 3: (NIR1 - Red) / (NIR1 + Red) - WorldView 4: (NIR - Red) / (NIR + Red) - WorldView Legion: (NIR1 - Red) / (NIR1 + Red) --- #### NDVI — Normalized Difference Vegetation Index (Classic) URL: https://docs.geopera.com/spectral-indices/ndvi_classic Category: vegetation The most widely used vegetation index for assessing vegetation health, density, and photosynthetic activity. Classic formulation using standard red and near-infrared bands. Formula: (NIR - Red) / (NIR + Red) Wavelengths: Red (620-700 nm), NIR (770-900 nm) Applications: Vegetation Health Monitoring, Agricultural Assessment, Forest Mapping, Biomass Estimation, Crop Yield Prediction, Drought Impact Assessment References: Rouse et al. (1974) Sensor-specific formulas: - BJ3A: (NIR - Red) / (NIR + Red) - BJ3N: (NIR - Red) / (NIR + Red) - Dragonette-1: (Band 22 - Band 10) / (Band 22 + Band 10) - Dragonette-2/3: (Band26 - Band14) / (Band26 + Band14) - Gaofen-1: (NIR - Red) / (NIR + Red) - Gaofen-2: (NIR - Red) / (NIR + Red) - GeoEye-1: (NIR - Red) / (NIR + Red) - Göktürk-1: (NIR - Red) / (NIR + Red) - Jilin-1: (NIR - Red) / (NIR + Red) - Jilin-1 GF03D: (NIR - Red) / (NIR + Red) - KOMPSAT-3: (NIR - Red) / (NIR + Red) - KOMPSAT-3A: (NIR - Red) / (NIR + Red) - Landsat 8/9: (B5 - B4) / (B5 + B4) - NAIP: (NIR - Red) / (NIR + Red) - Sentinel-2: (B8 - B4) / (B8 + B4) - SuperView Neo: (NIR - Red) / (NIR + Red) - SuperView-1: (NIR - Red) / (NIR + Red) - SuperView-2: (NIR1 - Red) / (NIR1 + Red) - TripleSat: (NIR - Red) / (NIR + Red) - WorldView 2: (NIR1 - Yellow) / (NIR1 + Yellow) - WorldView 3: (NIR1 - Yellow) / (NIR1 + Yellow) - WorldView 4: (NIR - Red) / (NIR + Red) - WorldView Legion: (NIR1 - Red) / (NIR1 + Red) --- #### NDVIc — Normalized Difference Vegetation Index C URL: https://docs.geopera.com/spectral-indices/ndvic Category: vegetation NDVIc is a corrected version of NDVI that incorporates SWIR bands to account for atmospheric and canopy background effects. The correction factor using SWIR bands helps improve the accuracy of vegetation assessments, particularly in areas with varying atmospheric conditions or soil backgrounds. Formula: (NIR - RED) / (NIR + RED) * (1 - (SWIR - SWIRmin) / (SWIRmax - SWIRmin)) Wavelengths: RED (640-760), NIR (780-1400), SWIR (1550-1750) Applications: vegetation monitoring with atmospheric correction, improved vegetation assessment, canopy background effect reduction, precision agriculture, vegetation stress detection References: Normalized Difference Vegetation Index with atmospheric and canopy background corrections Sensor-specific formulas: - Landsat 8/9: (B5 - B4) / (B5 + B4) * (1 - (B6 - SWIRmin) / (SWIRmax - SWIRmin)) - Sentinel-2: (B8 - B4) / (B8 + B4) * (1 - (B11 - SWIRmin) / (SWIRmax - SWIRmin)) - SuperView-2: (NIR1 - Red) / (NIR1 + Red) * (1 - (SWIR - SWIRmin) / (SWIRmax - SWIRmin)) - WorldView 3: (NIR1 - Red) / (NIR1 + Red) * (1 - (SWIR2 - SWIRmin) / (SWIRmax - SWIRmin)) --- #### NDWI — Normalized Difference Water Index (Gao) URL: https://docs.geopera.com/spectral-indices/ndwi_gao Category: vegetation Vegetation water content index for remote sensing of vegetation liquid water. Sensitive to changes in leaf water content and useful for drought stress assessment. Formula: (860nm - 1240nm) / (860nm + 1240nm) Wavelengths: NIR (860 nm), SWIR (1240 nm) Applications: Vegetation Water Content, Drought Stress Assessment, Irrigation Scheduling, Forest Fire Risk Assessment, Plant Physiology Studies References: Gao (1996) --- #### NGRDI — Normalized Green Red Difference Index URL: https://docs.geopera.com/spectral-indices/ngrdi Category: vegetation Normalized Green Red Difference Index for vegetation applications Formula: (G - R) / (G + R) Wavelengths: G (520-600 nm), R (630-690 nm) Applications: Vegetation References: https://doi.org/10.1016/0034-4257(79)90013-0 Sensor-specific formulas: - BJ3A: (Green - Red) / (Green + Red) - BJ3N: (Green - Red) / (Green + Red) - Dragonette-1: (Band 6 - Band 12) / (Band 6 + Band 12) - Dragonette-2/3: (Band10 - Band16) / (Band10 + Band16) - Gaofen-1: (Green - Red) / (Green + Red) - Gaofen-2: (Green - Red) / (Green + Red) - GeoEye-1: (Green - Red) / (Green + Red) - Göktürk-1: (Green - Red) / (Green + Red) - Jilin-1: (Green - Red) / (Green + Red) - Jilin-1 GF03D: (Green - Red) / (Green + Red) - KOMPSAT-3: (Green - Red) / (Green + Red) - KOMPSAT-3A: (Green - Red) / (Green + Red) - Landsat 8/9: (B3 - B4) / (B3 + B4) - NAIP: (Green - Red) / (Green + Red) - Sentinel-2: (B3 - B4) / (B3 + B4) - SuperView Neo: (Green - Red) / (Green + Red) - SuperView-1: (Green - Red) / (Green + Red) - SuperView-2: (Green - Red) / (Green + Red) - TripleSat: (Green - Red) / (Green + Red) - WorldView 2: (Green - Red) / (Green + Red) - WorldView 3: (Green - Red) / (Green + Red) - WorldView 4: (Green - Red) / (Green + Red) - WorldView Legion: (Green - Red) / (Green + Red) --- #### NIRv — Near-Infrared Reflectance of Vegetation URL: https://docs.geopera.com/spectral-indices/nirv Category: vegetation Near-Infrared Reflectance of Vegetation for vegetation applications Formula: ((N - R) / (N + R)) * N Wavelengths: N (770-900 nm), R (630-690 nm) Applications: Vegetation References: https://doi.org/10.1126/sciadv.1602244 Sensor-specific formulas: - BJ3A: ((NIR - Red) / (NIR + Red)) * NIR - BJ3N: ((NIR - Red) / (NIR + Red)) * NIR - Dragonette-1: ((Band 23 - Band 12) / (Band 23 + Band 12)) * Band 23 - Dragonette-2/3: ((Band29 - Band16) / (Band29 + Band16)) * Band29 - Gaofen-1: ((NIR - Red) / (NIR + Red)) * NIR - Gaofen-2: ((NIR - Red) / (NIR + Red)) * NIR - GeoEye-1: ((NIR - Red) / (NIR + Red)) * NIR - Göktürk-1: ((NIR - Red) / (NIR + Red)) * NIR - Jilin-1: ((NIR - Red) / (NIR + Red)) * NIR - Jilin-1 GF03D: ((NIR - Red) / (NIR + Red)) * NIR - KOMPSAT-3: ((NIR - Red) / (NIR + Red)) * NIR - KOMPSAT-3A: ((NIR - Red) / (NIR + Red)) * NIR - Landsat 8/9: ((B5 - B4) / (B5 + B4)) * B5 - NAIP: ((NIR - Red) / (NIR + Red)) * NIR - Sentinel-2: ((B8 - B4) / (B8 + B4)) * B8 - SuperView Neo: ((NIR - Red) / (NIR + Red)) * NIR - SuperView-1: ((NIR - Red) / (NIR + Red)) * NIR - SuperView-2: ((NIR1 - Red) / (NIR1 + Red)) * NIR1 - TripleSat: ((NIR - Red) / (NIR + Red)) * NIR - WorldView 2: ((NIR1 - Red) / (NIR1 + Red)) * NIR1 - WorldView 3: ((NIR1 - Red) / (NIR1 + Red)) * NIR1 - WorldView 4: ((NIR - Red) / (NIR + Red)) * NIR - WorldView Legion: ((NIR1 - Red) / (NIR1 + Red)) * NIR1 --- #### NIRvH2 — Hyperspectral Near-Infrared Reflectance of Vegetation URL: https://docs.geopera.com/spectral-indices/nirvh2 Category: vegetation Hyperspectral Near-Infrared Reflectance of Vegetation for vegetation applications Formula: N - R - k * (lambdaN - lambdaR) Wavelengths: N (770-900 nm), R (630-690 nm) Applications: Vegetation References: https://doi.org/10.1016/j.rse.2021.112723 Sensor-specific formulas: - BJ3A: NIR - Red - k * (lambdaN - lambdaR) - BJ3N: NIR - Red - k * (lambdaN - lambdaR) - Dragonette-1: Band 23 - Band 12 - k * (lambdaN - lambdaR) - Dragonette-2/3: Band29 - Band16 - k * (lambdaN - lambdaR) - Gaofen-1: NIR - Red - k * (lambdaN - lambdaR) - Gaofen-2: NIR - Red - k * (lambdaN - lambdaR) - GeoEye-1: NIR - Red - k * (lambdaN - lambdaR) - Göktürk-1: NIR - Red - k * (lambdaN - lambdaR) - Jilin-1: NIR - Red - k * (lambdaN - lambdaR) - Jilin-1 GF03D: NIR - Red - k * (lambdaN - lambdaR) - KOMPSAT-3: NIR - Red - k * (lambdaN - lambdaR) - KOMPSAT-3A: NIR - Red - k * (lambdaN - lambdaR) - Landsat 8/9: B5 - B4 - k * (lambdaN - lambdaR) - NAIP: NIR - Red - k * (lambdaN - lambdaR) - Sentinel-2: B8 - B4 - k * (lambdaN - lambdaR) - SuperView Neo: NIR - Red - k * (lambdaN - lambdaR) - SuperView-1: NIR - Red - k * (lambdaN - lambdaR) - SuperView-2: NIR1 - Red - k * (lambdaN - lambdaR) - TripleSat: NIR - Red - k * (lambdaN - lambdaR) - WorldView 2: NIR1 - Red - k * (lambdaN - lambdaR) - WorldView 3: NIR1 - Red - k * (lambdaN - lambdaR) - WorldView 4: NIR - Red - k * (lambdaN - lambdaR) - WorldView Legion: NIR1 - Red - k * (lambdaN - lambdaR) --- #### NIRvP — Near-Infrared Reflectance of Vegetation and Incoming PAR URL: https://docs.geopera.com/spectral-indices/nirvp Category: vegetation Near-Infrared Reflectance of Vegetation and Incoming PAR for vegetation applications Formula: ((N - R) / (N + R)) * N * PAR Wavelengths: N (770-900 nm), R (630-690 nm) Applications: Vegetation References: https://doi.org/10.1016/j.rse.2021.112763 Sensor-specific formulas: - BJ3A: ((NIR - Red) / (NIR + Red)) * NIR * PAR - BJ3N: ((NIR - Red) / (NIR + Red)) * NIR * PAR - Dragonette-1: ((Band 23 - Band 12) / (Band 23 + Band 12)) * Band 23 * PAR - Dragonette-2/3: ((Band29 - Band16) / (Band29 + Band16)) * Band29 * PAR - Gaofen-1: ((NIR - Red) / (NIR + Red)) * NIR * PAR - Gaofen-2: ((NIR - Red) / (NIR + Red)) * NIR * PAR - GeoEye-1: ((NIR - Red) / (NIR + Red)) * NIR * PAR - Göktürk-1: ((NIR - Red) / (NIR + Red)) * NIR * PAR - Jilin-1: ((NIR - Red) / (NIR + Red)) * NIR * PAR - Jilin-1 GF03D: ((NIR - Red) / (NIR + Red)) * NIR * PAR - KOMPSAT-3: ((NIR - Red) / (NIR + Red)) * NIR * PAR - KOMPSAT-3A: ((NIR - Red) / (NIR + Red)) * NIR * PAR - Landsat 8/9: ((B5 - B4) / (B5 + B4)) * B5 * PAR - NAIP: ((NIR - Red) / (NIR + Red)) * NIR * PAR - Sentinel-2: ((B8 - B4) / (B8 + B4)) * B8 * PAR - SuperView Neo: ((NIR - Red) / (NIR + Red)) * NIR * PAR - SuperView-1: ((NIR - Red) / (NIR + Red)) * NIR * PAR - SuperView-2: ((NIR1 - Red) / (NIR1 + Red)) * NIR1 * PAR - TripleSat: ((NIR - Red) / (NIR + Red)) * NIR * PAR - WorldView 2: ((NIR1 - Red) / (NIR1 + Red)) * NIR1 * PAR - WorldView 3: ((NIR1 - Red) / (NIR1 + Red)) * NIR1 * PAR - WorldView 4: ((NIR - Red) / (NIR + Red)) * NIR * PAR - WorldView Legion: ((NIR1 - Red) / (NIR1 + Red)) * NIR1 * PAR --- #### NLI — Nonlinear vegetation index URL: https://docs.geopera.com/spectral-indices/nli Category: vegetation A vegetation index that uses a nonlinear relationship between NIR and red bands to reduce the saturation effect at high biomass levels. The squared NIR term helps maintain sensitivity to vegetation changes in dense canopies. Formula: (NIR² - Red) / (NIR² + Red) Wavelengths: red (640-680), nir (780-900) Applications: Dense vegetation monitoring, LAI estimation with reduced saturation, Forest canopy analysis, High biomass vegetation assessment, Agricultural crop monitoring References: Goel & Qin (1994). Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation.; Chen (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications.; Pu et al. (2008). Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index. Sensor-specific formulas: - BJ3A: (NIR² - Red) / (NIR² + Red) - BJ3N: (NIR² - Red) / (NIR² + Red) - Dragonette-1: (Band 23² - Band 12) / (Band 23² + Band 12) - Dragonette-2/3: (Band29² - Band16) / (Band29² + Band16) - Gaofen-1: (NIR² - Red) / (NIR² + Red) - Gaofen-2: (NIR² - Red) / (NIR² + Red) - GeoEye-1: (NIR² - Red) / (NIR² + Red) - Göktürk-1: (NIR² - Red) / (NIR² + Red) - Jilin-1: (NIR² - Red) / (NIR² + Red) - Jilin-1 GF03D: (NIR² - Red) / (NIR² + Red) - KOMPSAT-3: (NIR² - Red) / (NIR² + Red) - KOMPSAT-3A: (NIR² - Red) / (NIR² + Red) - Landsat 8/9: (B5² - B4) / (B5² + B4) - NAIP: (NIR² - Red) / (NIR² + Red) - Sentinel-2: (B8² - B4) / (B8² + B4) - SuperView Neo: (NIR² - Red) / (NIR² + Red) - SuperView-1: (NIR² - Red) / (NIR² + Red) - SuperView-2: (NIR1² - Red) / (NIR1² + Red) - TripleSat: (NIR² - Red) / (NIR² + Red) - WorldView 2: (NIR1² - Red) / (NIR1² + Red) - WorldView 3: (NIR1² - Red) / (NIR1² + Red) - WorldView 4: (NIR² - Red) / (NIR² + Red) - WorldView Legion: (NIR1² - Red) / (NIR1² + Red) --- #### NMDI — Normalized Multi-band Drought Index URL: https://docs.geopera.com/spectral-indices/nmdi Category: vegetation Normalized Multi-band Drought Index for vegetation applications Formula: (N - (S1 - S2))/(N + (S1 - S2)) Wavelengths: N (770-900 nm), S1 (1550-1750 nm), S2 (2080-2350 nm) Applications: Vegetation References: https://doi.org/10.1029/2007GL031021 Sensor-specific formulas: - Landsat 8/9: (B5 - (B6 - B7))/(B5 + (B6 - B7)) - Sentinel-2: (B8 - (B11 - B12))/(B8 + (B11 - B12)) - WorldView 3: (NIR1 - (SWIR3 - SWIR6))/(NIR1 + (SWIR3 - SWIR6)) --- #### Norm G — Normalized Green URL: https://docs.geopera.com/spectral-indices/norm_g Category: vegetation Normalized green reflectance component for vegetation analysis. Provides relative contribution of green band reflectance in visible-NIR spectrum. Formula: Green / (NIR + Red + Green) Wavelengths: Green (490-570 nm), Red (640-760 nm), NIR (780-1400 nm) Applications: Vegetation Analysis, Color Space Analysis, Spectral Normalization, Vegetation Phenology, Agricultural Monitoring References: Normalized spectral components Sensor-specific formulas: - BJ3A: Blue / (NIR + Red + Blue) - BJ3N: Blue / (NIR + Red + Blue) - Dragonette-1: Band 1 / (Band 22 + Band 11 + Band 1) - Dragonette-2/3: Band5 / (Band26 + Band15 + Band5) - Gaofen-1: Panchromatic / (NIR + Red + Panchromatic) - Gaofen-2: Panchromatic / (NIR + Red + Panchromatic) - GeoEye-1: Panchromatic / (NIR + Red + Panchromatic) - Göktürk-1: Panchromatic / (NIR + Red + Panchromatic) - Jilin-1: Blue / (NIR + Red + Blue) - Jilin-1 GF03D: Panchromatic / (NIR + Red + Panchromatic) - KOMPSAT-3: Panchromatic / (NIR + Red + Panchromatic) - KOMPSAT-3A: Panchromatic / (NIR + Red + Panchromatic) - Landsat 8/9: B3 / (B5 + B4 + B3) - NAIP: Green / (NIR + Red + Green) - Sentinel-2: B3 / (B8 + B4 + B3) - SuperView Neo: Blue / (NIR + Red + Blue) - SuperView-1: Blue / (NIR + Red + Blue) - SuperView-2: Blue / (NIR1 + Red + Blue) - TripleSat: Blue / (NIR + Red + Blue) - WorldView 2: Panchromatic / (NIR1 + Red + Panchromatic) - WorldView 3: Panchromatic / (NIR1 + Red + Panchromatic) - WorldView 4: Panchromatic / (NIR + Red + Panchromatic) - WorldView Legion: Green / (NIR1 + Red + Green) --- #### Norm NIR — Normalized Near-Infrared URL: https://docs.geopera.com/spectral-indices/norm_nir Category: vegetation Normalized near-infrared reflectance component for vegetation analysis. Highlights vegetation structural properties and biomass distribution. Formula: NIR / (NIR + Red + Green) Wavelengths: Green (490-570 nm), Red (640-760 nm), NIR (780-1400 nm) Applications: Vegetation Analysis, Biomass Assessment, Canopy Structure Analysis, Spectral Normalization, Agricultural Monitoring References: Normalized spectral components Sensor-specific formulas: - BJ3A: NIR / (NIR + Red + Blue) - BJ3N: NIR / (NIR + Red + Blue) - Dragonette-1: Band 22 / (Band 22 + Band 11 + Band 1) - Dragonette-2/3: Band26 / (Band26 + Band15 + Band5) - Gaofen-1: NIR / (NIR + Red + Panchromatic) - Gaofen-2: NIR / (NIR + Red + Panchromatic) - GeoEye-1: NIR / (NIR + Red + Panchromatic) - Göktürk-1: NIR / (NIR + Red + Panchromatic) - Jilin-1: NIR / (NIR + Red + Blue) - Jilin-1 GF03D: NIR / (NIR + Red + Panchromatic) - KOMPSAT-3: NIR / (NIR + Red + Panchromatic) - KOMPSAT-3A: NIR / (NIR + Red + Panchromatic) - Landsat 8/9: B5 / (B5 + B4 + B3) - NAIP: NIR / (NIR + Red + Green) - Sentinel-2: B8 / (B8 + B4 + B3) - SuperView Neo: NIR / (NIR + Red + Blue) - SuperView-1: NIR / (NIR + Red + Blue) - SuperView-2: NIR1 / (NIR1 + Red + Blue) - TripleSat: NIR / (NIR + Red + Blue) - WorldView 2: NIR1 / (NIR1 + Red + Panchromatic) - WorldView 3: NIR1 / (NIR1 + Red + Panchromatic) - WorldView 4: NIR / (NIR + Red + Panchromatic) - WorldView Legion: NIR1 / (NIR1 + Red + Green) --- #### Norm R — Normalized Red URL: https://docs.geopera.com/spectral-indices/norm_r Category: vegetation Normalized red reflectance component for vegetation analysis. Useful for analyzing chlorophyll absorption and vegetation stress indicators. Formula: Red / (NIR + Red + Green) Wavelengths: Green (490-570 nm), Red (640-760 nm), NIR (780-1400 nm) Applications: Vegetation Analysis, Chlorophyll Assessment, Vegetation Stress Detection, Spectral Normalization, Agricultural Monitoring References: Normalized spectral components Sensor-specific formulas: - BJ3A: Red / (NIR + Red + Blue) - BJ3N: Red / (NIR + Red + Blue) - Dragonette-1: Band 11 / (Band 22 + Band 11 + Band 1) - Dragonette-2/3: Band15 / (Band26 + Band15 + Band5) - Gaofen-1: Red / (NIR + Red + Panchromatic) - Gaofen-2: Red / (NIR + Red + Panchromatic) - GeoEye-1: Red / (NIR + Red + Panchromatic) - Göktürk-1: Red / (NIR + Red + Panchromatic) - Jilin-1: Red / (NIR + Red + Blue) - Jilin-1 GF03D: Red / (NIR + Red + Panchromatic) - KOMPSAT-3: Red / (NIR + Red + Panchromatic) - KOMPSAT-3A: Red / (NIR + Red + Panchromatic) - Landsat 8/9: B4 / (B5 + B4 + B3) - NAIP: Red / (NIR + Red + Green) - Sentinel-2: B4 / (B8 + B4 + B3) - SuperView Neo: Red / (NIR + Red + Blue) - SuperView-1: Red / (NIR + Red + Blue) - SuperView-2: Red / (NIR1 + Red + Blue) - TripleSat: Red / (NIR + Red + Blue) - WorldView 2: Red / (NIR1 + Red + Panchromatic) - WorldView 3: Red / (NIR1 + Red + Panchromatic) - WorldView 4: Red / (NIR + Red + Panchromatic) - WorldView Legion: Red / (NIR1 + Red + Green) --- #### NormG — Normalized Green URL: https://docs.geopera.com/spectral-indices/normg Category: vegetation Normalized Green for vegetation applications Formula: G/(N + G + R) Wavelengths: G (520-600 nm), N (770-900 nm), R (630-690 nm) Applications: Vegetation References: https://doi.org/10.2134/agronj2004.0314 Sensor-specific formulas: - BJ3A: Green/(NIR + Green + Red) - BJ3N: Green/(NIR + Green + Red) - Dragonette-1: Band 6/(Band 23 + Band 6 + Band 12) - Dragonette-2/3: Band10/(Band29 + Band10 + Band16) - Gaofen-1: Green/(NIR + Green + Red) - Gaofen-2: Green/(NIR + Green + Red) - GeoEye-1: Green/(NIR + Green + Red) - Göktürk-1: Green/(NIR + Green + Red) - Jilin-1: Green/(NIR + Green + Red) - Jilin-1 GF03D: Green/(NIR + Green + Red) - KOMPSAT-3: Green/(NIR + Green + Red) - KOMPSAT-3A: Green/(NIR + Green + Red) - Landsat 8/9: B3/(B5 + B3 + B4) - NAIP: Green/(NIR + Green + Red) - Sentinel-2: B3/(B8 + B3 + B4) - SuperView Neo: Green/(NIR + Green + Red) - SuperView-1: Green/(NIR + Green + Red) - SuperView-2: Green/(NIR1 + Green + Red) - TripleSat: Green/(NIR + Green + Red) - WorldView 2: Green/(NIR1 + Green + Red) - WorldView 3: Green/(NIR1 + Green + Red) - WorldView 4: Green/(NIR + Green + Red) - WorldView Legion: Green/(NIR1 + Green + Red) --- #### NormNIR — Normalized NIR URL: https://docs.geopera.com/spectral-indices/normnir Category: vegetation Normalized NIR for vegetation applications Formula: N/(N + G + R) Wavelengths: N (770-900 nm), G (520-600 nm), R (630-690 nm) Applications: Vegetation References: https://doi.org/10.2134/agronj2004.0314 Sensor-specific formulas: - BJ3A: NIR/(NIR + Green + Red) - BJ3N: NIR/(NIR + Green + Red) - Dragonette-1: Band 23/(Band 23 + Band 6 + Band 12) - Dragonette-2/3: Band29/(Band29 + Band10 + Band16) - Gaofen-1: NIR/(NIR + Green + Red) - Gaofen-2: NIR/(NIR + Green + Red) - GeoEye-1: NIR/(NIR + Green + Red) - Göktürk-1: NIR/(NIR + Green + Red) - Jilin-1: NIR/(NIR + Green + Red) - Jilin-1 GF03D: NIR/(NIR + Green + Red) - KOMPSAT-3: NIR/(NIR + Green + Red) - KOMPSAT-3A: NIR/(NIR + Green + Red) - Landsat 8/9: B5/(B5 + B3 + B4) - NAIP: NIR/(NIR + Green + Red) - Sentinel-2: B8/(B8 + B3 + B4) - SuperView Neo: NIR/(NIR + Green + Red) - SuperView-1: NIR/(NIR + Green + Red) - SuperView-2: NIR1/(NIR1 + Green + Red) - TripleSat: NIR/(NIR + Green + Red) - WorldView 2: NIR1/(NIR1 + Green + Red) - WorldView 3: NIR1/(NIR1 + Green + Red) - WorldView 4: NIR/(NIR + Green + Red) - WorldView Legion: NIR1/(NIR1 + Green + Red) --- #### NormR — Normalized Red URL: https://docs.geopera.com/spectral-indices/normr Category: vegetation Normalized Red for vegetation applications Formula: R/(N + G + R) Wavelengths: R (630-690 nm), N (770-900 nm), G (520-600 nm) Applications: Vegetation References: https://doi.org/10.2134/agronj2004.0314 Sensor-specific formulas: - BJ3A: Red/(NIR + Green + Red) - BJ3N: Red/(NIR + Green + Red) - Dragonette-1: Band 12/(Band 23 + Band 6 + Band 12) - Dragonette-2/3: Band16/(Band29 + Band10 + Band16) - Gaofen-1: Red/(NIR + Green + Red) - Gaofen-2: Red/(NIR + Green + Red) - GeoEye-1: Red/(NIR + Green + Red) - Göktürk-1: Red/(NIR + Green + Red) - Jilin-1: Red/(NIR + Green + Red) - Jilin-1 GF03D: Red/(NIR + Green + Red) - KOMPSAT-3: Red/(NIR + Green + Red) - KOMPSAT-3A: Red/(NIR + Green + Red) - Landsat 8/9: B4/(B5 + B3 + B4) - NAIP: Red/(NIR + Green + Red) - Sentinel-2: B4/(B8 + B3 + B4) - SuperView Neo: Red/(NIR + Green + Red) - SuperView-1: Red/(NIR + Green + Red) - SuperView-2: Red/(NIR1 + Green + Red) - TripleSat: Red/(NIR + Green + Red) - WorldView 2: Red/(NIR1 + Green + Red) - WorldView 3: Red/(NIR1 + Green + Red) - WorldView 4: Red/(NIR + Green + Red) - WorldView Legion: Red/(NIR1 + Green + Red) --- #### NPCI — Normalized Difference 680/430 Normalized Pigment Chlorophyll Index URL: https://docs.geopera.com/spectral-indices/npci Category: vegetation A spectral index focused on vegetation and chlorophyll assessment. Uses reflectance at 430 nm and 680 nm wavelengths to provide insights into plant pigment and chlorophyll content. Formula: (R680 - R430) / (R680 + R430) Wavelengths: Applications: Vegetation analysis, Vegetation chlorophyll measurement References: Multiple references for plant physiological status and crop management --- #### NPQI — Normalized Difference 415/435 Normalized Phaeophytinization Index URL: https://docs.geopera.com/spectral-indices/npqi Category: vegetation A spectral index using specific wavelengths to assess vegetation characteristics, particularly related to chlorophyll and plant stress. Provides insights into vegetation health and chlorophyll content using normalized difference calculations. Formula: (R415 - R435) / (R415 + R435) Wavelengths: Applications: Vegetation, Vegetation - Chlorophyll, Vegetation - Stress References: Barnes et al. (1992); le Maire et al. (2004); Peñuelas et al. (1995; 1998) --- #### NRFIg — Normalized Rapeseed Flowering Index Green URL: https://docs.geopera.com/spectral-indices/nrfig Category: vegetation Normalized Rapeseed Flowering Index Green for vegetation applications Formula: (G - S2) / (G + S2) Wavelengths: G (520-600 nm), S2 (2080-2350 nm) Applications: Vegetation References: https://doi.org/10.3390/rs13010105 Sensor-specific formulas: - Landsat 8/9: (B3 - B7) / (B3 + B7) - Sentinel-2: (B3 - B12) / (B3 + B12) - WorldView 3: (Green - SWIR6) / (Green + SWIR6) --- #### NRFIr — Normalized Rapeseed Flowering Index Red URL: https://docs.geopera.com/spectral-indices/nrfir Category: vegetation Normalized Rapeseed Flowering Index Red for vegetation applications Formula: (R - S2) / (R + S2) Wavelengths: R (630-690 nm), S2 (2080-2350 nm) Applications: Vegetation References: https://doi.org/10.3390/rs13010105 Sensor-specific formulas: - Landsat 8/9: (B4 - B7) / (B4 + B7) - Sentinel-2: (B4 - B12) / (B4 + B12) - WorldView 3: (Red - SWIR6) / (Red + SWIR6) --- #### OCVI — Optimized Chlorophyll Vegetation Index URL: https://docs.geopera.com/spectral-indices/ocvi Category: vegetation Optimized Chlorophyll Vegetation Index for vegetation applications Formula: (N / G) * (R / G) ** cexp Wavelengths: N (770-900 nm), G (520-600 nm), R (630-690 nm) Applications: Vegetation References: http://dx.doi.org/10.1007/s11119-008-9075-z Sensor-specific formulas: - BJ3A: (NIR / Green) * (Red / Green) ** cexp - BJ3N: (NIR / Green) * (Red / Green) ** cexp - Dragonette-1: (Band 23 / Band 6) * (Band 12 / Band 6) ** cexp - Dragonette-2/3: (Band29 / Band10) * (Band16 / Band10) ** cexp - Gaofen-1: (NIR / Green) * (Red / Green) ** cexp - Gaofen-2: (NIR / Green) * (Red / Green) ** cexp - GeoEye-1: (NIR / Green) * (Red / Green) ** cexp - Göktürk-1: (NIR / Green) * (Red / Green) ** cexp - Jilin-1: (NIR / Green) * (Red / Green) ** cexp - Jilin-1 GF03D: (NIR / Green) * (Red / Green) ** cexp - KOMPSAT-3: (NIR / Green) * (Red / Green) ** cexp - KOMPSAT-3A: (NIR / Green) * (Red / Green) ** cexp - Landsat 8/9: (B5 / B3) * (B4 / B3) ** cexp - NAIP: (NIR / Green) * (Red / Green) ** cexp - Sentinel-2: (B8 / B3) * (B4 / B3) ** cexp - SuperView Neo: (NIR / Green) * (Red / Green) ** cexp - SuperView-1: (NIR / Green) * (Red / Green) ** cexp - SuperView-2: (NIR1 / Green) * (Red / Green) ** cexp - TripleSat: (NIR / Green) * (Red / Green) ** cexp - WorldView 2: (NIR1 / Green) * (Red / Green) ** cexp - WorldView 3: (NIR1 / Green) * (Red / Green) ** cexp - WorldView 4: (NIR / Green) * (Red / Green) ** cexp - WorldView Legion: (NIR1 / Green) * (Red / Green) ** cexp --- #### OSAVI — Optimized Soil Adjusted Vegetation Index URL: https://docs.geopera.com/spectral-indices/osavi Category: vegetation An optimized vegetation index designed to minimize soil background influences on vegetation measurements. Helps discriminate vegetation characteristics and can be used for estimating crop chlorophyll content, detecting vegetation stress, and monitoring plant health. Formula: (1 + 0.16) * (NIR - Red) / (NIR + Red + 0.16) Wavelengths: Applications: Vegetation analysis, Crop chlorophyll estimation, Vegetation stress detection, Plant health monitoring References: Rondeaux; Steven; and Baret (1996) --- #### OSAVI2 — Optimized Soil Adjusted Vegetation Index 2 URL: https://docs.geopera.com/spectral-indices/osavi2 Category: vegetation OSAVI2 is a variant of the Optimized Soil Adjusted Vegetation Index that uses red-edge bands (750nm and 705nm) instead of traditional NIR and red bands. This modification improves sensitivity to vegetation changes while maintaining the soil brightness correction factor of 0.16, making it particularly effective for vegetation monitoring in areas with variable soil backgrounds. Formula: (1 + 0.16) * (750nm - 705nm) / (750nm + 705nm + 0.16) Wavelengths: 705 (705), 750 (750) Applications: vegetation monitoring with soil adjustment, red-edge vegetation analysis, agricultural monitoring, sparse vegetation assessment, soil background minimization References: Wu et al. (2008) - Development of red-edge based vegetation indices Sensor-specific formulas: - Dragonette-1: (1 + 0.16) * (Band 20 - Band 16) / (Band 20 + Band 16 + 0.16) - Dragonette-2/3: (1 + 0.16) * (Band24 - Band20) / (Band24 + Band20 + 0.16) - GeoEye-1: (1 + 0.16) * (NIR - Red) / (NIR + Red + 0.16) - Sentinel-2: (1 + 0.16) * (B6 - B5) / (B6 + B5 + 0.16) --- #### PNDVI — Pan NDVI URL: https://docs.geopera.com/spectral-indices/pndvi Category: vegetation Pan NDVI is designed for vegetation analysis, calculating vegetation health and density by comparing near-infrared and visible light reflectance. It provides a normalized method to assess vegetation health across multiple spectral bands. Formula: (NIR - (GREEN + RED + BLUE)) / (NIR + (GREEN + RED + BLUE)) Wavelengths: BLUE (420-480), GREEN (490-570), RED (640-760), NIR (780-1400) Applications: vegetation studies, leaf area index estimation, vegetation health assessment, vegetation density mapping References: Wang et al. (2007) - Application of Pan NDVI in rice leaf area estimation Sensor-specific formulas: - BJ3A: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - BJ3N: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - Dragonette-2/3: (Band31 - (Band8 + Band20 + Band1)) / (Band31 + (Band8 + Band20 + Band1)) - Gaofen-1: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - Gaofen-2: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - GeoEye-1: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - Göktürk-1: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - Jilin-1: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - Jilin-1 GF03D: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - KOMPSAT-3: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - KOMPSAT-3A: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - Landsat 8/9: (B5 - (B3 + B4 + B1)) / (B5 + (B3 + B4 + B1)) - NAIP: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - Sentinel-2: (B8 - (B3 + B4 + B1)) / (B8 + (B3 + B4 + B1)) - SuperView Neo: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - SuperView-1: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - SuperView-2: (NIR1 - (Green + Red + Blue)) / (NIR1 + (Green + Red + Blue)) - TripleSat: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - WorldView 2: (NIR1 - (Green + Red + Blue)) / (NIR1 + (Green + Red + Blue)) - WorldView 3: (NIR1 - (Green + Red + Blue)) / (NIR1 + (Green + Red + Blue)) - WorldView 4: (NIR - (Green + Red + Blue)) / (NIR + (Green + Red + Blue)) - WorldView Legion: (NIR1 - (Green + Red + Blue)) / (NIR1 + (Green + Red + Blue)) --- #### PRI — Photochemical Reflectance Index URL: https://docs.geopera.com/spectral-indices/pri_standard Category: vegetation The Photochemical Reflectance Index (PRI) was developed by Gamon, Penuelas, and Field (1992) to track diurnal changes in photosynthetic efficiency. PRI detects changes in xanthophyll cycle pigments that occur during plant stress, providing a measure of light use efficiency and general ecosystem health through remote sensing. Formula: (531nm - 570nm) / (531nm + 570nm) Wavelengths: 531 (531), 570 (570) Applications: photosynthetic efficiency monitoring, plant stress detection, water stress assessment, light use efficiency estimation, ecosystem health monitoring, carbon uptake estimation, drought stress detection References: Gamon, J.A., Penuelas, J., and Field, C.B. (1992) - A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1), 35-44; Gamon, J.A., Serrano, L., and Surfus, J.S. (1997) - The photochemical reflectance index: an optical indicator of photosynthetic radiation-use efficiency across species, functional types, and nutrient levels. Oecologia, 112(4), 492-501 Sensor-specific formulas: - Dragonette-1: (Band 4 - Band 6) / (Band 4 + Band 6) - Dragonette-2/3: (Band8 - Band10) / (Band8 + Band10) --- #### PRI528/587 — Normalized Difference 528/587 Photochemical Reflectance Index URL: https://docs.geopera.com/spectral-indices/pri_528_587 Category: vegetation A spectral index designed to track changes in photosynthetic efficiency across different vegetation types. The index tracks diurnal changes in photosynthetic efficiency and can be used for chlorophyll content estimation in forest canopies. Formula: (R528 - R567) / (R528 + R567) Wavelengths: Applications: Vegetation - Chlorophyll analysis, Photosynthetic efficiency tracking References: Gamon et al. (1992); Zarco-Tejada et al. (2001) --- #### PRI531/570 — Normalized Difference 531/570 Photochemical Reflectance Index URL: https://docs.geopera.com/spectral-indices/pri_531_570 Category: vegetation A photochemical reflectance index designed to assess photosynthetic efficiency. The index helps researchers understand plant physiological conditions by measuring spectral reflectance changes related to photosynthetic processes. Formula: (R531 - R570) / (R531 + R570) Wavelengths: Applications: Vegetation analysis, Vegetation chlorophyll assessment, Photosynthetic efficiency References: Gamon et al. (1997); Peñuelas & Filella (1998) --- #### PRI570/531 — Normalized Difference 570/531 Photochemical Reflectance Index URL: https://docs.geopera.com/spectral-indices/pri_570_531 Category: vegetation A photochemical reflectance index that tracks changes in photosynthetic efficiency by comparing reflectance at two specific wavelengths. Used for remote sensing of photosynthetic-light-use efficiency in vegetation. Formula: (R570 - R531) / (R570 + R531) Wavelengths: Applications: Vegetation - Chlorophyll analysis, Photosynthetic efficiency assessment References: Gamon et al. (1992); Nichol et al. (2000) --- #### PSRI — Plant Senescence Reflectance Index URL: https://docs.geopera.com/spectral-indices/psri Category: vegetation An index used to detect changes in plant pigmentation during leaf senescence and development. Helps researchers understand plant pigment changes and vegetation health across different species and developmental stages. Formula: (R678 - R500) / R750 Wavelengths: Applications: Vegetation analysis, Plant senescence detection, Pigment change monitoring References: Apan et al. (2003); Merzlyak et al. (1999); Sims & Gamon (2002) --- #### PSRI — Plant Senescence Reflectance Index URL: https://docs.geopera.com/spectral-indices/psri2 Category: vegetation Plant Senescence Reflectance Index (PSRI) is designed to detect plant stress and senescence by measuring the ratio of carotenoid to chlorophyll pigments. It is sensitive to changes in leaf pigments that occur during plant aging, stress, or fruit ripening, making it useful for monitoring crop maturity and health status. Formula: (678nm - 500nm) / 750nm Wavelengths: 500 (500), 678 (678), 750 (750) Applications: plant senescence detection, crop maturity assessment, stress monitoring, fruit ripening detection, vegetation health assessment, phenology studies References: Merzlyak et al. (1999) - Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening Sensor-specific formulas: - Dragonette-1: (Band 14 - Band 1) / Band 20 - Dragonette-2/3: (Band18 - Band5) / Band24 - GeoEye-1: (Red - Blue) / NIR - Sentinel-2: (B4 - B2) / B6 - WorldView 2: (Red - Blue) / Red_Edge - WorldView 3: (Red - Blue) / Red Edge - WorldView 4: (Red - Blue) / Panchromatic - WorldView Legion: (Red - Blue) / Red_Edge --- #### PVI — Perpendicular Vegetation Index URL: https://docs.geopera.com/spectral-indices/pvi Category: vegetation A vegetation index that accounts for soil background effects by using a perpendicular approach. Can estimate leaf area index and green biomass, useful for analyzing crop canopies and vegetation health. The index helps evaluate vegetation characteristics while accounting for soil influences. Formula: (1/√(a² + 1)) * (NIR - a*Red - b) Wavelengths: Applications: Vegetation assessment, Leaf area index estimation, Green biomass estimation, Crop canopy analysis References: Bannari et al. (1995); Baret & Guyot (1991); Huete (1988) --- #### RCC — Red Chromatic Coordinate URL: https://docs.geopera.com/spectral-indices/rcc Category: vegetation Red Chromatic Coordinate for vegetation applications Formula: R / (R + G + B) Wavelengths: R (630-690 nm), G (520-600 nm), B (450-520 nm) Applications: Vegetation References: https://doi.org/10.1016/0034-4257(87)90088-5 Sensor-specific formulas: - BJ3A: Red / (Red + Green + Blue) - BJ3N: Red / (Red + Green + Blue) - Dragonette-1: Band 12 / (Band 12 + Band 6 + Band 1) - Dragonette-2/3: Band16 / (Band16 + Band10 + Band4) - Gaofen-1: Red / (Red + Green + Blue) - Gaofen-2: Red / (Red + Green + Blue) - GeoEye-1: Red / (Red + Green + Blue) - Göktürk-1: Red / (Red + Green + Blue) - Jilin-1: Red / (Red + Green + Blue) - Jilin-1 GF03D: Red / (Red + Green + Blue) - KOMPSAT-3: Red / (Red + Green + Blue) - KOMPSAT-3A: Red / (Red + Green + Blue) - Landsat 8/9: B4 / (B4 + B3 + B2) - NAIP: Red / (Red + Green + Blue) - Sentinel-2: B4 / (B4 + B3 + B2) - SuperView Neo: Red / (Red + Green + Blue) - SuperView-1: Red / (Red + Green + Blue) - SuperView-2: Red / (Red + Green + Blue) - TripleSat: Red / (Red + Green + Blue) - WorldView 2: Red / (Red + Green + Blue) - WorldView 3: Red / (Red + Green + Blue) - WorldView 4: Red / (Red + Green + Blue) - WorldView Legion: Red / (Red + Green + Blue) --- #### RDI — Simple Ratio MIR/NIR Ratio Drought Index URL: https://docs.geopera.com/spectral-indices/rdi Category: vegetation A spectral index used for detecting drought conditions by comparing middle infrared and near-infrared bands. Useful for assessing vegetation stress and drought conditions using spectral reflectance measurements. Formula: MIR / NIR Wavelengths: Applications: Vegetation monitoring, Drought detection, Vegetation stress assessment References: Pinder; J. E.; McLeod; K. W. (1999) --- #### RDVI — Renormalized Difference Vegetation Index URL: https://docs.geopera.com/spectral-indices/rdvi Category: vegetation A vegetation index used for analyzing vegetation characteristics, particularly for quantifying biophysical vegetation characteristics and predicting green leaf area index. Provides insights into vegetation health, density, and chlorophyll content using near-infrared and red wavelength measurements. Formula: (NIR - Red) / √(NIR + Red) Wavelengths: Applications: Vegetation analysis, Vegetation chlorophyll assessment, Precision agriculture, Green leaf area index prediction References: Chen; J.M. (1996); Gitelson; A.A. (2004); Haboudane et al. (2004) --- #### REDSI — Red-Edge Disease Stress Index URL: https://docs.geopera.com/spectral-indices/redsi Category: vegetation Red-Edge Disease Stress Index for vegetation applications Formula: ((705.0 - 665.0) * (RE3 - R) - (783.0 - 665.0) * (RE1 - R)) / (2.0 * R) Wavelengths: RE3 (773-793 nm), R (630-690 nm), RE1 (700-710 nm) Applications: Vegetation References: https://doi.org/10.3390/s18030868 Sensor-specific formulas: - Dragonette-1: ((705.0 - 665.0) * (Band 22 - Band 12) - (783.0 - 665.0) * (Band 16 - Band 12)) / (2.0 * Band 12) - Dragonette-2/3: ((705.0 - 665.0) * (Band26 - Band16) - (783.0 - 665.0) * (Band20 - Band16)) / (2.0 * Band16) - Gaofen-1: ((705.0 - 665.0) * (NIR - Red) - (783.0 - 665.0) * (Panchromatic - Red)) / (2.0 * Red) - Gaofen-2: ((705.0 - 665.0) * (NIR - Red) - (783.0 - 665.0) * (Panchromatic - Red)) / (2.0 * Red) - GeoEye-1: ((705.0 - 665.0) * (NIR - Red) - (783.0 - 665.0) * (Panchromatic - Red)) / (2.0 * Red) - Göktürk-1: ((705.0 - 665.0) * (NIR - Red) - (783.0 - 665.0) * (Panchromatic - Red)) / (2.0 * Red) - Jilin-1 GF03D: ((705.0 - 665.0) * (NIR - Red) - (783.0 - 665.0) * (Panchromatic - Red)) / (2.0 * Red) - KOMPSAT-3: ((705.0 - 665.0) * (NIR - Red) - (783.0 - 665.0) * (Panchromatic - Red)) / (2.0 * Red) - KOMPSAT-3A: ((705.0 - 665.0) * (NIR - Red) - (783.0 - 665.0) * (Panchromatic - Red)) / (2.0 * Red) - Sentinel-2: ((705.0 - 665.0) * (B7 - B4) - (783.0 - 665.0) * (B5 - B4)) / (2.0 * B4) - SuperView-2: ((705.0 - 665.0) * (NIR1 - Red) - (783.0 - 665.0) * (Red_Edge - Red)) / (2.0 * Red) - WorldView 2: ((705.0 - 665.0) * (NIR1 - Red) - (783.0 - 665.0) * (Red_Edge - Red)) / (2.0 * Red) - WorldView 3: ((705.0 - 665.0) * (NIR1 - Red) - (783.0 - 665.0) * (Red Edge - Red)) / (2.0 * Red) - WorldView 4: ((705.0 - 665.0) * (NIR - Red) - (783.0 - 665.0) * (Panchromatic - Red)) / (2.0 * Red) - WorldView Legion: ((705.0 - 665.0) * (NIR1 - Red) - (783.0 - 665.0) * (Red_Edge - Red)) / (2.0 * Red) --- #### REIP1 — Red-Edge Inflection Point 1 URL: https://docs.geopera.com/spectral-indices/reip1 Category: vegetation An index that calculates the red edge inflection point, which is the wavelength of maximum slope in the red edge region. REIP is sensitive to chlorophyll content and vegetation stress. Formula: 700 + 40 * (((red + nir) / 2 - re1) / (re2 - re1)) Wavelengths: red (670), re1 (700), re2 (740), nir (780) Applications: Chlorophyll content estimation, Red edge position detection, Vegetation stress monitoring, Crop nutrient status assessment, Early disease detection References: Clevers et al. (2002). Derivation of the red edge index using the MERIS standard band setting.; Herrmann et al. (2011). LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Sensor-specific formulas: - Dragonette-1: 700 + 40 * (((Band 13 + Band 22) / 2 - Band 16) / (Band 19 - Band 16)) - Dragonette-2/3: 700 + 40 * (((Band17 + Band26) / 2 - Band20) / (Band23 - Band20)) - Sentinel-2: 700 + 40 * (((B4 + B7) / 2 - B5) / (B6 - B5)) --- #### REIP2 — Red-Edge Inflection Point 2 URL: https://docs.geopera.com/spectral-indices/reip2 Category: vegetation A variant of the red edge inflection point calculation using slightly different wavelengths. This index provides an alternative measurement of the red edge position for chlorophyll assessment. Formula: 702 + 40 * (((red + nir) / 2 - re1) / (re2 - re1)) Wavelengths: red (667), re1 (702), re2 (742), nir (782) Applications: Chlorophyll content mapping, Red edge position analysis, Vegetation health monitoring, LAI estimation, Precision agriculture References: Herrmann et al. (2011). LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands.; le Maire et al. (2004). Towards universal broad leaf chlorophyll indices. Sensor-specific formulas: - Dragonette-1: 702 + 40 * (((Band 13 + Band 22) / 2 - Band 16) / (Band 19 - Band 16)) - Dragonette-2/3: 702 + 40 * (((Band17 + Band26) / 2 - Band20) / (Band23 - Band20)) - Sentinel-2: 702 + 40 * (((B4 + B7) / 2 - B5) / (B6 - B5)) --- #### reNDVI — Red Edge NDVI URL: https://docs.geopera.com/spectral-indices/rendvi Category: vegetation A normalized difference vegetation index using red edge bands instead of traditional red and NIR. This index is particularly sensitive to chlorophyll content and vegetation stress. Formula: (re2 - re1) / (re2 + re1) Wavelengths: re1 (710), re2 (750) Applications: Chlorophyll content estimation, Vegetation stress detection, Crop health monitoring, Early disease detection, Precision agriculture References: Gitelson & Merzlyak (1994). Quantitative estimation of chlorophyll-a using reflectance spectra.; Ahamed et al. (2011). A review of remote sensing methods for biomass feedstock production. Sensor-specific formulas: - Dragonette-1: (Band 20 - Band 17) / (Band 20 + Band 17) - Dragonette-2/3: (Band24 - Band21) / (Band24 + Band21) - GeoEye-1: (NIR - Panchromatic) / (NIR + Panchromatic) - Sentinel-2: (B6 - B5) / (B6 + B5) --- #### REP — Red-Edge Position Linear Interpolation URL: https://docs.geopera.com/spectral-indices/rep Category: vegetation Red-Edge Position Linear Interpolation (REP) is a spectral index that detects the red-edge position through linear interpolation. The red-edge position is a key indicator of vegetation health, chlorophyll content, and plant stress, representing the inflection point between red absorption and NIR reflectance. Formula: 700 + 40 * ((670nm + 780nm)/2 - 700nm) / (740nm - 700nm) Wavelengths: 670 (670), 700 (700), 740 (740), 780 (780) Applications: hyperspectral remote sensing - red-edge position, vegetation health monitoring, chlorophyll content estimation, plant stress detection, crop monitoring, vegetation phenology assessment References: Terrestrial chlorophyll index studies (1988-2011); Spectral resolution determination research; Vegetation reflectance and soil contamination analysis Sensor-specific formulas: - Dragonette-1: Band 16 + 40 * ((Band 13 + Band 22)/2 - Band 16) / (Band 19 - Band 16) - Dragonette-2/3: Band20 + 40 * ((Band17 + Band26)/2 - Band20) / (Band23 - Band20) - Sentinel-2: B5 + 40 * ((B4 + B7)/2 - B5) / (B6 - B5) --- #### RGBVI — Red Green Blue Vegetation Index URL: https://docs.geopera.com/spectral-indices/rgbvi Category: vegetation Red Green Blue Vegetation Index for vegetation applications Formula: (G ** 2.0 - B * R)/(G ** 2.0 + B * R) Wavelengths: G (520-600 nm), B (450-520 nm), R (630-690 nm) Applications: Vegetation References: https://doi.org/10.1016/j.jag.2015.02.012 Sensor-specific formulas: - BJ3A: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - BJ3N: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - Dragonette-1: (Band 6 ** 2.0 - Band 1 * Band 12)/(Band 6 ** 2.0 + Band 1 * Band 12) - Dragonette-2/3: (Band10 ** 2.0 - Band4 * Band16)/(Band10 ** 2.0 + Band4 * Band16) - Gaofen-1: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - Gaofen-2: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - GeoEye-1: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - Göktürk-1: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - Jilin-1: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - Jilin-1 GF03D: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - KOMPSAT-3: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - KOMPSAT-3A: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - Landsat 8/9: (B3 ** 2.0 - B2 * B4)/(B3 ** 2.0 + B2 * B4) - NAIP: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - Sentinel-2: (B3 ** 2.0 - B2 * B4)/(B3 ** 2.0 + B2 * B4) - SuperView Neo: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - SuperView-1: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - SuperView-2: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - TripleSat: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - WorldView 2: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - WorldView 3: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - WorldView 4: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) - WorldView Legion: (Green ** 2.0 - Blue * Red)/(Green ** 2.0 + Blue * Red) --- #### RGRI — Red-Green Ratio Index URL: https://docs.geopera.com/spectral-indices/rgri Category: vegetation Red-Green Ratio Index for vegetation applications Formula: R/G Wavelengths: R (630-690 nm), G (520-600 nm) Applications: Vegetation References: https://doi.org/10.1016/j.jag.2014.03.018 Sensor-specific formulas: - BJ3A: Red/Green - BJ3N: Red/Green - Dragonette-1: Band 12/Band 6 - Dragonette-2/3: Band16/Band10 - Gaofen-1: Red/Green - Gaofen-2: Red/Green - GeoEye-1: Red/Green - Göktürk-1: Red/Green - Jilin-1: Red/Green - Jilin-1 GF03D: Red/Green - KOMPSAT-3: Red/Green - KOMPSAT-3A: Red/Green - Landsat 8/9: B4/B3 - NAIP: Red/Green - Sentinel-2: B4/B3 - SuperView Neo: Red/Green - SuperView-1: Red/Green - SuperView-2: Red/Green - TripleSat: Red/Green - WorldView 2: Red/Green - WorldView 3: Red/Green - WorldView 4: Red/Green - WorldView Legion: Red/Green --- #### RI — Normalized Difference Red/Green Redness Index URL: https://docs.geopera.com/spectral-indices/ri Category: vegetation A spectral index designed to highlight the difference between red and green spectral bands, which can provide insights into vegetation health, pigmentation, and other environmental characteristics. Used for assessing vegetation characteristics and redness. Formula: (Red - Green) / (Red + Green) Wavelengths: Applications: Vegetation studies, Vegetation health assessment, Pigmentation analysis References: Bannari et al. (1995); Escadafal et al. (1991; 1994) --- #### RSR — Reduced Simple Ratio URL: https://docs.geopera.com/spectral-indices/rsr Category: vegetation A spectral index designed to assess vegetation characteristics using near-infrared and mid-infrared spectral bands. Used for estimating aboveground tree biomass and leaf area index in forest applications. Formula: NIR * Red * (MIRmax - MIR) / (MIRmax - MIRmin) Wavelengths: Applications: Vegetation analysis, Biomass estimation, Forest monitoring References: Heiskanen; J. (2006) --- #### RVI — Simple Ratio 800/670 Ratio Vegetation Index URL: https://docs.geopera.com/spectral-indices/rvi Category: vegetation A simple ratio vegetation index used for assessing vegetation characteristics such as leaf area index and canopy chlorophyll density. Useful for monitoring crop yield, estimating vegetation biomass, and analyzing plant health and productivity. Formula: NIR / Red Wavelengths: Applications: Vegetation analysis, Crop yield monitoring, Vegetation biomass estimation, Plant health assessment References: Multiple remote sensing vegetation studies --- #### RVSI — Red-Edge Stress Vegetation Index URL: https://docs.geopera.com/spectral-indices/rvsi Category: vegetation A spectral index focused on vegetation analysis, specifically examining stress in vegetation using red-edge wavelengths. Designed to assess vegetation condition through spectral reflectance in the red-edge region of the electromagnetic spectrum. Formula: (R718 + R748) / 2 - R733 Wavelengths: Applications: Hyperspectral remote sensing - Red-edge position, Vegetation analysis, Vegetation stress detection References: Original formula --- #### S — Saturation URL: https://docs.geopera.com/spectral-indices/saturation Category: vegetation A spectral index that calculates the color saturation by comparing the maximum and minimum RGB values. Used for analyzing vegetation characteristics and spectral indices for degradation of natural environments. Formula: (max(R,G,B) - min(R,G,B)) / max(R,G,B) Wavelengths: Applications: Vegetation analysis, Environmental degradation assessment, Color analysis References: Escadafal; R. et al. (1994) Sensor-specific formulas: - BJ3A: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - BJ3N: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - Dragonette-1: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - Dragonette-2/3: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - Gaofen-1: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - Gaofen-2: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - GeoEye-1: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - Göktürk-1: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - Jilin-1: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - Jilin-1 GF03D: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - KOMPSAT-3: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - KOMPSAT-3A: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - Landsat 8/9: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - NAIP: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - Sentinel-2: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - SuperView Neo: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - SuperView-1: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - SuperView-2: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - TripleSat: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - WorldView 1: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - WorldView 2: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - WorldView 3: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - WorldView 4: (max(R,G,B) - min(R,G,B)) / max(R,G,B) - WorldView Legion: (max(R,G,B) - min(R,G,B)) / max(R,G,B) --- #### S2REP — Sentinel-2 Red-Edge Position URL: https://docs.geopera.com/spectral-indices/s2rep Category: vegetation Sentinel-2 Red-Edge Position for vegetation applications Formula: 705.0 + 35.0 * ((((RE3 + R) / 2.0) - RE1) / (RE2 - RE1)) Wavelengths: RE3 (773-793 nm), R (630-690 nm), RE1 (700-710 nm), RE2 (730-745 nm) Applications: Vegetation References: https://doi.org/10.1016/j.isprsjprs.2013.04.007 Sensor-specific formulas: - Dragonette-1: 705.0 + 35.0 * ((((Band 22 + Band 12) / 2.0) - Band 16) / (Band 19 - Band 16)) - Dragonette-2/3: 705.0 + 35.0 * ((((Band26 + Band16) / 2.0) - Band20) / (Band23 - Band20)) - Sentinel-2: 705.0 + 35.0 * ((((B7 + B4) / 2.0) - B5) / (B6 - B5)) --- #### SARVI — Soil Adjusted and Atmospherically Resistant Vegetation Index URL: https://docs.geopera.com/spectral-indices/sarvi Category: vegetation Soil Adjusted and Atmospherically Resistant Vegetation Index for vegetation applications Formula: (1 + L)*(N - (R - (R - B))) / (N + (R - (R - B)) + L) Wavelengths: N (770-900 nm), R (630-690 nm), B (450-520 nm) Applications: Vegetation References: https://doi.org/10.1109/36.134076 Sensor-specific formulas: - BJ3A: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - BJ3N: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - Dragonette-1: (1 + L)*(Band 23 - (Band 12 - (Band 12 - Band 1))) / (Band 23 + (Band 12 - (Band 12 - Band 1)) + L) - Dragonette-2/3: (1 + L)*(Band29 - (Band16 - (Band16 - Band4))) / (Band29 + (Band16 - (Band16 - Band4)) + L) - Gaofen-1: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - Gaofen-2: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - GeoEye-1: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - Göktürk-1: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - Jilin-1: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - Jilin-1 GF03D: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - KOMPSAT-3: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - KOMPSAT-3A: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - Landsat 8/9: (1 + L)*(B5 - (B4 - (B4 - B2))) / (B5 + (B4 - (B4 - B2)) + L) - NAIP: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - Sentinel-2: (1 + L)*(B8 - (B4 - (B4 - B2))) / (B8 + (B4 - (B4 - B2)) + L) - SuperView Neo: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - SuperView-1: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - SuperView-2: (1 + L)*(NIR1 - (Red - (Red - Blue))) / (NIR1 + (Red - (Red - Blue)) + L) - TripleSat: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - WorldView 2: (1 + L)*(NIR1 - (Red - (Red - Blue))) / (NIR1 + (Red - (Red - Blue)) + L) - WorldView 3: (1 + L)*(NIR1 - (Red - (Red - Blue))) / (NIR1 + (Red - (Red - Blue)) + L) - WorldView 4: (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) - WorldView Legion: (1 + L)*(NIR1 - (Red - (Red - Blue))) / (NIR1 + (Red - (Red - Blue)) + L) --- #### SAVI — Soil Adjusted Vegetation Index URL: https://docs.geopera.com/spectral-indices/savi Category: vegetation Vegetation index that minimizes soil brightness influences. The L factor is typically set to 0.5 for moderate vegetation cover. Formula: ((NIR - Red) / (NIR + Red + L)) * (1 + L) Wavelengths: Red (630-690 nm), NIR (770-900 nm) Applications: Arid and Semi-arid Regions, Sparse Vegetation Monitoring, Soil-Vegetation Studies References: Huete (1988) Sensor-specific formulas: - BJ3A: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - BJ3N: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - Dragonette-1: ((Band 22 - Band 10) / (Band 22 + Band 10 + L)) * (1 + L) - Dragonette-2/3: ((Band26 - Band14) / (Band26 + Band14 + L)) * (1 + L) - Gaofen-1: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - Gaofen-2: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - GeoEye-1: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - Göktürk-1: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - Jilin-1: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - Jilin-1 GF03D: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - KOMPSAT-3: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - KOMPSAT-3A: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - Landsat 8/9: ((B5 - B4) / (B5 + B4 + L)) * (1 + L) - NAIP: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - Sentinel-2: ((B8 - B4) / (B8 + B4 + L)) * (1 + L) - SuperView Neo: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - SuperView-1: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - SuperView-2: ((NIR1 - Red) / (NIR1 + Red + L)) * (1 + L) - TripleSat: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - WorldView 2: ((NIR1 - Red) / (NIR1 + Red + L)) * (1 + L) - WorldView 3: ((NIR1 - Red) / (NIR1 + Red + L)) * (1 + L) - WorldView 4: ((NIR - Red) / (NIR + Red + L)) * (1 + L) - WorldView Legion: ((NIR1 - Red) / (NIR1 + Red + L)) * (1 + L) --- #### SAVI2 — Soil-Adjusted Vegetation Index 2 URL: https://docs.geopera.com/spectral-indices/savi2 Category: vegetation Soil-Adjusted Vegetation Index 2 for vegetation applications Formula: N / (R + (slb / sla)) Wavelengths: N (770-900 nm), R (630-690 nm) Applications: Vegetation References: https://doi.org/10.1080/01431169008955053 Sensor-specific formulas: - BJ3A: NIR / (Red + (slb / sla)) - BJ3N: NIR / (Red + (slb / sla)) - Dragonette-1: Band 23 / (Band 12 + (slb / sla)) - Dragonette-2/3: Band29 / (Band16 + (slb / sla)) - Gaofen-1: NIR / (Red + (slb / sla)) - Gaofen-2: NIR / (Red + (slb / sla)) - GeoEye-1: NIR / (Red + (slb / sla)) - Göktürk-1: NIR / (Red + (slb / sla)) - Jilin-1: NIR / (Red + (slb / sla)) - Jilin-1 GF03D: NIR / (Red + (slb / sla)) - KOMPSAT-3: NIR / (Red + (slb / sla)) - KOMPSAT-3A: NIR / (Red + (slb / sla)) - Landsat 8/9: B5 / (B4 + (slb / sla)) - NAIP: NIR / (Red + (slb / sla)) - Sentinel-2: B8 / (B4 + (slb / sla)) - SuperView Neo: NIR / (Red + (slb / sla)) - SuperView-1: NIR / (Red + (slb / sla)) - SuperView-2: NIR1 / (Red + (slb / sla)) - TripleSat: NIR / (Red + (slb / sla)) - WorldView 2: NIR1 / (Red + (slb / sla)) - WorldView 3: NIR1 / (Red + (slb / sla)) - WorldView 4: NIR / (Red + (slb / sla)) - WorldView Legion: NIR1 / (Red + (slb / sla)) --- #### SBI — Tasselled Cap - brightness URL: https://docs.geopera.com/spectral-indices/sbi Category: vegetation A spectral index for analyzing vegetation characteristics across different spectral bands. The Tasselled Cap transformation creates a new coordinate system that aligns with physical scene characteristics. Formula: 0.3037 * Blue + 0.2793 * Green + 0.4743 * Red + 0.5585 * NIR + 0.5082 * SWIR1 + 0.1863 * SWIR2 Wavelengths: blue (450-520), green (520-600), red (630-690), nir (760-900), swir1 (1150-1750), swir2 (2080-2350) Applications: Vegetation analysis, Land cover classification, Change detection, Soil brightness assessment References: Bannari et al. (1995). A review of vegetation indices.; Crist & Cicone (1984). A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap. Sensor-specific formulas: - Landsat 8/9: 0.3037 * B1 + 0.2793 * B3 + 0.4743 * B4 + 0.5585 * B5 + 0.5082 * B9 + 0.1863 * B7 - Sentinel-2: 0.3037 * B1 + 0.2793 * B3 + 0.4743 * B4 + 0.5585 * B8 + 0.5082 * B11 + 0.1863 * B12 - WorldView 3: 0.3037 * Blue + 0.2793 * Green + 0.4743 * Red + 0.5585 * NIR1 + 0.5082 * SWIR1 + 0.1863 * SWIR6 --- #### SeLI — Sentinel-2 LAI Green Index URL: https://docs.geopera.com/spectral-indices/seli Category: vegetation Sentinel-2 LAI Green Index for vegetation applications Formula: (N2 - RE1) / (N2 + RE1) Wavelengths: RE1 (700-710 nm) Applications: Vegetation References: https://doi.org/10.3390/s19040904 Sensor-specific formulas: - Dragonette-1: (N2 - Band 16) / (N2 + Band 16) - Dragonette-2/3: (N2 - Band20) / (N2 + Band20) - Gaofen-1: (N2 - Panchromatic) / (N2 + Panchromatic) - Gaofen-2: (N2 - Panchromatic) / (N2 + Panchromatic) - GeoEye-1: (N2 - Panchromatic) / (N2 + Panchromatic) - Göktürk-1: (N2 - Panchromatic) / (N2 + Panchromatic) - Jilin-1 GF03D: (N2 - Panchromatic) / (N2 + Panchromatic) - KOMPSAT-3: (N2 - Panchromatic) / (N2 + Panchromatic) - KOMPSAT-3A: (N2 - Panchromatic) / (N2 + Panchromatic) - Sentinel-2: (N2 - B5) / (N2 + B5) - SuperView-2: (N2 - Red_Edge) / (N2 + Red_Edge) - WorldView 1: (N2 - Panchromatic) / (N2 + Panchromatic) - WorldView 2: (N2 - Red_Edge) / (N2 + Red_Edge) - WorldView 3: (N2 - Red Edge) / (N2 + Red Edge) - WorldView 4: (N2 - Panchromatic) / (N2 + Panchromatic) - WorldView Legion: (N2 - Red_Edge) / (N2 + Red_Edge) --- #### SEVI — Shadow-Eliminated Vegetation Index URL: https://docs.geopera.com/spectral-indices/sevi Category: vegetation Shadow-Eliminated Vegetation Index for vegetation applications Formula: (N/R) + fdelta * (1.0/R) Wavelengths: N (770-900 nm), R (630-690 nm) Applications: Vegetation References: https://doi.org/10.1080/17538947.2018.1495770 Sensor-specific formulas: - BJ3A: (NIR/Red) + fdelta * (1.0/Red) - BJ3N: (NIR/Red) + fdelta * (1.0/Red) - Dragonette-1: (Band 23/Band 12) + fdelta * (1.0/Band 12) - Dragonette-2/3: (Band29/Band16) + fdelta * (1.0/Band16) - Gaofen-1: (NIR/Red) + fdelta * (1.0/Red) - Gaofen-2: (NIR/Red) + fdelta * (1.0/Red) - GeoEye-1: (NIR/Red) + fdelta * (1.0/Red) - Göktürk-1: (NIR/Red) + fdelta * (1.0/Red) - Jilin-1: (NIR/Red) + fdelta * (1.0/Red) - Jilin-1 GF03D: (NIR/Red) + fdelta * (1.0/Red) - KOMPSAT-3: (NIR/Red) + fdelta * (1.0/Red) - KOMPSAT-3A: (NIR/Red) + fdelta * (1.0/Red) - Landsat 8/9: (B5/B4) + fdelta * (1.0/B4) - NAIP: (NIR/Red) + fdelta * (1.0/Red) - Sentinel-2: (B8/B4) + fdelta * (1.0/B4) - SuperView Neo: (NIR/Red) + fdelta * (1.0/Red) - SuperView-1: (NIR/Red) + fdelta * (1.0/Red) - SuperView-2: (NIR1/Red) + fdelta * (1.0/Red) - TripleSat: (NIR/Red) + fdelta * (1.0/Red) - WorldView 2: (NIR1/Red) + fdelta * (1.0/Red) - WorldView 3: (NIR1/Red) + fdelta * (1.0/Red) - WorldView 4: (NIR/Red) + fdelta * (1.0/Red) - WorldView Legion: (NIR1/Red) + fdelta * (1.0/Red) --- #### SI — Shadow Index URL: https://docs.geopera.com/spectral-indices/si Category: vegetation Shadow Index for vegetation applications Formula: ((1.0 - B) * (1.0 - G) * (1.0 - R)) ** (1/3) Wavelengths: B (450-520 nm), G (520-600 nm), R (630-690 nm) Applications: Vegetation References: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.465.8749&rep=rep1&type=pdf Sensor-specific formulas: - BJ3A: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - BJ3N: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - Dragonette-1: ((1.0 - Band 1) * (1.0 - Band 6) * (1.0 - Band 12)) ** (1/3) - Dragonette-2/3: ((1.0 - Band4) * (1.0 - Band10) * (1.0 - Band16)) ** (1/3) - Gaofen-1: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - Gaofen-2: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - GeoEye-1: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - Göktürk-1: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - Jilin-1: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - Jilin-1 GF03D: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - KOMPSAT-3: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - KOMPSAT-3A: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - Landsat 8/9: ((1.0 - B2) * (1.0 - B3) * (1.0 - B4)) ** (1/3) - NAIP: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - Sentinel-2: ((1.0 - B2) * (1.0 - B3) * (1.0 - B4)) ** (1/3) - SuperView Neo: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - SuperView-1: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - SuperView-2: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - TripleSat: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - WorldView 2: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - WorldView 3: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - WorldView 4: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) - WorldView Legion: ((1.0 - Blue) * (1.0 - Green) * (1.0 - Red)) ** (1/3) --- #### SIPI — Structure Insensitive Pigment Index URL: https://docs.geopera.com/spectral-indices/sipi Category: vegetation Structure Insensitive Pigment Index for vegetation applications Formula: (N - A) / (N - R) Wavelengths: N (770-900 nm), R (630-690 nm) Applications: Vegetation References: https://eurekamag.com/research/009/395/009395053.php Sensor-specific formulas: - BJ3A: (NIR - A) / (NIR - Red) - BJ3N: (NIR - A) / (NIR - Red) - Dragonette-1: (Band 23 - A) / (Band 23 - Band 12) - Dragonette-2/3: (Band29 - A) / (Band29 - Band16) - Gaofen-1: (NIR - A) / (NIR - Red) - Gaofen-2: (NIR - A) / (NIR - Red) - GeoEye-1: (NIR - A) / (NIR - Red) - Göktürk-1: (NIR - A) / (NIR - Red) - Jilin-1: (NIR - A) / (NIR - Red) - Jilin-1 GF03D: (NIR - A) / (NIR - Red) - KOMPSAT-3: (NIR - A) / (NIR - Red) - KOMPSAT-3A: (NIR - A) / (NIR - Red) - Landsat 8/9: (B5 - A) / (B5 - B4) - NAIP: (NIR - A) / (NIR - Red) - Sentinel-2: (B8 - A) / (B8 - B4) - SuperView Neo: (NIR - A) / (NIR - Red) - SuperView-1: (NIR - A) / (NIR - Red) - SuperView-2: (NIR1 - A) / (NIR1 - Red) - TripleSat: (NIR - A) / (NIR - Red) - WorldView 2: (NIR1 - A) / (NIR1 - Red) - WorldView 3: (NIR1 - A) / (NIR1 - Red) - WorldView 4: (NIR - A) / (NIR - Red) - WorldView Legion: (NIR1 - A) / (NIR1 - Red) --- #### SIPI — Structure Insensitive Pigment Index URL: https://docs.geopera.com/spectral-indices/sipi2 Category: vegetation The Structure Insensitive Pigment Index (SIPI) was developed by Peñuelas et al. (1995) to assess the ratio of carotenoids to chlorophyll-a while minimizing the effects of canopy structure variation. SIPI is particularly useful for detecting plant stress and senescence, as chlorophylls tend to decline more rapidly than carotenoids under stress conditions. Formula: (800nm - 445nm) / (800nm - 680nm) Wavelengths: 445 (445), 680 (680), 800 (800) Applications: plant stress detection, carotenoid/chlorophyll ratio assessment, leaf senescence monitoring, physiological status evaluation, canopy structure-independent analysis, crop health monitoring References: Peñuelas, J., Baret, F., and Filella, I. (1995) - Semi-empirical indices to assess carotenoids/chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica, 31(2), 221-230 Sensor-specific formulas: - BJ3A: (NIR - Blue) / (NIR - Red) - BJ3N: (NIR - Blue) / (NIR - Red) - Dragonette-2/3: (Band27 - Band1) / (Band27 - Band18) - Gaofen-1: (NIR - Blue) / (NIR - Panchromatic) - Gaofen-2: (NIR - Blue) / (NIR - Panchromatic) - GeoEye-1: (NIR - Blue) / (NIR - Red) - Göktürk-1: (NIR - Blue) / (NIR - Panchromatic) - Jilin-1: (NIR - Blue) / (NIR - Red) - Jilin-1 GF03D: (NIR - Blue) / (NIR - Panchromatic) - KOMPSAT-3: (NIR - Blue) / (NIR - Panchromatic) - KOMPSAT-3A: (NIR - Blue) / (NIR - Panchromatic) - Sentinel-2: (B7 - B1) / (B7 - B4) - SuperView Neo: (NIR - Blue) / (NIR - Red) - SuperView-1: (NIR - Blue) / (NIR - Red) - SuperView-2: (NIR1 - Blue) / (NIR1 - Red) - TripleSat: (NIR - Blue) / (NIR - Red) - WorldView 2: (NIR1 - Coastal) / (NIR1 - Red) - WorldView 3: (NIR1 - Coastal) / (NIR1 - Red) - WorldView 4: (NIR - Blue) / (NIR - Red) - WorldView Legion: (NIR1 - Coastal) / (NIR1 - Red) --- #### SLAVI — Specific Leaf Area Vegetation Index URL: https://docs.geopera.com/spectral-indices/slavi Category: vegetation Specific Leaf Area Vegetation Index for vegetation applications Formula: N/(R + S2) Wavelengths: N (770-900 nm), R (630-690 nm), S2 (2080-2350 nm) Applications: Vegetation References: https://www.asprs.org/wp-content/uploads/pers/2000journal/february/2000_feb_183-191.pdf Sensor-specific formulas: - Landsat 8/9: B5/(B4 + B7) - Sentinel-2: B8/(B4 + B12) - WorldView 3: NIR1/(Red + SWIR6) --- #### sNIRvLSWI — SWIR-enhanced Near-Infrared Reflectance of Vegetation for LSWI URL: https://docs.geopera.com/spectral-indices/snirvlswi Category: vegetation SWIR-enhanced Near-Infrared Reflectance of Vegetation for LSWI - A spectral index for vegetation applications. Formula: ((N - S2)/(N + S2)) * N Wavelengths: N (850), S2 (2190) Applications: vegetation References: https://doi.org/10.1029/2024JG008240 Sensor-specific formulas: - Landsat 8/9: ((B5 - B7)/(B5 + B7)) * B5 - Sentinel-2: ((B8 - B12)/(B8 + B12)) * B8 - WorldView 3: ((NIR1 - SWIR6)/(NIR1 + SWIR6)) * NIR1 --- #### sNIRvNDPI — SWIR-enhanced Near-Infrared Reflectance of Vegetation for NDPI URL: https://docs.geopera.com/spectral-indices/snirvndpi Category: vegetation SWIR-enhanced Near-Infrared Reflectance of Vegetation for NDPI - A spectral index for vegetation applications. Formula: (N - (alpha * R + (1.0 - alpha) * S2))/(N + (alpha * R + (1.0 - alpha) * S2)) * N Wavelengths: N (850), R (650), S2 (2190) Applications: vegetation References: https://doi.org/10.1029/2024JG008240 Sensor-specific formulas: - Landsat 8/9: (B5 - (alpha * B4 + (1.0 - alpha) * B7))/(B5 + (alpha * B4 + (1.0 - alpha) * B7)) * B5 - Sentinel-2: (B8 - (alpha * B4 + (1.0 - alpha) * B12))/(B8 + (alpha * B4 + (1.0 - alpha) * B12)) * B8 - WorldView 3: (NIR1 - (alpha * Red + (1.0 - alpha) * SWIR6))/(NIR1 + (alpha * Red + (1.0 - alpha) * SWIR6)) * NIR1 --- #### sNIRvNDVILSWIP — SWIR-enhanced Near-Infrared Reflectance of Vegetation for the NDVI-LSWI Product URL: https://docs.geopera.com/spectral-indices/snirvndvilswip Category: vegetation SWIR-enhanced Near-Infrared Reflectance of Vegetation for the NDVI-LSWI Product - A spectral index for vegetation applications. Formula: ((N - R)/(N + R)) * ((N - S2)/(N + S2)) * N Wavelengths: N (850), R (650), S2 (2190) Applications: vegetation References: https://doi.org/10.1029/2024JG008240 Sensor-specific formulas: - Landsat 8/9: ((B5 - B4)/(B5 + B4)) * ((B5 - B7)/(B5 + B7)) * B5 - Sentinel-2: ((B8 - B4)/(B8 + B4)) * ((B8 - B12)/(B8 + B12)) * B8 - WorldView 3: ((NIR1 - Red)/(NIR1 + Red)) * ((NIR1 - SWIR6)/(NIR1 + SWIR6)) * NIR1 --- #### sNIRvNDVILSWIS — SWIR-enhanced Near-Infrared Reflectance of Vegetation for the NDVI-LSWI Sum URL: https://docs.geopera.com/spectral-indices/snirvndvilswis Category: vegetation SWIR-enhanced Near-Infrared Reflectance of Vegetation for the NDVI-LSWI Sum - A spectral index for vegetation applications. Formula: (((N - R)/(N + R)) + ((N - S2)/(N + S2))) * N Wavelengths: N (850), R (650), S2 (2190) Applications: vegetation References: https://doi.org/10.1029/2024JG008240 Sensor-specific formulas: - Landsat 8/9: (((B5 - B4)/(B5 + B4)) + ((B5 - B7)/(B5 + B7))) * B5 - Sentinel-2: (((B8 - B4)/(B8 + B4)) + ((B8 - B12)/(B8 + B12))) * B8 - WorldView 3: (((NIR1 - Red)/(NIR1 + Red)) + ((NIR1 - SWIR6)/(NIR1 + SWIR6))) * NIR1 --- #### sNIRvSWIR — SWIR-enhanced Near-Infrared Reflectance of Vegetation URL: https://docs.geopera.com/spectral-indices/snirvswir Category: vegetation SWIR-enhanced Near-Infrared Reflectance of Vegetation - A spectral index for vegetation applications. Formula: ((N - R - S2 ** 2.0)/(N + R + S2 ** 2.0)) * N Wavelengths: N (850), R (650), S2 (2190) Applications: vegetation References: https://doi.org/10.1029/2024JG008240 Sensor-specific formulas: - Landsat 8/9: ((B5 - B4 - B7 ** 2.0)/(B5 + B4 + B7 ** 2.0)) * B5 - Sentinel-2: ((B8 - B4 - B12 ** 2.0)/(B8 + B4 + B12 ** 2.0)) * B8 - WorldView 3: ((NIR1 - Red - SWIR6 ** 2.0)/(NIR1 + Red + SWIR6 ** 2.0)) * NIR1 --- #### SR — Simple Ratio URL: https://docs.geopera.com/spectral-indices/sr Category: vegetation Simple Ratio for vegetation applications Formula: N/R Wavelengths: N (770-900 nm), R (630-690 nm) Applications: Vegetation References: https://doi.org/10.2307/1936256 Sensor-specific formulas: - BJ3A: NIR/Red - BJ3N: NIR/Red - Dragonette-1: Band 23/Band 12 - Dragonette-2/3: Band29/Band16 - Gaofen-1: NIR/Red - Gaofen-2: NIR/Red - GeoEye-1: NIR/Red - Göktürk-1: NIR/Red - Jilin-1: NIR/Red - Jilin-1 GF03D: NIR/Red - KOMPSAT-3: NIR/Red - KOMPSAT-3A: NIR/Red - Landsat 8/9: B5/B4 - NAIP: NIR/Red - Sentinel-2: B8/B4 - SuperView Neo: NIR/Red - SuperView-1: NIR/Red - SuperView-2: NIR1/Red - TripleSat: NIR/Red - WorldView 2: NIR1/Red - WorldView 3: NIR1/Red - WorldView 4: NIR/Red - WorldView Legion: NIR1/Red --- #### SR2 — Simple Ratio (800 and 550 nm) URL: https://docs.geopera.com/spectral-indices/sr2 Category: vegetation Simple Ratio (800 and 550 nm) for vegetation applications Formula: N/G Wavelengths: N (770-900 nm), G (520-600 nm) Applications: Vegetation References: https://doi.org/10.1080/01431169308904370 Sensor-specific formulas: - BJ3A: NIR/Green - BJ3N: NIR/Green - Dragonette-1: Band 23/Band 6 - Dragonette-2/3: Band29/Band10 - Gaofen-1: NIR/Green - Gaofen-2: NIR/Green - GeoEye-1: NIR/Green - Göktürk-1: NIR/Green - Jilin-1: NIR/Green - Jilin-1 GF03D: NIR/Green - KOMPSAT-3: NIR/Green - KOMPSAT-3A: NIR/Green - Landsat 8/9: B5/B3 - NAIP: NIR/Green - Sentinel-2: B8/B3 - SuperView Neo: NIR/Green - SuperView-1: NIR/Green - SuperView-2: NIR1/Green - TripleSat: NIR/Green - WorldView 2: NIR1/Green - WorldView 3: NIR1/Green - WorldView 4: NIR/Green - WorldView Legion: NIR1/Green --- #### SR3 — Simple Ratio (860, 550 and 708 nm) URL: https://docs.geopera.com/spectral-indices/sr3 Category: vegetation Simple Ratio (860, 550 and 708 nm) for vegetation applications Formula: N2/(G * RE1) Wavelengths: G (520-600 nm), RE1 (700-710 nm) Applications: Vegetation References: https://doi.org/10.1016/S0034-4257(98)00046-7 Sensor-specific formulas: - Dragonette-1: N2/(Band 6 * Band 16) - Dragonette-2/3: N2/(Band10 * Band20) - Gaofen-1: N2/(Green * Panchromatic) - Gaofen-2: N2/(Green * Panchromatic) - GeoEye-1: N2/(Green * Panchromatic) - Göktürk-1: N2/(Green * Panchromatic) - Jilin-1 GF03D: N2/(Green * Panchromatic) - KOMPSAT-3: N2/(Green * Panchromatic) - KOMPSAT-3A: N2/(Green * Panchromatic) - Sentinel-2: N2/(B3 * B5) - SuperView-2: N2/(Green * Red_Edge) - WorldView 2: N2/(Green * Red_Edge) - WorldView 3: N2/(Green * Red Edge) - WorldView 4: N2/(Green * Panchromatic) - WorldView Legion: N2/(Green * Red_Edge) --- #### SR555 — Simple Ratio (555 and 750 nm) URL: https://docs.geopera.com/spectral-indices/sr555 Category: vegetation Simple Ratio (555 and 750 nm) for vegetation applications Formula: RE2 / G Wavelengths: RE2 (730-745 nm), G (520-600 nm) Applications: Vegetation References: https://doi.org/10.1016/S0176-1617(11)81633-0 Sensor-specific formulas: - Dragonette-1: Band 19 / Band 6 - Dragonette-2/3: Band23 / Band10 - Gaofen-1: Panchromatic / Green - Gaofen-2: Panchromatic / Green - GeoEye-1: Panchromatic / Green - Göktürk-1: Panchromatic / Green - Jilin-1 GF03D: Panchromatic / Green - KOMPSAT-3: Panchromatic / Green - KOMPSAT-3A: Panchromatic / Green - Sentinel-2: B6 / B3 - SuperView-2: Red_Edge / Green - WorldView 2: Red_Edge / Green - WorldView 3: Red Edge / Green - WorldView 4: Panchromatic / Green - WorldView Legion: Red_Edge / Green --- #### SR705 — Simple Ratio (705 and 750 nm) URL: https://docs.geopera.com/spectral-indices/sr705 Category: vegetation Simple Ratio (705 and 750 nm) for vegetation applications Formula: RE2 / RE1 Wavelengths: RE2 (730-745 nm), RE1 (700-710 nm) Applications: Vegetation References: https://doi.org/10.1016/S0176-1617(11)81633-0 Sensor-specific formulas: - Dragonette-1: Band 19 / Band 16 - Dragonette-2/3: Band23 / Band20 - Sentinel-2: B6 / B5 --- #### TCARI — Transformed Chlorophyll Absorption Ratio URL: https://docs.geopera.com/spectral-indices/tcari Category: vegetation An index designed to estimate vegetation chlorophyll content while minimizing the effects of leaf area index. TCARI is particularly useful for precision agriculture and crop health monitoring. Formula: 3 * ((RE1 - Red) - 0.2 * (RE1 - Green) * (RE1/Red)) Wavelengths: green (550), red (670), re1 (700) Applications: Chlorophyll content estimation, Crop health monitoring, Precision agriculture, Plant stress detection, Nutrient deficiency assessment References: Haboudane et al. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content.; Wu et al. (2008). Estimating chlorophyll content from hyperspectral vegetation indices. Sensor-specific formulas: - Dragonette-1: 3 * ((Band 16 - Band 13) - 0.2 * (Band 16 - Band 5) * (Band 16/Band 13)) - Dragonette-2/3: 3 * ((Band20 - Band17) - 0.2 * (Band20 - Band9) * (Band20/Band17)) - Gaofen-1: 3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic/Red)) - Gaofen-2: 3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic/Red)) - Göktürk-1: 3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic/Red)) - Jilin-1 GF03D: 3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic/Red)) - KOMPSAT-3: 3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic/Red)) - KOMPSAT-3A: 3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic/Red)) - Sentinel-2: 3 * ((B5 - B4) - 0.2 * (B5 - B3) * (B5/B4)) - SuperView-2: 3 * ((Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge/Red)) - WorldView 2: 3 * ((Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge/Red)) - WorldView 3: 3 * ((Red Edge - Red) - 0.2 * (Red Edge - Green) * (Red Edge/Red)) - WorldView Legion: 3 * ((Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge/Red)) --- #### TCARI/OSAVI — TCARI/OSAVI URL: https://docs.geopera.com/spectral-indices/tcari_osavi Category: vegetation TCARI/OSAVI is a combined vegetation index that integrates the Transformed Chlorophyll Absorption in Reflectance Index (TCARI) with the Optimized Soil-Adjusted Vegetation Index (OSAVI). It is designed for accurate estimation of crop chlorophyll content while minimizing the effects of soil background and leaf area index variations. Formula: 3 * (700nm - 670nm) - 0.2 * (700nm - 550nm) * (700nm / 670nm) / [(1 + 0.16) * (800nm - 670nm) / (800nm + 670nm + 0.16)] Wavelengths: 550 (550), 670 (670), 700 (700), 800 (800) Applications: crop chlorophyll content estimation, precision agriculture, vegetation stress detection, land cover change monitoring, vegetation health assessment, agricultural monitoring References: Haboudane et al. (2002) - Integrated narrow-band vegetation indices for prediction of crop chlorophyll content; Wu et al. (2008) - Estimating chlorophyll content from hyperspectral vegetation indices; Zarco-Tejada et al. (2007) - Remote sensing of vegetation biophysical parameters for detecting stress conditions Sensor-specific formulas: - Dragonette-1: 3 * (Band 16 - Band 13) - 0.2 * (Band 16 - Band 5) * (Band 16 / Band 13) / [(1 + 0.16) * (Band 23 - Band 13) / (Band 23 + Band 13 + 0.16)] - Dragonette-2/3: 3 * (Band20 - Band17) - 0.2 * (Band20 - Band9) * (Band20 / Band17) / [(1 + 0.16) * (Band27 - Band17) / (Band27 + Band17 + 0.16)] - Sentinel-2: 3 * (B5 - B4) - 0.2 * (B5 - B3) * (B5 / B4) / [(1 + 0.16) * (B7 - B4) / (B7 + B4 + 0.16)] - SuperView-2: 3 * (Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge / Red) / [(1 + 0.16) * (NIR1 - Red) / (NIR1 + Red + 0.16)] - WorldView 2: 3 * (Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge / Red) / [(1 + 0.16) * (NIR1 - Red) / (NIR1 + Red + 0.16)] - WorldView 3: 3 * (Red Edge - Red) - 0.2 * (Red Edge - Green) * (Red Edge / Red) / [(1 + 0.16) * (NIR1 - Red) / (NIR1 + Red + 0.16)] - WorldView Legion: 3 * (Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge / Red) / [(1 + 0.16) * (NIR1 - Red) / (NIR1 + Red + 0.16)] --- #### TCARI/OSAVI705 — TCARI/OSAVI 705,750 URL: https://docs.geopera.com/spectral-indices/tcari_osavi705 Category: vegetation TCARI/OSAVI 705,750 is a modified version of the TCARI/OSAVI index that uses the red-edge bands at 705nm and 750nm instead of traditional red and NIR bands. This modification improves sensitivity to chlorophyll content estimation while reducing the influence of leaf area index and soil background. Formula: 3 * (750nm - 705nm) - 0.2 * (750nm - 550nm) * (750nm / 705nm) / ((1 + 0.16) * (750nm - 705nm) / (750nm + 705nm + 0.16)) Wavelengths: 550 (550), 705 (705), 750 (750) Applications: robust leaf chlorophyll estimation, vegetation health monitoring, precision agriculture, red-edge analysis, crop stress detection, chlorophyll content mapping References: Main et al. (2011) - An investigation into robust spectral indices for leaf chlorophyll estimation; Wu et al. (2008) - Estimating chlorophyll content from hyperspectral vegetation indices Sensor-specific formulas: - Dragonette-1: 3 * (Band 20 - Band 16) - 0.2 * (Band 20 - Band 5) * (Band 20 / Band 16) / ((1 + 0.16) * (Band 20 - Band 16) / (Band 20 + Band 16 + 0.16)) - Dragonette-2/3: 3 * (Band24 - Band20) - 0.2 * (Band24 - Band9) * (Band24 / Band20) / ((1 + 0.16) * (Band24 - Band20) / (Band24 + Band20 + 0.16)) - GeoEye-1: 3 * (NIR - Red) - 0.2 * (NIR - Green) * (NIR / Red) / ((1 + 0.16) * (NIR - Red) / (NIR + Red + 0.16)) - Sentinel-2: 3 * (B6 - B5) - 0.2 * (B6 - B3) * (B6 / B5) / ((1 + 0.16) * (B6 - B5) / (B6 + B5 + 0.16)) --- #### TCARIOSAVI — TCARI/OSAVI Ratio URL: https://docs.geopera.com/spectral-indices/tcariosavi Category: vegetation TCARI/OSAVI Ratio for vegetation applications Formula: (3 * ((RE1 - R) - 0.2 * (RE1 - G) * (RE1 / R))) / (1.16 * (N - R) / (N + R + 0.16)) Wavelengths: RE1 (700-710 nm), R (630-690 nm), G (520-600 nm), N (770-900 nm) Applications: Vegetation References: https://doi.org/10.1016/S0034-4257(02)00018-4 Sensor-specific formulas: - Dragonette-1: (3 * ((Band 16 - Band 12) - 0.2 * (Band 16 - Band 6) * (Band 16 / Band 12))) / (1.16 * (Band 23 - Band 12) / (Band 23 + Band 12 + 0.16)) - Dragonette-2/3: (3 * ((Band20 - Band16) - 0.2 * (Band20 - Band10) * (Band20 / Band16))) / (1.16 * (Band29 - Band16) / (Band29 + Band16 + 0.16)) - Gaofen-1: (3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic / Red))) / (1.16 * (NIR - Red) / (NIR + Red + 0.16)) - Gaofen-2: (3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic / Red))) / (1.16 * (NIR - Red) / (NIR + Red + 0.16)) - GeoEye-1: (3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic / Red))) / (1.16 * (NIR - Red) / (NIR + Red + 0.16)) - Göktürk-1: (3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic / Red))) / (1.16 * (NIR - Red) / (NIR + Red + 0.16)) - Jilin-1 GF03D: (3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic / Red))) / (1.16 * (NIR - Red) / (NIR + Red + 0.16)) - KOMPSAT-3: (3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic / Red))) / (1.16 * (NIR - Red) / (NIR + Red + 0.16)) - KOMPSAT-3A: (3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic / Red))) / (1.16 * (NIR - Red) / (NIR + Red + 0.16)) - Sentinel-2: (3 * ((B5 - B4) - 0.2 * (B5 - B3) * (B5 / B4))) / (1.16 * (B8 - B4) / (B8 + B4 + 0.16)) - SuperView-2: (3 * ((Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge / Red))) / (1.16 * (NIR1 - Red) / (NIR1 + Red + 0.16)) - WorldView 2: (3 * ((Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge / Red))) / (1.16 * (NIR1 - Red) / (NIR1 + Red + 0.16)) - WorldView 3: (3 * ((Red Edge - Red) - 0.2 * (Red Edge - Green) * (Red Edge / Red))) / (1.16 * (NIR1 - Red) / (NIR1 + Red + 0.16)) - WorldView 4: (3 * ((Panchromatic - Red) - 0.2 * (Panchromatic - Green) * (Panchromatic / Red))) / (1.16 * (NIR - Red) / (NIR + Red + 0.16)) - WorldView Legion: (3 * ((Red_Edge - Red) - 0.2 * (Red_Edge - Green) * (Red_Edge / Red))) / (1.16 * (NIR1 - Red) / (NIR1 + Red + 0.16)) --- #### TCARIOSAVI705 — TCARI/OSAVI Ratio (705 and 750 nm) URL: https://docs.geopera.com/spectral-indices/tcariosavi705 Category: vegetation TCARI/OSAVI Ratio (705 and 750 nm) for vegetation applications Formula: (3 * ((RE2 - RE1) - 0.2 * (RE2 - G) * (RE2 / RE1))) / (1.16 * (RE2 - RE1) / (RE2 + RE1 + 0.16)) Wavelengths: RE2 (730-745 nm), RE1 (700-710 nm), G (520-600 nm) Applications: Vegetation References: https://doi.org/10.1016/j.agrformet.2008.03.005 Sensor-specific formulas: - Dragonette-1: (3 * ((Band 19 - Band 16) - 0.2 * (Band 19 - Band 6) * (Band 19 / Band 16))) / (1.16 * (Band 19 - Band 16) / (Band 19 + Band 16 + 0.16)) - Dragonette-2/3: (3 * ((Band23 - Band20) - 0.2 * (Band23 - Band10) * (Band23 / Band20))) / (1.16 * (Band23 - Band20) / (Band23 + Band20 + 0.16)) - Sentinel-2: (3 * ((B6 - B5) - 0.2 * (B6 - B3) * (B6 / B5))) / (1.16 * (B6 - B5) / (B6 + B5 + 0.16)) --- #### TCI — Triangular Chlorophyll Index URL: https://docs.geopera.com/spectral-indices/tci Category: vegetation Triangular Chlorophyll Index - A spectral index for vegetation applications. Formula: 1.2 * (RE1 - G) - 1.5 * (R - G) * (RE1 / R) ** 0.5 Wavelengths: RE1 (705), G (550), R (650) Applications: vegetation References: http://dx.doi.org/10.1109/TGRS.2007.904836 Sensor-specific formulas: - Dragonette-1: 1.2 * (Band 16 - Band 5) - 1.5 * (Band 11 - Band 5) * (Band 16 / Band 11) ** 0.5 - Dragonette-2/3: 1.2 * (Band20 - Band9) - 1.5 * (Band15 - Band9) * (Band20 / Band15) ** 0.5 - Gaofen-1: 1.2 * (Panchromatic - Green) - 1.5 * (Red - Green) * (Panchromatic / Red) ** 0.5 - Gaofen-2: 1.2 * (Panchromatic - Green) - 1.5 * (Red - Green) * (Panchromatic / Red) ** 0.5 - Göktürk-1: 1.2 * (Panchromatic - Green) - 1.5 * (Red - Green) * (Panchromatic / Red) ** 0.5 - Jilin-1 GF03D: 1.2 * (Panchromatic - Green) - 1.5 * (Red - Green) * (Panchromatic / Red) ** 0.5 - KOMPSAT-3: 1.2 * (Panchromatic - Green) - 1.5 * (Red - Green) * (Panchromatic / Red) ** 0.5 - KOMPSAT-3A: 1.2 * (Panchromatic - Green) - 1.5 * (Red - Green) * (Panchromatic / Red) ** 0.5 - Sentinel-2: 1.2 * (B5 - B3) - 1.5 * (B4 - B3) * (B5 / B4) ** 0.5 - SuperView-2: 1.2 * (Red_Edge - Green) - 1.5 * (Red - Green) * (Red_Edge / Red) ** 0.5 - WorldView 2: 1.2 * (Red_Edge - Green) - 1.5 * (Red - Green) * (Red_Edge / Red) ** 0.5 - WorldView 3: 1.2 * (Red Edge - Green) - 1.5 * (Red - Green) * (Red Edge / Red) ** 0.5 - WorldView 4: 1.2 * (Panchromatic - Green) - 1.5 * (Red - Green) * (Panchromatic / Red) ** 0.5 - WorldView Legion: 1.2 * (Red_Edge - Green) - 1.5 * (Red - Green) * (Red_Edge / Red) ** 0.5 --- #### TDVI — Transformed Difference Vegetation Index URL: https://docs.geopera.com/spectral-indices/tdvi Category: vegetation Transformed Difference Vegetation Index - A spectral index for vegetation applications. Formula: 1.5 * ((N - R)/((N ** 2.0 + R + 0.5) ** 0.5)) Wavelengths: N (850), R (650) Applications: vegetation References: https://doi.org/10.1109/IGARSS.2002.1026867 Sensor-specific formulas: - BJ3A: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - BJ3N: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - Dragonette-2/3: 1.5 * ((Band30 - Band15)/((Band30 ** 2.0 + Band15 + 0.5) ** 0.5)) - Gaofen-1: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - Gaofen-2: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - GeoEye-1: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - Göktürk-1: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - Jilin-1: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - Jilin-1 GF03D: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - KOMPSAT-3: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - KOMPSAT-3A: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - Landsat 8/9: 1.5 * ((B5 - B4)/((B5 ** 2.0 + B4 + 0.5) ** 0.5)) - NAIP: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - Sentinel-2: 1.5 * ((B8 - B4)/((B8 ** 2.0 + B4 + 0.5) ** 0.5)) - SuperView Neo: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - SuperView-1: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - SuperView-2: 1.5 * ((NIR1 - Red)/((NIR1 ** 2.0 + Red + 0.5) ** 0.5)) - TripleSat: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - WorldView 2: 1.5 * ((NIR1 - Red)/((NIR1 ** 2.0 + Red + 0.5) ** 0.5)) - WorldView 3: 1.5 * ((NIR1 - Red)/((NIR1 ** 2.0 + Red + 0.5) ** 0.5)) - WorldView 4: 1.5 * ((NIR - Red)/((NIR ** 2.0 + Red + 0.5) ** 0.5)) - WorldView Legion: 1.5 * ((NIR1 - Red)/((NIR1 ** 2.0 + Red + 0.5) ** 0.5)) --- #### TGI — Triangular Greenness Index URL: https://docs.geopera.com/spectral-indices/tgi Category: vegetation Triangular Greenness Index - A spectral index for vegetation applications. Formula: - 0.5 * (190 * (R - G) - 120 * (R - B)) Wavelengths: R (650), G (550), B (450) Applications: vegetation References: http://dx.doi.org/10.1016/j.jag.2012.07.020 Sensor-specific formulas: - BJ3A: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - BJ3N: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - Dragonette-2/3: - 0.5 * (190 * (Band15 - Band9) - 120 * (Band15 - Band1)) - Gaofen-1: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - Gaofen-2: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - GeoEye-1: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - Göktürk-1: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - Jilin-1: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - Jilin-1 GF03D: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - KOMPSAT-3: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - KOMPSAT-3A: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - Landsat 8/9: - 0.5 * (190 * (B4 - B3) - 120 * (B4 - B1)) - NAIP: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - Sentinel-2: - 0.5 * (190 * (B4 - B3) - 120 * (B4 - B1)) - SuperView Neo: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - SuperView-1: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - SuperView-2: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - TripleSat: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - WorldView 2: - 0.5 * (190 * (Red - Green) - 120 * (Red - Coastal)) - WorldView 3: - 0.5 * (190 * (Red - Green) - 120 * (Red - Coastal)) - WorldView 4: - 0.5 * (190 * (Red - Green) - 120 * (Red - Blue)) - WorldView Legion: - 0.5 * (190 * (Red - Green) - 120 * (Red - Coastal)) --- #### TriVI — Triangular Vegetation Index URL: https://docs.geopera.com/spectral-indices/trivi Category: vegetation Triangular Vegetation Index - A spectral index for vegetation applications. Formula: 0.5 * (120 * (N - G) - 200 * (R - G)) Wavelengths: N (850), G (550), R (650) Applications: vegetation References: http://dx.doi.org/10.1016/S0034-4257(00)00197-8 Sensor-specific formulas: - BJ3A: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - BJ3N: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - Dragonette-2/3: 0.5 * (120 * (Band30 - Band9) - 200 * (Band15 - Band9)) - Gaofen-1: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - Gaofen-2: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - GeoEye-1: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - Göktürk-1: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - Jilin-1: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - Jilin-1 GF03D: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - KOMPSAT-3: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - KOMPSAT-3A: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - Landsat 8/9: 0.5 * (120 * (B5 - B3) - 200 * (B4 - B3)) - NAIP: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - Sentinel-2: 0.5 * (120 * (B8 - B3) - 200 * (B4 - B3)) - SuperView Neo: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - SuperView-1: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - SuperView-2: 0.5 * (120 * (NIR1 - Green) - 200 * (Red - Green)) - TripleSat: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - WorldView 2: 0.5 * (120 * (NIR1 - Green) - 200 * (Red - Green)) - WorldView 3: 0.5 * (120 * (NIR1 - Green) - 200 * (Red - Green)) - WorldView 4: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - WorldView Legion: 0.5 * (120 * (NIR1 - Green) - 200 * (Red - Green)) --- #### TRRVI — Transformed Red Range Vegetation Index URL: https://docs.geopera.com/spectral-indices/trrvi Category: vegetation Transformed Red Range Vegetation Index - A spectral index for vegetation applications. Formula: ((RE2 - R) / (RE2 + R)) / (((N - R) / (N + R)) + 1.0) Wavelengths: RE2 (740), R (650), N (850) Applications: vegetation References: https://doi.org/10.3390/rs12152359 Sensor-specific formulas: - Dragonette-2/3: ((Band23 - Band15) / (Band23 + Band15)) / (((Band30 - Band15) / (Band30 + Band15)) + 1.0) - Gaofen-1: ((Panchromatic - Red) / (Panchromatic + Red)) / (((NIR - Red) / (NIR + Red)) + 1.0) - Gaofen-2: ((Panchromatic - Red) / (Panchromatic + Red)) / (((NIR - Red) / (NIR + Red)) + 1.0) - GeoEye-1: ((Panchromatic - Red) / (Panchromatic + Red)) / (((NIR - Red) / (NIR + Red)) + 1.0) - Göktürk-1: ((Panchromatic - Red) / (Panchromatic + Red)) / (((NIR - Red) / (NIR + Red)) + 1.0) - Jilin-1 GF03D: ((Panchromatic - Red) / (Panchromatic + Red)) / (((NIR - Red) / (NIR + Red)) + 1.0) - KOMPSAT-3: ((Panchromatic - Red) / (Panchromatic + Red)) / (((NIR - Red) / (NIR + Red)) + 1.0) - KOMPSAT-3A: ((Panchromatic - Red) / (Panchromatic + Red)) / (((NIR - Red) / (NIR + Red)) + 1.0) - Sentinel-2: ((B6 - B4) / (B6 + B4)) / (((B8 - B4) / (B8 + B4)) + 1.0) - SuperView-2: ((Red_Edge - Red) / (Red_Edge + Red)) / (((NIR1 - Red) / (NIR1 + Red)) + 1.0) - WorldView 2: ((Red_Edge - Red) / (Red_Edge + Red)) / (((NIR1 - Red) / (NIR1 + Red)) + 1.0) - WorldView 3: ((Red Edge - Red) / (Red Edge + Red)) / (((NIR1 - Red) / (NIR1 + Red)) + 1.0) - WorldView 4: ((Panchromatic - Red) / (Panchromatic + Red)) / (((NIR - Red) / (NIR + Red)) + 1.0) - WorldView Legion: ((Red_Edge - Red) / (Red_Edge + Red)) / (((NIR1 - Red) / (NIR1 + Red)) + 1.0) --- #### TSAVI — Transformed Soil Adjusted Vegetation Index URL: https://docs.geopera.com/spectral-indices/tsavi Category: vegetation A vegetation index designed to minimize soil brightness effects on vegetation measurements. TSAVI requires knowledge of the soil line parameters (slope and intercept) for optimal performance. Formula: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) Wavelengths: red (640-680), nir (760-900) Applications: Vegetation monitoring in areas with exposed soil, Agricultural crop assessment, Biomass estimation, Leaf Area Index (LAI) measurement, Sparse vegetation mapping References: Baret et al. (1989). TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. Sensor-specific formulas: - BJ3A: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - BJ3N: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - Dragonette-1: (a * (Band 23 - a * Band 12 - b)) / (Band 12 + a * Band 23 - a * b + X * (1 + a^2)) - Dragonette-2/3: (a * (Band29 - a * Band16 - b)) / (Band16 + a * Band29 - a * b + X * (1 + a^2)) - Gaofen-1: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - Gaofen-2: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - GeoEye-1: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - Göktürk-1: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - Jilin-1: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - Jilin-1 GF03D: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - KOMPSAT-3: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - KOMPSAT-3A: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - Landsat 8/9: (a * (B5 - a * B4 - b)) / (B4 + a * B5 - a * b + X * (1 + a^2)) - NAIP: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - Sentinel-2: (a * (B8 - a * B4 - b)) / (B4 + a * B8 - a * b + X * (1 + a^2)) - SuperView Neo: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - SuperView-1: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - SuperView-2: (a * (NIR1 - a * Red - b)) / (Red + a * NIR1 - a * b + X * (1 + a^2)) - TripleSat: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - WorldView 2: (a * (NIR1 - a * Red - b)) / (Red + a * NIR1 - a * b + X * (1 + a^2)) - WorldView 3: (a * (NIR1 - a * Red - b)) / (Red + a * NIR1 - a * b + X * (1 + a^2)) - WorldView 4: (a * (NIR - a * Red - b)) / (Red + a * NIR - a * b + X * (1 + a^2)) - WorldView Legion: (a * (NIR1 - a * Red - b)) / (Red + a * NIR1 - a * b + X * (1 + a^2)) --- #### TTVI — Transformed Triangular Vegetation Index URL: https://docs.geopera.com/spectral-indices/ttvi Category: vegetation Transformed Triangular Vegetation Index - A spectral index for vegetation applications. Formula: 0.5 * ((865.0 - 740.0) * (RE3 - RE2) - (N2 - RE2) * (783.0 - 740)) Wavelengths: RE3 (783), RE2 (740), N2 (860) Applications: vegetation References: https://doi.org/10.3390/rs12010016 Sensor-specific formulas: - Dragonette-2/3: 0.5 * ((865.0 - 740.0) * (Band26 - Band23) - (Band30 - Band23) * (783.0 - 740)) - Sentinel-2: 0.5 * ((865.0 - 740.0) * (B7 - B6) - (B8A - B6) * (783.0 - 740)) --- #### TVI — Transformed Vegetation Index URL: https://docs.geopera.com/spectral-indices/tvi Category: vegetation A simple transformation of NDVI that shifts values to avoid negative numbers. TVI ranges from 0 to 1, making it easier to interpret and use in some applications. Formula: sqrt((NIR - Red) / (NIR + Red) + 0.5) Wavelengths: red (640-680), nir (760-900) Applications: Vegetation mapping, Crop monitoring, Land cover classification, Vegetation change detection References: Deering et al. (1975).; Bannari et al. (1995). A review of vegetation indices.; Hunt Jr. et al. (2011). Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index. Sensor-specific formulas: - BJ3A: sqrt((NIR - Red) / (NIR + Red) + 0.5) - BJ3N: sqrt((NIR - Red) / (NIR + Red) + 0.5) - Dragonette-1: sqrt((Band 23 - Band 12) / (Band 23 + Band 12) + 0.5) - Dragonette-2/3: sqrt((Band29 - Band16) / (Band29 + Band16) + 0.5) - Gaofen-1: sqrt((NIR - Red) / (NIR + Red) + 0.5) - Gaofen-2: sqrt((NIR - Red) / (NIR + Red) + 0.5) - GeoEye-1: sqrt((NIR - Red) / (NIR + Red) + 0.5) - Göktürk-1: sqrt((NIR - Red) / (NIR + Red) + 0.5) - Jilin-1: sqrt((NIR - Red) / (NIR + Red) + 0.5) - Jilin-1 GF03D: sqrt((NIR - Red) / (NIR + Red) + 0.5) - KOMPSAT-3: sqrt((NIR - Red) / (NIR + Red) + 0.5) - KOMPSAT-3A: sqrt((NIR - Red) / (NIR + Red) + 0.5) - Landsat 8/9: sqrt((B5 - B4) / (B5 + B4) + 0.5) - NAIP: sqrt((NIR - Red) / (NIR + Red) + 0.5) - Sentinel-2: sqrt((B8 - B4) / (B8 + B4) + 0.5) - SuperView Neo: sqrt((NIR - Red) / (NIR + Red) + 0.5) - SuperView-1: sqrt((NIR - Red) / (NIR + Red) + 0.5) - SuperView-2: sqrt((NIR1 - Red) / (NIR1 + Red) + 0.5) - TripleSat: sqrt((NIR - Red) / (NIR + Red) + 0.5) - WorldView 2: sqrt((NIR1 - Red) / (NIR1 + Red) + 0.5) - WorldView 3: sqrt((NIR1 - Red) / (NIR1 + Red) + 0.5) - WorldView 4: sqrt((NIR - Red) / (NIR + Red) + 0.5) - WorldView Legion: sqrt((NIR1 - Red) / (NIR1 + Red) + 0.5) --- #### TVI — Triangular Vegetation Index URL: https://docs.geopera.com/spectral-indices/tvi_broge Category: vegetation The Triangular Vegetation Index (TVI) was developed by Broge and Hansen (2000) based on the triangular area formed by green peak, near-infrared shoulder, and chlorophyll absorption minimum. TVI is sensitive to both chlorophyll content and LAI, capturing radiative energy absorbed by pigments and providing improved retrieval accuracy with reduced saturation effects. Formula: 0.5 * (120 * (750nm - 550nm) - 200 * (670nm - 550nm)) Wavelengths: 550 (550), 670 (670), 750 (750) Applications: leaf area index estimation, chlorophyll content assessment, vegetation biomass monitoring, crop yield prediction, canopy structure analysis, reduced saturation effects References: Broge, N.H. and Leblanc, E. (2000) - Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2), 156-172 Sensor-specific formulas: - Dragonette-1: 0.5 * (120 * (Band 20 - Band 5) - 200 * (Band 13 - Band 5)) - Dragonette-2/3: 0.5 * (120 * (Band24 - Band9) - 200 * (Band17 - Band9)) - GeoEye-1: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - Sentinel-2: 0.5 * (120 * (B6 - B3) - 200 * (B4 - B3)) - WorldView 2: 0.5 * (120 * (Red_Edge - Green) - 200 * (Red - Green)) - WorldView 3: 0.5 * (120 * (Red Edge - Green) - 200 * (Red - Green)) - WorldView 4: 0.5 * (120 * (Panchromatic - Green) - 200 * (Red - Green)) - WorldView Legion: 0.5 * (120 * (Red_Edge - Green) - 200 * (Red - Green)) --- #### TVI — Triangular Vegetation Index URL: https://docs.geopera.com/spectral-indices/tvi_triangular Category: vegetation A vegetation index that uses the triangular area formed by green, red, and red-edge reflectance values. It is sensitive to leaf chlorophyll content and is particularly useful for estimating green LAI. Formula: 0.5 * (120 * (RE1 - Green) - 200 * (Red - Green)) Wavelengths: green (550), red (670), re1 (750) Applications: Chlorophyll content estimation, Green LAI prediction, Crop canopy analysis, Vegetation health monitoring, Precision agriculture References: Broge & Leblanc (2000). Comparing prediction power and stability of broadband and hyperspectral vegetation indices.; Haboudane et al. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies. Sensor-specific formulas: - Dragonette-1: 0.5 * (120 * (Band 20 - Band 5) - 200 * (Band 13 - Band 5)) - Dragonette-2/3: 0.5 * (120 * (Band24 - Band9) - 200 * (Band17 - Band9)) - Gaofen-1: 0.5 * (120 * (Panchromatic - Green) - 200 * (Red - Green)) - Gaofen-2: 0.5 * (120 * (Panchromatic - Green) - 200 * (Red - Green)) - GeoEye-1: 0.5 * (120 * (NIR - Green) - 200 * (Red - Green)) - Göktürk-1: 0.5 * (120 * (Panchromatic - Green) - 200 * (Red - Green)) - Jilin-1 GF03D: 0.5 * (120 * (Panchromatic - Green) - 200 * (Red - Green)) - KOMPSAT-3: 0.5 * (120 * (Panchromatic - Green) - 200 * (Red - Green)) - KOMPSAT-3A: 0.5 * (120 * (Panchromatic - Green) - 200 * (Red - Green)) - Sentinel-2: 0.5 * (120 * (B6 - B3) - 200 * (B4 - B3)) - WorldView 2: 0.5 * (120 * (Red_Edge - Green) - 200 * (Red - Green)) - WorldView 3: 0.5 * (120 * (Red Edge - Green) - 200 * (Red - Green)) - WorldView 4: 0.5 * (120 * (Panchromatic - Green) - 200 * (Red - Green)) - WorldView Legion: 0.5 * (120 * (Red_Edge - Green) - 200 * (Red - Green)) --- #### VARI — Visible Atmospherically Resistant Index URL: https://docs.geopera.com/spectral-indices/vari Category: vegetation Visible Atmospherically Resistant Index - A spectral index for vegetation applications. Formula: (G - R) / (G + R - B) Wavelengths: G (550), R (650), B (450) Applications: vegetation References: https://doi.org/10.1016/S0034-4257(01)00289-9 Sensor-specific formulas: - BJ3A: (Green - Red) / (Green + Red - Blue) - BJ3N: (Green - Red) / (Green + Red - Blue) - Dragonette-2/3: (Band9 - Band15) / (Band9 + Band15 - Band1) - Gaofen-1: (Green - Red) / (Green + Red - Blue) - Gaofen-2: (Green - Red) / (Green + Red - Blue) - GeoEye-1: (Green - Red) / (Green + Red - Blue) - Göktürk-1: (Green - Red) / (Green + Red - Blue) - Jilin-1: (Green - Red) / (Green + Red - Blue) - Jilin-1 GF03D: (Green - Red) / (Green + Red - Blue) - KOMPSAT-3: (Green - Red) / (Green + Red - Blue) - KOMPSAT-3A: (Green - Red) / (Green + Red - Blue) - Landsat 8/9: (B3 - B4) / (B3 + B4 - B1) - NAIP: (Green - Red) / (Green + Red - Blue) - Sentinel-2: (B3 - B4) / (B3 + B4 - B1) - SuperView Neo: (Green - Red) / (Green + Red - Blue) - SuperView-1: (Green - Red) / (Green + Red - Blue) - SuperView-2: (Green - Red) / (Green + Red - Blue) - TripleSat: (Green - Red) / (Green + Red - Blue) - WorldView 2: (Green - Red) / (Green + Red - Coastal) - WorldView 3: (Green - Red) / (Green + Red - Coastal) - WorldView 4: (Green - Red) / (Green + Red - Blue) - WorldView Legion: (Green - Red) / (Green + Red - Coastal) --- #### VARI — Visible Atmospherically Resistant Index URL: https://docs.geopera.com/spectral-indices/vari_visible Category: vegetation The Visible Atmospherically Resistant Index (VARI) is designed to emphasize vegetation in the visible portion of the spectrum while mitigating illumination differences and atmospheric effects. It evaluates the 'greenness' in plants using only visible light bands, making it ideal for standard RGB cameras without requiring specialized multispectral sensors. Formula: (Green - Red) / (Green + Red - Blue) Wavelengths: BLUE (450-520), GREEN (520-600), RED (640-760) Applications: vegetation health assessment, greenness evaluation, plant condition monitoring, atmospheric effect mitigation, RGB-based vegetation analysis, drone-based vegetation monitoring, precision agriculture with standard cameras References: Gitelson, A.A., Kaufman, Y.J., Stark, R., and Rundquist, D. (2002) - Novel algorithms for estimation of vegetation fraction. Remote Sensing of Environment, 80, 76-87 Sensor-specific formulas: - BJ3A: (Green - Red) / (Green + Red - Blue) - BJ3N: (Green - Red) / (Green + Red - Blue) - Dragonette-1: (Band 6 - Band 16) / (Band 6 + Band 16 - Band 1) - Dragonette-2/3: (Band10 - Band20) / (Band10 + Band20 - Band4) - Gaofen-1: (Green - Red) / (Green + Red - Blue) - Gaofen-2: (Green - Red) / (Green + Red - Blue) - GeoEye-1: (Green - Red) / (Green + Red - Blue) - Göktürk-1: (Green - Red) / (Green + Red - Blue) - Jilin-1: (Green - Red) / (Green + Red - Blue) - Jilin-1 GF03D: (Green - Red) / (Green + Red - Blue) - KOMPSAT-3: (Green - Red) / (Green + Red - Blue) - KOMPSAT-3A: (Green - Red) / (Green + Red - Blue) - Landsat 8/9: (B3 - B4) / (B3 + B4 - B1) - NAIP: (Green - Red) / (Green + Red - Blue) - Sentinel-2: (B3 - B4) / (B3 + B4 - B1) - SuperView Neo: (Green - Red) / (Green + Red - Blue) - SuperView-1: (Green - Red) / (Green + Red - Blue) - SuperView-2: (Green - Red) / (Green + Red - Blue) - TripleSat: (Green - Red) / (Green + Red - Blue) - WorldView 2: (Green - Red) / (Green + Red - Blue) - WorldView 3: (Green - Red) / (Green + Red - Blue) - WorldView 4: (Green - Red) / (Green + Red - Blue) - WorldView Legion: (Green - Red) / (Green + Red - Blue) --- #### VARI700 — Visible Atmospherically Resistant Index (700 nm) URL: https://docs.geopera.com/spectral-indices/vari700 Category: vegetation Visible Atmospherically Resistant Index (700 nm) - A spectral index for vegetation applications. Formula: (RE1 - 1.7 * R + 0.7 * B) / (RE1 + 1.3 * R - 1.3 * B) Wavelengths: RE1 (705), R (650), B (450) Applications: vegetation References: https://doi.org/10.1016/S0034-4257(01)00289-9 Sensor-specific formulas: - Dragonette-2/3: (Band20 - 1.7 * Band15 + 0.7 * Band1) / (Band20 + 1.3 * Band15 - 1.3 * Band1) - Gaofen-1: (Panchromatic - 1.7 * Red + 0.7 * Blue) / (Panchromatic + 1.3 * Red - 1.3 * Blue) - Gaofen-2: (Panchromatic - 1.7 * Red + 0.7 * Blue) / (Panchromatic + 1.3 * Red - 1.3 * Blue) - Göktürk-1: (Panchromatic - 1.7 * Red + 0.7 * Blue) / (Panchromatic + 1.3 * Red - 1.3 * Blue) - Jilin-1 GF03D: (Panchromatic - 1.7 * Red + 0.7 * Blue) / (Panchromatic + 1.3 * Red - 1.3 * Blue) - KOMPSAT-3: (Panchromatic - 1.7 * Red + 0.7 * Blue) / (Panchromatic + 1.3 * Red - 1.3 * Blue) - KOMPSAT-3A: (Panchromatic - 1.7 * Red + 0.7 * Blue) / (Panchromatic + 1.3 * Red - 1.3 * Blue) - Sentinel-2: (B5 - 1.7 * B4 + 0.7 * B1) / (B5 + 1.3 * B4 - 1.3 * B1) - SuperView-2: (Red_Edge - 1.7 * Red + 0.7 * Blue) / (Red_Edge + 1.3 * Red - 1.3 * Blue) - WorldView 2: (Red_Edge - 1.7 * Red + 0.7 * Coastal) / (Red_Edge + 1.3 * Red - 1.3 * Coastal) - WorldView 3: (Red Edge - 1.7 * Red + 0.7 * Coastal) / (Red Edge + 1.3 * Red - 1.3 * Coastal) - WorldView 4: (Panchromatic - 1.7 * Red + 0.7 * Blue) / (Panchromatic + 1.3 * Red - 1.3 * Blue) - WorldView Legion: (Red_Edge - 1.7 * Red + 0.7 * Coastal) / (Red_Edge + 1.3 * Red - 1.3 * Coastal) --- #### VARIrededge — Visible Atmospherically Resistant Index Red Edge URL: https://docs.geopera.com/spectral-indices/vari_rededge Category: vegetation A red edge variant of VARI that uses red edge bands instead of green. This index is designed to estimate vegetation fraction with reduced atmospheric effects. Formula: (RE1 - Red) / (RE1 + Red) Wavelengths: red (620-680), re1 (700-710) Applications: Vegetation fraction estimation, Red edge position analysis, Atmospheric correction, Vegetation cover mapping, LAI estimation References: Gitelson et al. (2002). Novel algorithms for remote estimation of vegetation fraction.; Gitelson et al. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies.; Ahamed et al. (2011). A review of remote sensing methods for biomass feedstock production. Sensor-specific formulas: - Dragonette-1: (Band 16 - Band 11) / (Band 16 + Band 11) - Dragonette-2/3: (Band20 - Band15) / (Band20 + Band15) - Gaofen-1: (Panchromatic - Red) / (Panchromatic + Red) - Gaofen-2: (Panchromatic - Red) / (Panchromatic + Red) - GeoEye-1: (Panchromatic - Red) / (Panchromatic + Red) - Göktürk-1: (Panchromatic - Red) / (Panchromatic + Red) - Jilin-1 GF03D: (Panchromatic - Red) / (Panchromatic + Red) - KOMPSAT-3: (Panchromatic - Red) / (Panchromatic + Red) - KOMPSAT-3A: (Panchromatic - Red) / (Panchromatic + Red) - Sentinel-2: (B5 - B4) / (B5 + B4) - SuperView-2: (Red_Edge - Red) / (Red_Edge + Red) - WorldView 2: (Red_Edge - Red) / (Red_Edge + Red) - WorldView 3: (Red Edge - Red) / (Red Edge + Red) - WorldView 4: (Panchromatic - Red) / (Panchromatic + Red) - WorldView Legion: (Red_Edge - Red) / (Red_Edge + Red) --- #### VI700 — Vegetation Index (700 nm) URL: https://docs.geopera.com/spectral-indices/vi700 Category: vegetation Vegetation Index (700 nm) - A spectral index for vegetation applications. Formula: (RE1 - R) / (RE1 + R) Wavelengths: RE1 (705), R (650) Applications: vegetation References: https://doi.org/10.1016/S0034-4257(01)00289-9 Sensor-specific formulas: - Dragonette-1: (Band 16 - Band 11) / (Band 16 + Band 11) - Dragonette-2/3: (Band20 - Band15) / (Band20 + Band15) - Gaofen-1: (Panchromatic - Red) / (Panchromatic + Red) - Gaofen-2: (Panchromatic - Red) / (Panchromatic + Red) - Göktürk-1: (Panchromatic - Red) / (Panchromatic + Red) - Jilin-1 GF03D: (Panchromatic - Red) / (Panchromatic + Red) - KOMPSAT-3: (Panchromatic - Red) / (Panchromatic + Red) - KOMPSAT-3A: (Panchromatic - Red) / (Panchromatic + Red) - Sentinel-2: (B5 - B4) / (B5 + B4) - SuperView-2: (Red_Edge - Red) / (Red_Edge + Red) - WorldView 2: (Red_Edge - Red) / (Red_Edge + Red) - WorldView 3: (Red Edge - Red) / (Red Edge + Red) - WorldView 4: (Panchromatic - Red) / (Panchromatic + Red) - WorldView Legion: (Red_Edge - Red) / (Red_Edge + Red) --- #### VIG — Vegetation Index Green URL: https://docs.geopera.com/spectral-indices/vig Category: vegetation Vegetation Index Green - A spectral index for vegetation applications. Formula: (G - R) / (G + R) Wavelengths: G (550), R (650) Applications: vegetation References: https://doi.org/10.1016/S0034-4257(01)00289-9 Sensor-specific formulas: - BJ3A: (Green - Red) / (Green + Red) - BJ3N: (Green - Red) / (Green + Red) - Dragonette-1: (Band 5 - Band 11) / (Band 5 + Band 11) - Dragonette-2/3: (Band9 - Band15) / (Band9 + Band15) - Gaofen-1: (Green - Red) / (Green + Red) - Gaofen-2: (Green - Red) / (Green + Red) - GeoEye-1: (Green - Red) / (Green + Red) - Göktürk-1: (Green - Red) / (Green + Red) - Jilin-1: (Green - Red) / (Green + Red) - Jilin-1 GF03D: (Green - Red) / (Green + Red) - KOMPSAT-3: (Green - Red) / (Green + Red) - KOMPSAT-3A: (Green - Red) / (Green + Red) - Landsat 8/9: (B3 - B4) / (B3 + B4) - NAIP: (Green - Red) / (Green + Red) - Sentinel-2: (B3 - B4) / (B3 + B4) - SuperView Neo: (Green - Red) / (Green + Red) - SuperView-1: (Green - Red) / (Green + Red) - SuperView-2: (Green - Red) / (Green + Red) - TripleSat: (Green - Red) / (Green + Red) - WorldView 2: (Green - Red) / (Green + Red) - WorldView 3: (Green - Red) / (Green + Red) - WorldView 4: (Green - Red) / (Green + Red) - WorldView Legion: (Green - Red) / (Green + Red) --- #### VOG1 — Vogelmann Red Edge Index 1 URL: https://docs.geopera.com/spectral-indices/vog1 Category: vegetation A hyperspectral vegetation index that uses the red edge spectral region to assess vegetation health and chlorophyll content. It is particularly sensitive to vegetation stress and early detection of plant diseases. Formula: RE2 / RE1 Wavelengths: re1 (720), re2 (740) Applications: Vegetation stress detection, Chlorophyll content assessment, Early disease detection, Forest health monitoring, Precision agriculture References: Vogelmann et al. (1993). Red Edge Spectral Measurements from Sugar Maple Leaves.; le Maire et al. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Sensor-specific formulas: - Dragonette-1: Band 19 / Band 18 - Dragonette-2/3: Band23 / Band22 - Sentinel-2: B6 / B5 --- #### VOG2 — Vogelmann Red Edge Index 2 URL: https://docs.geopera.com/spectral-indices/vog2 Category: vegetation A vegetation index that uses specific red edge wavelengths to assess vegetation health and chlorophyll content. It is sensitive to changes in leaf internal structure and chlorophyll concentration. Formula: (re2_734 - re2_747) / (re1_715 + re1_726) Wavelengths: re1_715 (715), re1_726 (726), re2_734 (734), re2_747 (747) Applications: Vegetation stress detection, Chlorophyll content assessment, Forest health monitoring, Early disease detection, Precision agriculture References: Vogelmann et al. (1993). Red Edge Spectral Measurements from Sugar Maple Leaves.; Gitelson et al. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Sensor-specific formulas: - Dragonette-1: (Band 19 - Band 20) / (Band 17 + Band 18) - Dragonette-2/3: (Band23 - Band24) / (Band21 + Band22) --- #### VOG3 — Vogelmann Red Edge Index 3 URL: https://docs.geopera.com/spectral-indices/vog3 Category: vegetation A simple ratio vegetation index using red edge wavelengths to assess vegetation health and chlorophyll content. The ratio of 715nm to 705nm provides information about the red edge position and slope. Formula: re1_715 / re1_705 Wavelengths: re1_705 (705), re1_715 (715) Applications: Chlorophyll content estimation, Vegetation stress detection, Leaf area index estimation, Forest canopy analysis, Crop health monitoring References: Vogelmann et al. (1993). Red Edge Spectral Measurements from Sugar Maple Leaves.; le Maire et al. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Sensor-specific formulas: - Dragonette-1: Band 17 / Band 16 - Dragonette-2/3: Band21 / Band20 - GeoEye-1: Panchromatic / Red --- #### WDRVI — Wide Dynamic Range Vegetation Index URL: https://docs.geopera.com/spectral-indices/wdrvi Category: vegetation A vegetation index designed to improve sensitivity for moderate to high biomass conditions where traditional NDVI saturates. The weighting factor (0.1) enhances the dynamic range of the vegetation signal. Formula: (0.1 * NIR - Red) / (0.1 * NIR + Red) Wavelengths: red (640-680), nir (780-900) Applications: High biomass vegetation monitoring, LAI estimation in dense canopies, Agricultural crop assessment, Forest biomass quantification, Vegetation phenology tracking References: Gitelson (2004). Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation.; Ahamed et al. (2011). A review of remote sensing methods for biomass feedstock production. Sensor-specific formulas: - BJ3A: (0.1 * NIR - Red) / (0.1 * NIR + Red) - BJ3N: (0.1 * NIR - Red) / (0.1 * NIR + Red) - Dragonette-1: (0.1 * Band 23 - Band 12) / (0.1 * Band 23 + Band 12) - Dragonette-2/3: (0.1 * Band29 - Band16) / (0.1 * Band29 + Band16) - Gaofen-1: (0.1 * NIR - Red) / (0.1 * NIR + Red) - Gaofen-2: (0.1 * NIR - Red) / (0.1 * NIR + Red) - GeoEye-1: (0.1 * NIR - Red) / (0.1 * NIR + Red) - Göktürk-1: (0.1 * NIR - Red) / (0.1 * NIR + Red) - Jilin-1: (0.1 * NIR - Red) / (0.1 * NIR + Red) - Jilin-1 GF03D: (0.1 * NIR - Red) / (0.1 * NIR + Red) - KOMPSAT-3: (0.1 * NIR - Red) / (0.1 * NIR + Red) - KOMPSAT-3A: (0.1 * NIR - Red) / (0.1 * NIR + Red) - Landsat 8/9: (0.1 * B5 - B4) / (0.1 * B5 + B4) - NAIP: (0.1 * NIR - Red) / (0.1 * NIR + Red) - Sentinel-2: (0.1 * B8 - B4) / (0.1 * B8 + B4) - SuperView Neo: (0.1 * NIR - Red) / (0.1 * NIR + Red) - SuperView-1: (0.1 * NIR - Red) / (0.1 * NIR + Red) - SuperView-2: (0.1 * NIR1 - Red) / (0.1 * NIR1 + Red) - TripleSat: (0.1 * NIR - Red) / (0.1 * NIR + Red) - WorldView 2: (0.1 * NIR1 - Red) / (0.1 * NIR1 + Red) - WorldView 3: (0.1 * NIR1 - Red) / (0.1 * NIR1 + Red) - WorldView 4: (0.1 * NIR - Red) / (0.1 * NIR + Red) - WorldView Legion: (0.1 * NIR1 - Red) / (0.1 * NIR1 + Red) --- #### WDVI — Weighted Difference Vegetation Index URL: https://docs.geopera.com/spectral-indices/wdvi Category: vegetation A vegetation index that accounts for soil background by using a weighted difference between NIR and red bands. The weight parameter 'a' is the slope of the soil line, typically derived from bare soil measurements. Formula: NIR - a * Red Wavelengths: red (640-680), nir (780-900) Applications: Vegetation monitoring with soil correction, LAI estimation, Crop growth monitoring, Agricultural applications, Sparse vegetation mapping References: Clevers (1989). Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture.; Baret & Guyot (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Sensor-specific formulas: - BJ3A: NIR - a * Red - BJ3N: NIR - a * Red - Dragonette-1: Band 23 - a * Band 12 - Dragonette-2/3: Band29 - a * Band16 - Gaofen-1: NIR - a * Red - Gaofen-2: NIR - a * Red - GeoEye-1: NIR - a * Red - Göktürk-1: NIR - a * Red - Jilin-1: NIR - a * Red - Jilin-1 GF03D: NIR - a * Red - KOMPSAT-3: NIR - a * Red - KOMPSAT-3A: NIR - a * Red - Landsat 8/9: B5 - a * B4 - NAIP: NIR - a * Red - Sentinel-2: B8 - a * B4 - SuperView Neo: NIR - a * Red - SuperView-1: NIR - a * Red - SuperView-2: NIR1 - a * Red - TripleSat: NIR - a * Red - WorldView 2: NIR1 - a * Red - WorldView 3: NIR1 - a * Red - WorldView 4: NIR - a * Red - WorldView Legion: NIR1 - a * Red --- ### Water Category (20 indices) #### LSWI — Land Surface Water Index URL: https://docs.geopera.com/spectral-indices/lswi Category: water The Land Surface Water Index (LSWI) was developed by Xiao et al. to monitor vegetation and soil water content. LSWI is sensitive to liquid water in vegetation due to strong SWIR absorption by water. It is widely used for drought monitoring, water stress detection, and integration into vegetation productivity models. Formula: (NIR - SWIR) / (NIR + SWIR) Wavelengths: NIR (841-876), SWIR (1628-1652) Applications: vegetation water content monitoring, drought detection, water stress assessment, flash drought identification, irrigation mapping, gross primary production modeling, ecosystem water status References: Xiao, X.M., Boles, S., Liu, J.Y., Zhuang, D.F., and Liu, M.L. (2002) - Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data; Xiao, X.M., Hollinger, D., Aber, J., Goltz, M., Davidson, E.A., Zhang, Q.Y., and Moore, B. (2004) - Satellite-based modeling of gross primary production in an evergreen needleleaf forest Sensor-specific formulas: - Landsat 8/9: (B5 - B6) / (B5 + B6) - SuperView-2: (NIR1 - SWIR) / (NIR1 + SWIR) - WorldView 3: (NIR1 - SWIR3) / (NIR1 + SWIR3) --- #### LWVI-1 — Leaf Water Vegetation Index 1 URL: https://docs.geopera.com/spectral-indices/lwvi1 Category: water An index designed to estimate leaf water content using NIR wavelengths. This NDWI variant is particularly sensitive to changes in leaf water status and can help monitor plant water stress. Formula: (nir_1094 - nir_893) / (nir_1094 + nir_893) Wavelengths: nir_893 (893), nir_1094 (1094) Applications: Leaf water content estimation, Plant water stress monitoring, Drought assessment, Crop water management, Vegetation health monitoring References: Galvão et al. (2005). Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. --- #### LWVI-2 — Leaf Water Vegetation Index 2 URL: https://docs.geopera.com/spectral-indices/lwvi2 Category: water An index for estimating leaf water content using specific NIR and SWIR wavelengths. This NDWI variant exploits water absorption features around 1200nm to assess vegetation water status. Formula: (nir_1094 - swir_1205) / (nir_1094 + swir_1205) Wavelengths: nir_1094 (1094), swir_1205 (1205) Applications: Leaf water content estimation, Vegetation water stress detection, Agricultural water management, Drought monitoring, Crop health assessment References: Galvão et al. (2005). Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. --- #### MNDWI — Modified Normalized Difference Water Index URL: https://docs.geopera.com/spectral-indices/mndwi Category: water The Modified Normalized Difference Water Index (MNDWI) was developed by Xu (2006) as an improvement over the original NDWI. By substituting the NIR band with SWIR, MNDWI can enhance open water features while efficiently suppressing noise from built-up areas, vegetation, and soil. This makes it particularly suitable for water detection in urban environments. Formula: (Green - SWIR) / (Green + SWIR) Wavelengths: Green (520-600), SWIR (1550-1750) Applications: open water feature detection, water body mapping, urban water monitoring, flood detection, wetland mapping, surface water change detection, reservoir monitoring References: Xu, H. (2006) - Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing, 27(14), 3025-3033 Sensor-specific formulas: - Landsat 8/9: (B3 - B6) / (B3 + B6) - Sentinel-2: (B3 - B11) / (B3 + B11) - SuperView-2: (Green - SWIR) / (Green + SWIR) - WorldView 3: (Green - SWIR2) / (Green + SWIR2) --- #### NDMI — Normalized Difference Moisture Index URL: https://docs.geopera.com/spectral-indices/ndmi2 Category: water The Normalized Difference Moisture Index (NDMI) was utilized by Wilson and Sader (2002) to detect moisture levels in vegetation. NDMI is sensitive to vegetation water content using NIR and SWIR bands, making it useful for drought monitoring, water stress detection, and forest disturbance mapping. Formula: (NIR - SWIR1) / (NIR + SWIR1) Wavelengths: NIR (780-1400), SWIR1 (1550-1750) Applications: vegetation water content monitoring, drought assessment, water stress detection, forest disturbance mapping, fire fuel level monitoring, crop moisture assessment, waterlogging detection References: Wilson, E.H. and Sader, S.A. (2002) - Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80, 385-396 Sensor-specific formulas: - Landsat 8/9: (B5 - B6) / (B5 + B6) - Sentinel-2: (B8 - B11) / (B8 + B11) - SuperView-2: (NIR1 - SWIR) / (NIR1 + SWIR) - WorldView 3: (NIR1 - SWIR2) / (NIR1 + SWIR2) --- #### NDWI — Normalized Difference Water Index URL: https://docs.geopera.com/spectral-indices/ndwi Category: water Used to detect water bodies and monitor water content in vegetation. Positive values typically indicate water presence. Formula: (Green - NIR) / (Green + NIR) Wavelengths: Green (510-580 nm), NIR (770-900 nm) Applications: Water Body Mapping, Flood Monitoring, Wetland Assessment, Drought Studies References: McFeeters (1996) Sensor-specific formulas: - BJ3A: (Green - NIR) / (Green + NIR) - BJ3N: (Green - NIR) / (Green + NIR) - Dragonette-1: (Band 2 - Band 22) / (Band 2 + Band 22) - Dragonette-2/3: (Band7 - Band26) / (Band7 + Band26) - Gaofen-1: (Green - NIR) / (Green + NIR) - Gaofen-2: (Green - NIR) / (Green + NIR) - GeoEye-1: (Green - NIR) / (Green + NIR) - Göktürk-1: (Green - NIR) / (Green + NIR) - Jilin-1: (Green - NIR) / (Green + NIR) - Jilin-1 GF03D: (Green - NIR) / (Green + NIR) - KOMPSAT-3: (Green - NIR) / (Green + NIR) - KOMPSAT-3A: (Green - NIR) / (Green + NIR) - Landsat 8/9: (B3 - B5) / (B3 + B5) - NAIP: (Green - NIR) / (Green + NIR) - Sentinel-2: (B3 - B8) / (B3 + B8) - SuperView Neo: (Green - NIR) / (Green + NIR) - SuperView-1: (Green - NIR) / (Green + NIR) - SuperView-2: (Green - NIR1) / (Green + NIR1) - TripleSat: (Green - NIR) / (Green + NIR) - WorldView 2: (Green - NIR1) / (Green + NIR1) - WorldView 3: (Green - NIR1) / (Green + NIR1) - WorldView 4: (Green - NIR) / (Green + NIR) - WorldView Legion: (Green - NIR1) / (Green + NIR1) --- #### NDWI — Normalized Difference Water Index (McFeeters) URL: https://docs.geopera.com/spectral-indices/ndwi_mcfeeters Category: water The Normalized Difference Water Index (NDWI) proposed by McFeeters (1996) is designed to delineate open water features and enhance their presence in remotely-sensed digital imagery. It uses reflected near-infrared radiation and visible green light to enhance water features while eliminating soil and terrestrial vegetation features. Formula: (Green - NIR) / (Green + NIR) Wavelengths: Green (560), NIR (830) Applications: open water feature detection, water body mapping, water content monitoring, turbidity estimation, wetland mapping, flood monitoring, coastal zone management References: McFeeters, S.K. (1996) - The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432 Sensor-specific formulas: - BJ3A: (Green - NIR) / (Green + NIR) - BJ3N: (Green - NIR) / (Green + NIR) - Dragonette-2/3: (Band10 - Band29) / (Band10 + Band29) - Gaofen-1: (Green - NIR) / (Green + NIR) - Gaofen-2: (Green - NIR) / (Green + NIR) - GeoEye-1: (Green - NIR) / (Green + NIR) - Göktürk-1: (Green - NIR) / (Green + NIR) - Jilin-1: (Green - NIR) / (Green + NIR) - Jilin-1 GF03D: (Green - NIR) / (Green + NIR) - KOMPSAT-3: (Green - NIR) / (Green + NIR) - KOMPSAT-3A: (Green - NIR) / (Green + NIR) - NAIP: (Green - NIR) / (Green + NIR) - Sentinel-2: (B3 - B8) / (B3 + B8) - SuperView Neo: (Green - NIR) / (Green + NIR) - SuperView-1: (Green - NIR) / (Green + NIR) - SuperView-2: (Green - NIR1) / (Green + NIR1) - TripleSat: (Green - NIR) / (Green + NIR) - WorldView 2: (Green - NIR1) / (Green + NIR1) - WorldView 3: (Green - NIR1) / (Green + NIR1) - WorldView 4: (Green - NIR) / (Green + NIR) - WorldView Legion: (Green - NIR1) / (Green + NIR1) --- #### NWI — New Water Index URL: https://docs.geopera.com/spectral-indices/nwi Category: water New Water Index for water applications Formula: (B - (N + S1 + S2))/(B + (N + S1 + S2)) Wavelengths: B (450-520 nm), N (770-900 nm), S1 (1550-1750 nm), S2 (2080-2350 nm) Applications: Water References: https://doi.org/10.11873/j.issn.1004-0323.2009.2.167 Sensor-specific formulas: - Landsat 8/9: (B2 - (B5 + B6 + B7))/(B2 + (B5 + B6 + B7)) - Sentinel-2: (B2 - (B8 + B11 + B12))/(B2 + (B8 + B11 + B12)) - WorldView 3: (Blue - (NIR1 + SWIR3 + SWIR6))/(Blue + (NIR1 + SWIR3 + SWIR6)) --- #### OSI — Oil Spill Index URL: https://docs.geopera.com/spectral-indices/osi Category: water Oil Spill Index for water applications Formula: (G + R)/B Wavelengths: G (520-600 nm), R (630-690 nm), B (450-520 nm) Applications: Water References: https://doi.org/10.1016/j.mex.2021.101327 Sensor-specific formulas: - BJ3A: (Green + Red)/Blue - BJ3N: (Green + Red)/Blue - Dragonette-1: (Band 6 + Band 12)/Band 1 - Dragonette-2/3: (Band10 + Band16)/Band4 - Gaofen-1: (Green + Red)/Blue - Gaofen-2: (Green + Red)/Blue - GeoEye-1: (Green + Red)/Blue - Göktürk-1: (Green + Red)/Blue - Jilin-1: (Green + Red)/Blue - Jilin-1 GF03D: (Green + Red)/Blue - KOMPSAT-3: (Green + Red)/Blue - KOMPSAT-3A: (Green + Red)/Blue - Landsat 8/9: (B3 + B4)/B2 - NAIP: (Green + Red)/Blue - Sentinel-2: (B3 + B4)/B2 - SuperView Neo: (Green + Red)/Blue - SuperView-1: (Green + Red)/Blue - SuperView-2: (Green + Red)/Blue - TripleSat: (Green + Red)/Blue - WorldView 2: (Green + Red)/Blue - WorldView 3: (Green + Red)/Blue - WorldView 4: (Green + Red)/Blue - WorldView Legion: (Green + Red)/Blue --- #### PI — Plastic Index URL: https://docs.geopera.com/spectral-indices/pi Category: water Plastic Index for water applications Formula: N/(N + R) Wavelengths: N (770-900 nm), R (630-690 nm) Applications: Water References: https://doi.org/10.3390/rs12162648 Sensor-specific formulas: - BJ3A: NIR/(NIR + Red) - BJ3N: NIR/(NIR + Red) - Dragonette-1: Band 23/(Band 23 + Band 12) - Dragonette-2/3: Band29/(Band29 + Band16) - Gaofen-1: NIR/(NIR + Red) - Gaofen-2: NIR/(NIR + Red) - GeoEye-1: NIR/(NIR + Red) - Göktürk-1: NIR/(NIR + Red) - Jilin-1: NIR/(NIR + Red) - Jilin-1 GF03D: NIR/(NIR + Red) - KOMPSAT-3: NIR/(NIR + Red) - KOMPSAT-3A: NIR/(NIR + Red) - Landsat 8/9: B5/(B5 + B4) - NAIP: NIR/(NIR + Red) - Sentinel-2: B8/(B8 + B4) - SuperView Neo: NIR/(NIR + Red) - SuperView-1: NIR/(NIR + Red) - SuperView-2: NIR1/(NIR1 + Red) - TripleSat: NIR/(NIR + Red) - WorldView 2: NIR1/(NIR1 + Red) - WorldView 3: NIR1/(NIR1 + Red) - WorldView 4: NIR/(NIR + Red) - WorldView Legion: NIR1/(NIR1 + Red) --- #### PWI — Plant Water Index URL: https://docs.geopera.com/spectral-indices/pwi Category: water A water stress index that quantifies relative water content at the leaf level. The ratio of 970nm to 900nm reflectance is sensitive to water absorption features and provides information about plant water status. Formula: nir_970 / nir_900 Wavelengths: nir_900 (900), nir_970 (970) Applications: Plant water content assessment, Water stress detection, Drought monitoring, Irrigation management, Crop water status evaluation References: Peñuelas et al. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status.; Datt (1999). Remote Sensing of Water Content in Eucalyptus Leaves.; Ceccato et al. (2002). Designing a spectral index to estimate vegetation water content from remote sensing data. Sensor-specific formulas: - SuperView-2: NIR2 / NIR1 - WorldView 2: NIR2 / NIR1 - WorldView 3: NIR2 / NIR1 - WorldView Legion: NIR2 / NIR1 --- #### RNDVI — Reversed Normalized Difference Vegetation Index URL: https://docs.geopera.com/spectral-indices/rndvi Category: water Reversed Normalized Difference Vegetation Index for water applications Formula: (R - N)/(R + N) Wavelengths: R (630-690 nm), N (770-900 nm) Applications: Water References: https://doi.org/10.3390/rs12162648 Sensor-specific formulas: - BJ3A: (Red - NIR)/(Red + NIR) - BJ3N: (Red - NIR)/(Red + NIR) - Dragonette-1: (Band 12 - Band 23)/(Band 12 + Band 23) - Dragonette-2/3: (Band16 - Band29)/(Band16 + Band29) - Gaofen-1: (Red - NIR)/(Red + NIR) - Gaofen-2: (Red - NIR)/(Red + NIR) - GeoEye-1: (Red - NIR)/(Red + NIR) - Göktürk-1: (Red - NIR)/(Red + NIR) - Jilin-1: (Red - NIR)/(Red + NIR) - Jilin-1 GF03D: (Red - NIR)/(Red + NIR) - KOMPSAT-3: (Red - NIR)/(Red + NIR) - KOMPSAT-3A: (Red - NIR)/(Red + NIR) - Landsat 8/9: (B4 - B5)/(B4 + B5) - NAIP: (Red - NIR)/(Red + NIR) - Sentinel-2: (B4 - B8)/(B4 + B8) - SuperView Neo: (Red - NIR)/(Red + NIR) - SuperView-1: (Red - NIR)/(Red + NIR) - SuperView-2: (Red - NIR1)/(Red + NIR1) - TripleSat: (Red - NIR)/(Red + NIR) - WorldView 2: (Red - NIR1)/(Red + NIR1) - WorldView 3: (Red - NIR1)/(Red + NIR1) - WorldView 4: (Red - NIR)/(Red + NIR) - WorldView Legion: (Red - NIR1)/(Red + NIR1) --- #### S2WI — Sentinel-2 Water Index URL: https://docs.geopera.com/spectral-indices/s2wi Category: water Sentinel-2 Water Index for water applications Formula: (RE1 - S2)/(RE1 + S2) Wavelengths: RE1 (700-710 nm), S2 (2080-2350 nm) Applications: Water References: https://doi.org/10.3390/w13121647 Sensor-specific formulas: - Sentinel-2: (B5 - B12)/(B5 + B12) - WorldView 3: (Red Edge - SWIR6)/(Red Edge + SWIR6) --- #### SWM — Sentinel Water Mask URL: https://docs.geopera.com/spectral-indices/swm Category: water Sentinel Water Mask for water applications Formula: (B + G)/(N + S1) Wavelengths: B (450-520 nm), G (520-600 nm), N (770-900 nm), S1 (1550-1750 nm) Applications: Water References: https://eoscience.esa.int/landtraining2017/files/posters/MILCZAREK.pdf Sensor-specific formulas: - Landsat 8/9: (B2 + B3)/(B5 + B6) - Sentinel-2: (B2 + B3)/(B8 + B11) - SuperView-2: (Blue + Green)/(NIR1 + SWIR) - WorldView 3: (Blue + Green)/(NIR1 + SWIR3) --- #### TWI — Triangle Water Index URL: https://docs.geopera.com/spectral-indices/twi Category: water Triangle Water Index - A spectral index for water applications. Formula: (2.84 * (RE1 - RE2) / (G + S2)) + ((1.25 * (G - B) - (N - B)) / (N + 1.25 * G - 0.25 * B)) Wavelengths: RE1 (705), RE2 (740), G (550), S2 (2190), B (450), N (850) Applications: water References: https://doi.org/10.3390/rs14215289 Sensor-specific formulas: - Sentinel-2: (2.84 * (B5 - B6) / (B3 + B12)) + ((1.25 * (B3 - B1) - (B8 - B1)) / (B8 + 1.25 * B3 - 0.25 * B1)) --- #### WET — Tasselled Cap - wetness URL: https://docs.geopera.com/spectral-indices/wet_tc Category: water The wetness component of the Tasselled Cap transformation, which relates to soil and canopy moisture. Positive values indicate wet conditions while negative values indicate dry conditions. Formula: 0.1509 * Blue + 0.1973 * Green + 0.3279 * Red + 0.3406 * NIR - 0.7112 * SWIR1 - 0.4572 * SWIR2 Wavelengths: blue (450-520), green (520-600), red (630-690), nir (760-900), swir1 (1550-1750), swir2 (2080-2350) Applications: Soil moisture assessment, Wetland mapping, Irrigation monitoring, Drought detection, Vegetation water stress analysis References: Bannari et al. (1995). A review of vegetation indices.; Crist & Cicone (1984). A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap. Sensor-specific formulas: - Landsat 8/9: 0.1509 * B1 + 0.1973 * B3 + 0.3279 * B4 + 0.3406 * B5 - 0.7112 * B6 - 0.4572 * B7 - Sentinel-2: 0.1509 * B1 + 0.1973 * B3 + 0.3279 * B4 + 0.3406 * B8 - 0.7112 * B11 - 0.4572 * B12 - WorldView 3: 0.1509 * Blue + 0.1973 * Green + 0.3279 * Red + 0.3406 * NIR1 - 0.7112 * SWIR2 - 0.4572 * SWIR6 --- #### WI1 — Water Index 1 URL: https://docs.geopera.com/spectral-indices/wi1 Category: water Water Index 1 - A spectral index for water applications. Formula: (G - S2) / (G + S2) Wavelengths: G (550), S2 (2190) Applications: water References: https://doi.org/10.3390/rs11182186 Sensor-specific formulas: - Landsat 8/9: (B3 - B7) / (B3 + B7) - Sentinel-2: (B3 - B12) / (B3 + B12) - WorldView 3: (Green - SWIR6) / (Green + SWIR6) --- #### WI2 — Water Index 2 URL: https://docs.geopera.com/spectral-indices/wi2 Category: water Water Index 2 - A spectral index for water applications. Formula: (B - S2) / (B + S2) Wavelengths: B (450), S2 (2190) Applications: water References: https://doi.org/10.3390/rs11182186 Sensor-specific formulas: - Landsat 8/9: (B1 - B7) / (B1 + B7) - Sentinel-2: (B1 - B12) / (B1 + B12) - WorldView 3: (Coastal - SWIR6) / (Coastal + SWIR6) --- #### WI2015 — Water Index 2015 URL: https://docs.geopera.com/spectral-indices/wi2015 Category: water Water Index 2015 - A spectral index for water applications. Formula: 1.7204 + 171 * G + 3 * R - 70 * N - 45 * S1 - 71 * S2 Wavelengths: G (550), R (650), N (850), S1 (1610), S2 (2190) Applications: water References: https://doi.org/10.1016/j.rse.2015.12.055 Sensor-specific formulas: - Landsat 8/9: 1.7204 + 171 * B3 + 3 * B4 - 70 * B5 - 45 * B6 - 71 * B7 - Sentinel-2: 1.7204 + 171 * B3 + 3 * B4 - 70 * B8 - 45 * B11 - 71 * B12 --- #### WRI — Water Ratio Index URL: https://docs.geopera.com/spectral-indices/wri Category: water Water Ratio Index - A spectral index for water applications. Formula: (G + R)/(N + S1) Wavelengths: G (550), R (650), N (850), S1 (1610) Applications: water References: https://doi.org/10.1109/GEOINFORMATICS.2010.5567762 Sensor-specific formulas: - Landsat 8/9: (B3 + B4)/(B5 + B6) - Sentinel-2: (B3 + B4)/(B8 + B11) - SuperView-2: (Green + Red)/(NIR1 + SWIR) --- ### Geology Category (19 indices) #### AKP — Alunite/Kaolinite/Pyrophylite Index URL: https://docs.geopera.com/spectral-indices/akp Category: geology Geological index designed to identify alunite, kaolinite, and pyrophylite minerals. These clay minerals are indicators of hydrothermal alteration and are valuable for mineral exploration and geological mapping. Formula: (SWIR1 + SWIR3) / SWIR2 Wavelengths: Applications: Mineral Exploration, Hydrothermal Alteration Mapping, Clay Mineral Detection, Geological Surveys, Iron Ore Detection References: Rowan & Mars (2003) --- #### ALT — Alteration Index URL: https://docs.geopera.com/spectral-indices/alteration Category: geology Geological index used to identify areas of hydrothermal alteration and mineral deposits. The ratio highlights areas where clay minerals and hydroxyl-bearing minerals are present, indicating potential alteration zones. Formula: SWIR3 / SWIR5 Wavelengths: SWIR3 (1640-1680 nm), SWIR5 (2145-2185 nm) Applications: Mineral Exploration, Hydrothermal Alteration Mapping, Geological Surveys, Iron Ore Detection References: Volesky et al. (2003) Sensor-specific formulas: - Landsat 8/9: B6 / B7 - WorldView 3: SWIR3 / SWIR5 --- #### AMP — Amphibole Index URL: https://docs.geopera.com/spectral-indices/amphibole Category: geology Geological index for detecting amphibole minerals in rocks. Amphiboles are important rock-forming minerals that provide insights into geological processes and mineral composition. Formula: SWIR1 / SWIR2 Wavelengths: Applications: Mineral Exploration, Geological Mapping, Rock Classification, Iron Ore Detection References: ASTER sensor applications --- #### Clay — Clay Index URL: https://docs.geopera.com/spectral-indices/clay Category: geology Geological index for detecting clay minerals in rocks and soils. Particularly effective for identifying clay-rich areas and alteration zones associated with hydrothermal processes. Formula: (SWIR1 * SWIR3) / (SWIR2 * SWIR2) Wavelengths: Applications: Mineral Exploration, Clay Mineral Detection, Geological Mapping, Iron Ore Detection, Soil Analysis References: Kalinowski & Oliver (2004) --- #### DOL — Dolomite Index URL: https://docs.geopera.com/spectral-indices/dolomite Category: geology Geological index for detecting dolomite minerals in rocks and carbonate formations. Particularly useful for lithologic mapping and identifying carbonate rocks. Formula: (SWIR1 + SWIR3) / SWIR2 Wavelengths: Applications: Geology, Lithologic Mapping, Carbonate Rock Detection, Iron Ore Detection, Mineral Exploration References: Rowan & Mars (2003) --- #### ECA — Epidote/Chlorite/Amphibole Index URL: https://docs.geopera.com/spectral-indices/epidote_chlorite_amphibole Category: geology Geological index for detecting epidote, chlorite, and amphibole minerals in rocks. These minerals are indicators of metamorphic processes and hydrothermal alteration, making this index valuable for geological mapping and mineral exploration. Formula: (SWIR1 + SWIR4) / (SWIR2 + SWIR3) Wavelengths: Applications: Geology, Mineral Exploration, Metamorphic Rock Detection, Iron Ore Detection, Lithologic Mapping References: ASTER sensor applications --- #### Fe3+ — Ferric Iron Index URL: https://docs.geopera.com/spectral-indices/fe3_iron Category: geology Geological index for detecting ferric iron (Fe3+) concentrations in rocks and soils. Useful for lithologic mapping and identifying iron-rich mineral formations. Formula: Red / Green Wavelengths: Green (520-600 nm), Red (630-690 nm) Applications: Geology, Mining, Heavy Metal Detection, Lithologic Mapping References: Rowan & Mars (2003) Sensor-specific formulas: - BJ3A: Red / Green - BJ3N: Red / Green - Dragonette-1: Band 10 / Band 4 - Dragonette-2/3: Band14 / Band8 - Gaofen-1: Red / Green - Gaofen-2: Red / Green - GeoEye-1: Red / Green - Göktürk-1: Red / Green - Jilin-1: Red / Green - Jilin-1 GF03D: Red / Green - KOMPSAT-3: Red / Green - KOMPSAT-3A: Red / Green - Landsat 8/9: B4 / B3 - NAIP: Red / Green - Sentinel-2: B4 / B3 - SuperView Neo: Red / Green - SuperView-1: Red / Green - SuperView-2: Red / Green - TripleSat: Red / Green - WorldView 2: Red / Green - WorldView 3: Red / Green - WorldView 4: Red / Green - WorldView Legion: Red / Green --- #### FEI — Ferrous Iron Index URL: https://docs.geopera.com/spectral-indices/ferrous_iron Category: geology Geological index for detecting ferrous iron (Fe2+) concentrations in rocks and soils. Combines SWIR and visible-NIR ratios to identify iron-bearing minerals and geological formations. Formula: (SWIR5 / Red + NIR1 / Green) Wavelengths: Green (520-600 nm), Red (630-690 nm), NIR1 (760-860 nm), SWIR5 (2145-2185 nm) Applications: Geology, Mineral Exploration, Iron Ore Detection, Metal Detection, Geological Mapping References: ASTER sensor applications Sensor-specific formulas: - Landsat 8/9: (B7 / B4 + B5 / B3) - Sentinel-2: (B12 / B4 + B8 / B3) - WorldView 3: (SWIR5 / Red + NIR1 / Green) --- #### FOX — Ferric Oxides Index URL: https://docs.geopera.com/spectral-indices/ferric_oxides Category: geology Geological index for detecting ferric oxide concentrations in rocks and soils. Useful for identifying iron-rich minerals and oxide formations in geological mapping and mineral exploration. Formula: NIR / Red Wavelengths: Red (760-860 nm), NIR (1600-1700 nm) Applications: Geology, Mineral Exploration, Iron Oxide Detection, Metal Detection, Geological Mapping References: ASTER sensor applications Sensor-specific formulas: - Landsat 8/9: B6 / B5 - Sentinel-2: B11 / B7 - SuperView-2: SWIR / NIR1 - WorldView 3: SWIR3 / NIR1 --- #### FSI — Ferrous Silicates Index URL: https://docs.geopera.com/spectral-indices/ferrous_silicates Category: geology Geological index for detecting ferrous silicate minerals in rocks and geological formations. Useful for identifying iron-bearing silicate minerals and mafic rock compositions. Formula: SWIR5 / SWIR4 Wavelengths: SWIR4 (1600-1700 nm), SWIR5 (2145-2185 nm) Applications: Geology, Mineral Exploration, Iron Ore Detection, Metal Detection, Mafic Rock Mapping References: ASTER sensor applications Sensor-specific formulas: - Landsat 8/9: B7 / B6 - Sentinel-2: B12 / B11 - WorldView 3: SWIR5 / SWIR3 --- #### GOS — Gossan Index URL: https://docs.geopera.com/spectral-indices/gossan Category: geology Geological index for detecting gossan formations - weathered, oxidized iron-bearing rocks that form at the surface above sulfide mineral deposits. Essential for mineral exploration and identifying potential ore deposits. Formula: SWIR4 / Red Wavelengths: Red (630-690 nm), SWIR4 (1600-1700 nm) Applications: Mineral Exploration, Iron Ore Detection, Metal Detection, Geological Mapping, Sulfide Deposit Detection References: ASTER sensor applications Sensor-specific formulas: - Landsat 8/9: B6 / B4 - Sentinel-2: B11 / B4 - SuperView-2: SWIR / Red - WorldView 3: SWIR3 / Red --- #### HRI — Host Rock Index URL: https://docs.geopera.com/spectral-indices/host_rock Category: geology Geological index for characterizing host rock properties and lithologic mapping. Uses SWIR bands to identify rock types and mineral compositions, particularly useful for geological surveys. Formula: SWIR5 / SWIR6 Wavelengths: SWIR5 (2145-2185 nm), SWIR6 (2185-2225 nm) Applications: Geology, Rock Identification, Metal Detection, Iron Analysis, Lithologic Mapping References: ASTER sensor applications Sensor-specific formulas: - WorldView 3: SWIR5 / SWIR6 --- #### Kaolinitic — Kaolinitic Index URL: https://docs.geopera.com/spectral-indices/kaolinitic Category: geology Geological index for detecting kaolinitic minerals and alteration zones. Uses shortwave infrared bands to identify clay mineral distributions in geological formations. Formula: SWIR2 / SWIR1 Wavelengths: SWIR1 (2145-2185 nm), SWIR2 (2235-2365 nm) Applications: Geological Mapping, Mineral Exploration, Clay Mineral Detection, Iron Ore Exploration, Alteration Zone Mapping References: Hewson et al. (2001) Sensor-specific formulas: - WorldView 3: SWIR8 / SWIR5 --- #### Laterite — Laterite Index URL: https://docs.geopera.com/spectral-indices/laterite Category: geology Geological index for detecting laterite formations and iron-rich weathering zones. Particularly effective for identifying lateritic soils and weathered surfaces. Formula: SWIR1 / SWIR0 Wavelengths: SWIR0 (1600-1700 nm), SWIR1 (2145-2185 nm) Applications: Geological Mapping, Iron Ore Detection, Weathering Zone Mapping, Laterite Formation Analysis, Mineral Exploration References: ASTER band ratio studies Sensor-specific formulas: - Landsat 8/9: B7 / B6 - Sentinel-2: B12 / B11 - WorldView 3: SWIR5 / SWIR3 --- #### Muscovite — Muscovite Index URL: https://docs.geopera.com/spectral-indices/muscovite Category: geology Geological index for detecting muscovite minerals and mica-rich formations. Uses shortwave infrared bands to identify phyllosilicate minerals in geological mapping. Formula: SWIR2 / SWIR1 Wavelengths: SWIR1 (2185-2225 nm), SWIR2 (2235-2365 nm) Applications: Geological Mapping, Mineral Exploration, Mica Detection, Iron-related Metal Detection, Alteration Zone Analysis References: ASTER geological applications Sensor-specific formulas: - WorldView 3: SWIR8 / SWIR6 --- #### NDSI — Normalized Difference Salinity Index URL: https://docs.geopera.com/spectral-indices/ndsi Category: geology Soil salinity detection index using shortwave infrared bands. Effective for identifying salt-affected soils and monitoring soil degradation in arid regions. Formula: (SWIR1 - SWIR2) / (SWIR1 + SWIR2) Wavelengths: SWIR1 (1600-1700 nm), SWIR2 (2145-2185 nm) Applications: Soil Salinity Detection, Soil Quality Assessment, Agricultural Land Management, Desertification Monitoring, Irrigation Impact Assessment References: Khan et al. (2005) Sensor-specific formulas: - Landsat 8/9: (B6 - B7) / (B6 + B7) - Sentinel-2: (B11 - B12) / (B11 + B12) - WorldView 3: (SWIR3 - SWIR5) / (SWIR3 + SWIR5) --- #### Phengitic — Phengitic URL: https://docs.geopera.com/spectral-indices/phengitic Category: geology A geological spectral index designed for mineral exploration, particularly for identifying iron-related geological features. Commonly used with ASTER satellite data for geological mapping and alteration detection. Formula: SWIR1 / SWIR2 Wavelengths: Applications: Geology, Metal - Iron, Mineral exploration, Geological mapping References: Hewson; Rob D. et al. (2001) --- #### Quartz Index — Quartz Rich Rocks URL: https://docs.geopera.com/spectral-indices/quartz_index Category: geology A geological spectral index for identifying quartz-rich geological formations using thermal infrared bands. Used for lithologic mapping and rock identification, particularly useful for detecting iron-related geological features. Formula: TIR1 / TIR2 Wavelengths: Applications: Geology, Rock identification, Metal (Iron) analysis, Lithologic mapping References: Rowan & Mars (2003) --- #### Sericite Index — Sericite/Muscovite/Illite/Smecite URL: https://docs.geopera.com/spectral-indices/sericite_index Category: geology A geological spectral index used for mineral identification and geological mapping, particularly for detecting specific clay minerals (sericite, muscovite, illite, smectite) and iron-related geological features. Used for lithologic mapping with ASTER data. Formula: (SWIR1 + SWIR3) / SWIR2 Wavelengths: Applications: Geology, Metal - Iron Detection, Mineral identification, Lithologic mapping References: Rowan & Mars (2003) --- ### Urban Category (9 indices) #### H — Hue Index URL: https://docs.geopera.com/spectral-indices/hue Category: urban Color index representing the dominant wavelength in visible spectrum. Used for color analysis and classification of materials based on their spectral hue characteristics. Formula: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) Wavelengths: Red (630-690 nm), Green (530-590 nm), Blue (450-515 nm) Applications: Color Analysis, Material Classification, Urban Mapping, Land Cover Analysis References: Standard color theory Sensor-specific formulas: - BJ3A: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) - BJ3N: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) - Dragonette-1: arctan((2 * Band 10 - Band 4 - Band 1) / (30.5 * (Band 4 - Band 1))) - Dragonette-2/3: arctan((2 * Band14 - Band8 - Band2) / (30.5 * (Band8 - Band2))) - Gaofen-1: arctan((2 * Red - Green - Panchromatic) / (30.5 * (Green - Panchromatic))) - Gaofen-2: arctan((2 * Red - Green - Panchromatic) / (30.5 * (Green - Panchromatic))) - GeoEye-1: arctan((2 * Red - Green - Panchromatic) / (30.5 * (Green - Panchromatic))) - Göktürk-1: arctan((2 * Red - Green - Panchromatic) / (30.5 * (Green - Panchromatic))) - Jilin-1: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) - Jilin-1 GF03D: arctan((2 * Red - Green - Panchromatic) / (30.5 * (Green - Panchromatic))) - KOMPSAT-3: arctan((2 * Red - Green - Panchromatic) / (30.5 * (Green - Panchromatic))) - KOMPSAT-3A: arctan((2 * Red - Green - Panchromatic) / (30.5 * (Green - Panchromatic))) - Landsat 8/9: arctan((2 * B4 - B3 - B1) / (30.5 * (B3 - B1))) - NAIP: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) - Sentinel-2: arctan((2 * B4 - B3 - B1) / (30.5 * (B3 - B1))) - SuperView Neo: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) - SuperView-1: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) - SuperView-2: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) - TripleSat: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) - WorldView 2: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) - WorldView 3: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) - WorldView 4: arctan((2 * Red - Green - Panchromatic) / (30.5 * (Green - Panchromatic))) - WorldView Legion: arctan((2 * Red - Green - Blue) / (30.5 * (Green - Blue))) --- #### I — Intensity Index URL: https://docs.geopera.com/spectral-indices/intensity Category: urban Color brightness measure representing the total reflectance across visible bands. Used for overall brightness analysis and intensity-based classification. Formula: (1/30.5) * (Red + Green + Blue) Wavelengths: Red (630-690 nm), Green (530-590 nm), Blue (450-515 nm) Applications: Brightness Analysis, Color Space Conversion, Urban Mapping, Material Classification References: Standard color theory Sensor-specific formulas: - BJ3A: (1/30.5) * (Red + Green + Blue) - BJ3N: (1/30.5) * (Red + Green + Blue) - Dragonette-1: (1/30.5) * (Band 10 + Band 4 + Band 1) - Dragonette-2/3: (1/30.5) * (Band14 + Band8 + Band2) - Gaofen-1: (1/30.5) * (Red + Green + Panchromatic) - Gaofen-2: (1/30.5) * (Red + Green + Panchromatic) - GeoEye-1: (1/30.5) * (Red + Green + Panchromatic) - Göktürk-1: (1/30.5) * (Red + Green + Panchromatic) - Jilin-1: (1/30.5) * (Red + Green + Blue) - Jilin-1 GF03D: (1/30.5) * (Red + Green + Panchromatic) - KOMPSAT-3: (1/30.5) * (Red + Green + Panchromatic) - KOMPSAT-3A: (1/30.5) * (Red + Green + Panchromatic) - Landsat 8/9: (1/30.5) * (B4 + B3 + B1) - NAIP: (1/30.5) * (Red + Green + Blue) - Sentinel-2: (1/30.5) * (B4 + B3 + B1) - SuperView Neo: (1/30.5) * (Red + Green + Blue) - SuperView-1: (1/30.5) * (Red + Green + Blue) - SuperView-2: (1/30.5) * (Red + Green + Blue) - TripleSat: (1/30.5) * (Red + Green + Blue) - WorldView 2: (1/30.5) * (Red + Green + Blue) - WorldView 3: (1/30.5) * (Red + Green + Blue) - WorldView 4: (1/30.5) * (Red + Green + Panchromatic) - WorldView Legion: (1/30.5) * (Red + Green + Blue) --- #### NDBI — Normalized Difference Built-up Index URL: https://docs.geopera.com/spectral-indices/ndbi Category: urban Highlights built-up areas and urban development. Higher values indicate more built-up surfaces. Formula: (SWIR - NIR) / (SWIR + NIR) Wavelengths: NIR (770-900 nm), SWIR (1550-1750 nm) Applications: Urban Planning, Land Use Classification, Urban Growth Monitoring References: Zha et al. (2003) Sensor-specific formulas: - Landsat 8/9: (B6 - B5) / (B6 + B5) - Sentinel-2: (B11 - B8) / (B11 + B8) - SuperView-2: (SWIR - NIR1) / (SWIR + NIR1) - WorldView 3: (SWIR2 - NIR1) / (SWIR2 + NIR1) --- #### NHFD — Non-Homogeneous Feature Difference URL: https://docs.geopera.com/spectral-indices/nhfd Category: urban Non-Homogeneous Feature Difference for urban applications Formula: (RE1 - A) / (RE1 + A) Wavelengths: RE1 (700-710 nm) Applications: Urban References: https://www.semanticscholar.org/paper/Using-WorldView-2-Vis-NIR-MSI-Imagery-to-Support-Wolf/5e5063ccc4ee76b56b721c866e871d47a77f9fb4 Sensor-specific formulas: - Dragonette-1: (Band 16 - A) / (Band 16 + A) - Dragonette-2/3: (Band20 - A) / (Band20 + A) - Gaofen-1: (Panchromatic - A) / (Panchromatic + A) - Gaofen-2: (Panchromatic - A) / (Panchromatic + A) - GeoEye-1: (Panchromatic - A) / (Panchromatic + A) - Göktürk-1: (Panchromatic - A) / (Panchromatic + A) - Jilin-1 GF03D: (Panchromatic - A) / (Panchromatic + A) - KOMPSAT-3: (Panchromatic - A) / (Panchromatic + A) - KOMPSAT-3A: (Panchromatic - A) / (Panchromatic + A) - Sentinel-2: (B5 - A) / (B5 + A) - SuperView-2: (Red_Edge - A) / (Red_Edge + A) - WorldView 1: (Panchromatic - A) / (Panchromatic + A) - WorldView 2: (Red_Edge - A) / (Red_Edge + A) - WorldView 3: (Red Edge - A) / (Red Edge + A) - WorldView 4: (Panchromatic - A) / (Panchromatic + A) - WorldView Legion: (Red_Edge - A) / (Red_Edge + A) --- #### PISI — Perpendicular Impervious Surface Index URL: https://docs.geopera.com/spectral-indices/pisi Category: urban Perpendicular Impervious Surface Index for urban applications Formula: 0.8192 * B - 0.5735 * N + 0.0750 Wavelengths: B (450-520 nm), N (770-900 nm) Applications: Urban References: https://doi.org/10.3390/rs10101521 Sensor-specific formulas: - BJ3A: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - BJ3N: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - Dragonette-1: 0.8192 * Band 1 - 0.5735 * Band 23 + 0.0750 - Dragonette-2/3: 0.8192 * Band4 - 0.5735 * Band29 + 0.0750 - Gaofen-1: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - Gaofen-2: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - GeoEye-1: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - Göktürk-1: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - Jilin-1: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - Jilin-1 GF03D: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - KOMPSAT-3: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - KOMPSAT-3A: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - Landsat 8/9: 0.8192 * B2 - 0.5735 * B5 + 0.0750 - NAIP: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - Sentinel-2: 0.8192 * B2 - 0.5735 * B8 + 0.0750 - SuperView Neo: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - SuperView-1: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - SuperView-2: 0.8192 * Blue - 0.5735 * NIR1 + 0.0750 - TripleSat: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - WorldView 2: 0.8192 * Blue - 0.5735 * NIR1 + 0.0750 - WorldView 3: 0.8192 * Blue - 0.5735 * NIR1 + 0.0750 - WorldView 4: 0.8192 * Blue - 0.5735 * NIR + 0.0750 - WorldView Legion: 0.8192 * Blue - 0.5735 * NIR1 + 0.0750 --- #### UI — Urban Index URL: https://docs.geopera.com/spectral-indices/ui Category: urban Urban Index - A spectral index for urban applications. Formula: (S2 - N)/(S2 + N) Wavelengths: S2 (2190), N (850) Applications: urban References: https://www.isprs.org/proceedings/XXXI/congress/part7/321_XXXI-part7.pdf Sensor-specific formulas: - Landsat 8/9: (B7 - B5)/(B7 + B5) - Sentinel-2: (B12 - B8)/(B12 + B8) - WorldView 3: (SWIR6 - NIR1)/(SWIR6 + NIR1) --- #### VgNIRBI — Visible Green-Based Built-Up Index URL: https://docs.geopera.com/spectral-indices/vgnirbi Category: urban Visible Green-Based Built-Up Index - A spectral index for urban applications. Formula: (G - N)/(G + N) Wavelengths: G (550), N (850) Applications: urban References: https://doi.org/10.1016/j.ecolind.2015.03.037 Sensor-specific formulas: - BJ3A: (Green - NIR)/(Green + NIR) - BJ3N: (Green - NIR)/(Green + NIR) - Dragonette-2/3: (Band9 - Band30)/(Band9 + Band30) - Gaofen-1: (Green - NIR)/(Green + NIR) - Gaofen-2: (Green - NIR)/(Green + NIR) - GeoEye-1: (Green - NIR)/(Green + NIR) - Göktürk-1: (Green - NIR)/(Green + NIR) - Jilin-1: (Green - NIR)/(Green + NIR) - Jilin-1 GF03D: (Green - NIR)/(Green + NIR) - KOMPSAT-3: (Green - NIR)/(Green + NIR) - KOMPSAT-3A: (Green - NIR)/(Green + NIR) - Landsat 8/9: (B3 - B5)/(B3 + B5) - NAIP: (Green - NIR)/(Green + NIR) - Sentinel-2: (B3 - B8)/(B3 + B8) - SuperView Neo: (Green - NIR)/(Green + NIR) - SuperView-1: (Green - NIR)/(Green + NIR) - SuperView-2: (Green - NIR1)/(Green + NIR1) - TripleSat: (Green - NIR)/(Green + NIR) - WorldView 2: (Green - NIR1)/(Green + NIR1) - WorldView 3: (Green - NIR1)/(Green + NIR1) - WorldView 4: (Green - NIR)/(Green + NIR) - WorldView Legion: (Green - NIR1)/(Green + NIR1) --- #### VIBI — Vegetation Index Built-up Index URL: https://docs.geopera.com/spectral-indices/vibi Category: urban Vegetation Index Built-up Index - A spectral index for urban applications. Formula: ((N-R)/(N+R))/(((N-R)/(N+R)) + ((S1-N)/(S1+N))) Wavelengths: N (850), R (650), S1 (1610) Applications: urban References: http://dx.doi.org/10.1080/01431161.2012.687842 Sensor-specific formulas: - Landsat 8/9: ((B5-B4)/(B5+B4))/(((B5-B4)/(B5+B4)) + ((B6-B5)/(B6+B5))) - Sentinel-2: ((B8-B4)/(B8+B4))/(((B8-B4)/(B8+B4)) + ((B11-B8)/(B11+B8))) - SuperView-2: ((NIR1-Red)/(NIR1+Red))/(((NIR1-Red)/(NIR1+Red)) + ((SWIR-NIR1)/(SWIR+NIR1))) --- #### VrNIRBI — Visible Red-Based Built-Up Index URL: https://docs.geopera.com/spectral-indices/vrnirbi Category: urban Visible Red-Based Built-Up Index - A spectral index for urban applications. Formula: (R - N)/(R + N) Wavelengths: R (650), N (850) Applications: urban References: https://doi.org/10.1016/j.ecolind.2015.03.037 Sensor-specific formulas: - BJ3A: (Red - NIR)/(Red + NIR) - BJ3N: (Red - NIR)/(Red + NIR) - Dragonette-2/3: (Band15 - Band30)/(Band15 + Band30) - Gaofen-1: (Red - NIR)/(Red + NIR) - Gaofen-2: (Red - NIR)/(Red + NIR) - GeoEye-1: (Red - NIR)/(Red + NIR) - Göktürk-1: (Red - NIR)/(Red + NIR) - Jilin-1: (Red - NIR)/(Red + NIR) - Jilin-1 GF03D: (Red - NIR)/(Red + NIR) - KOMPSAT-3: (Red - NIR)/(Red + NIR) - KOMPSAT-3A: (Red - NIR)/(Red + NIR) - Landsat 8/9: (B4 - B5)/(B4 + B5) - NAIP: (Red - NIR)/(Red + NIR) - Sentinel-2: (B4 - B8)/(B4 + B8) - SuperView Neo: (Red - NIR)/(Red + NIR) - SuperView-1: (Red - NIR)/(Red + NIR) - SuperView-2: (Red - NIR1)/(Red + NIR1) - TripleSat: (Red - NIR)/(Red + NIR) - WorldView 2: (Red - NIR1)/(Red + NIR1) - WorldView 3: (Red - NIR1)/(Red + NIR1) - WorldView 4: (Red - NIR)/(Red + NIR) - WorldView Legion: (Red - NIR1)/(Red + NIR1) --- ### Soil Category (8 indices) #### BI — Brightness Index URL: https://docs.geopera.com/spectral-indices/bi Category: soil The Brightness Index (BI) is a remote sensing index used to assess soil brightness, which is highly correlated with soil moisture, salt content, and organic matter. Developed by Mathieu and Escadafal, it provides valuable information about soil properties and fertility. As brightness increases, soil fertility typically decreases. Formula: sqrt((RED^2 + GREEN^2) / 2) Wavelengths: GREEN (560), RED (665) Applications: soil brightness assessment, soil moisture detection, salt content evaluation, organic matter estimation, soil fertility mapping, agricultural field monitoring, bare soil analysis References: Escadafal, R. (1989) - Remote sensing of arid soil surface color with Landsat thematic mapper. Advances in Space Research, 159–163; Mathieu, R., Pouget, M., Cervelle, B., and Escadafal, R. (1998) - Relationships between satellite-based radiometric indices simulated using laboratory reflectance data and typic soil color of an arid environment Sensor-specific formulas: - BJ3A: sqrt((Red^2 + Green^2) / 2) - BJ3N: sqrt((Red^2 + Green^2) / 2) - Dragonette-1: sqrt((Band 13^2 + Band 6^2) / 2) - Dragonette-2/3: sqrt((Band16^2 + Band10^2) / 2) - Gaofen-1: sqrt((Red^2 + Green^2) / 2) - Gaofen-2: sqrt((Red^2 + Green^2) / 2) - GeoEye-1: sqrt((Red^2 + Green^2) / 2) - Göktürk-1: sqrt((Red^2 + Green^2) / 2) - Jilin-1: sqrt((Red^2 + Green^2) / 2) - Jilin-1 GF03D: sqrt((Red^2 + Green^2) / 2) - KOMPSAT-3: sqrt((Red^2 + Green^2) / 2) - KOMPSAT-3A: sqrt((Red^2 + Green^2) / 2) - Landsat 8/9: sqrt((B4^2 + B3^2) / 2) - NAIP: sqrt((Red^2 + Green^2) / 2) - Sentinel-2: sqrt((B4^2 + B3^2) / 2) - SuperView Neo: sqrt((Red^2 + Green^2) / 2) - SuperView-1: sqrt((Red^2 + Green^2) / 2) - SuperView-2: sqrt((Red^2 + Green^2) / 2) - TripleSat: sqrt((Red^2 + Green^2) / 2) - WorldView 2: sqrt((Red^2 + Green^2) / 2) - WorldView 3: sqrt((Red^2 + Green^2) / 2) - WorldView 4: sqrt((Red^2 + Green^2) / 2) - WorldView Legion: sqrt((Red^2 + Green^2) / 2) --- #### BI2 — Second Brightness Index URL: https://docs.geopera.com/spectral-indices/bi2 Category: soil The Second Brightness Index (BI2) is an enhanced version of the Brightness Index that includes the near-infrared band in addition to red and green bands. Developed by Escadafal and Huete, it provides improved assessment of soil properties, particularly soil organic carbon content and moisture levels. Formula: sqrt((RED^2 + GREEN^2 + NIR^2) / 3) Wavelengths: GREEN (560), RED (665), NIR (842) Applications: soil brightness assessment, soil organic carbon estimation, soil moisture detection, soil fertility mapping, bare soil analysis, agricultural monitoring References: Escadafal, R. and Huete, A. (1992) - Improvement in remote sensing of low vegetation cover in arid regions by correcting for soil 'brightness' Sensor-specific formulas: - BJ3A: sqrt((Red^2 + Green^2 + NIR^2) / 3) - BJ3N: sqrt((Red^2 + Green^2 + NIR^2) / 3) - Dragonette-2/3: sqrt((Band16^2 + Band10^2 + Band30^2) / 3) - Gaofen-1: sqrt((Red^2 + Green^2 + NIR^2) / 3) - Gaofen-2: sqrt((Red^2 + Green^2 + NIR^2) / 3) - GeoEye-1: sqrt((Red^2 + Green^2 + NIR^2) / 3) - Göktürk-1: sqrt((Red^2 + Green^2 + NIR^2) / 3) - Jilin-1: sqrt((Red^2 + Green^2 + NIR^2) / 3) - Jilin-1 GF03D: sqrt((Red^2 + Green^2 + NIR^2) / 3) - KOMPSAT-3: sqrt((Red^2 + Green^2 + NIR^2) / 3) - KOMPSAT-3A: sqrt((Red^2 + Green^2 + NIR^2) / 3) - Landsat 8/9: sqrt((B4^2 + B3^2 + B5^2) / 3) - NAIP: sqrt((Red^2 + Green^2 + NIR^2) / 3) - Sentinel-2: sqrt((B4^2 + B3^2 + B8^2) / 3) - SuperView Neo: sqrt((Red^2 + Green^2 + NIR^2) / 3) - SuperView-1: sqrt((Red^2 + Green^2 + NIR^2) / 3) - SuperView-2: sqrt((Red^2 + Green^2 + NIR1^2) / 3) - TripleSat: sqrt((Red^2 + Green^2 + NIR^2) / 3) - WorldView 2: sqrt((Red^2 + Green^2 + NIR1^2) / 3) - WorldView 3: sqrt((Red^2 + Green^2 + NIR1^2) / 3) - WorldView 4: sqrt((Red^2 + Green^2 + NIR^2) / 3) - WorldView Legion: sqrt((Red^2 + Green^2 + NIR1^2) / 3) --- #### CI — Coloration Index URL: https://docs.geopera.com/spectral-indices/ci_soil Category: soil The Coloration Index (CI) was developed by Pouget et al. (1990) to characterize soil color properties in arid and semi-arid regions. Low CI values correlate with high concentrations of carbonates or sulfates, while higher values correlate with crusted soils and sands. The index helps monitor surface degradation and infiltrability variations. Formula: (RED - GREEN) / (RED + GREEN) Wavelengths: GREEN (520-600), RED (640-760) Applications: soil color characterization, carbonate and sulfate detection, surface degradation monitoring, infiltrability assessment, arid land soil mapping, soil crust detection, diachronic soil surface evolution References: Pouget, M., Madeira, J., Le Floch, E., and Kamal, S. (1990) - Caracteristiques spectrales des surfaces sableuses de la region cotiere Nord-Ouest de I'Egypte; Mathieu, R. and Pouget, M. (1998) - Relationships between satellite-based radiometric indices simulated using laboratory reflectance data and typic soil color of an arid environment Sensor-specific formulas: - BJ3A: (Red - Green) / (Red + Green) - BJ3N: (Red - Green) / (Red + Green) - Dragonette-1: (Band 16 - Band 6) / (Band 16 + Band 6) - Dragonette-2/3: (Band20 - Band10) / (Band20 + Band10) - Gaofen-1: (Red - Green) / (Red + Green) - Gaofen-2: (Red - Green) / (Red + Green) - GeoEye-1: (Red - Green) / (Red + Green) - Göktürk-1: (Red - Green) / (Red + Green) - Jilin-1: (Red - Green) / (Red + Green) - Jilin-1 GF03D: (Red - Green) / (Red + Green) - KOMPSAT-3: (Red - Green) / (Red + Green) - KOMPSAT-3A: (Red - Green) / (Red + Green) - Landsat 8/9: (B4 - B3) / (B4 + B3) - NAIP: (Red - Green) / (Red + Green) - Sentinel-2: (B4 - B3) / (B4 + B3) - SuperView Neo: (Red - Green) / (Red + Green) - SuperView-1: (Red - Green) / (Red + Green) - SuperView-2: (Red - Green) / (Red + Green) - TripleSat: (Red - Green) / (Red + Green) - WorldView 2: (Red - Green) / (Red + Green) - WorldView 3: (Red - Green) / (Red + Green) - WorldView 4: (Red - Green) / (Red + Green) - WorldView Legion: (Red - Green) / (Red + Green) --- #### NSDS — Normalized Shortwave Infrared Difference Soil-Moisture URL: https://docs.geopera.com/spectral-indices/nsds Category: soil Normalized Shortwave Infrared Difference Soil-Moisture for soil applications Formula: (S1 - S2)/(S1 + S2) Wavelengths: S1 (1550-1750 nm), S2 (2080-2350 nm) Applications: Soil References: https://doi.org/10.3390/land10030231 Sensor-specific formulas: - Landsat 8/9: (B6 - B7)/(B6 + B7) - Sentinel-2: (B11 - B12)/(B11 + B12) - WorldView 3: (SWIR3 - SWIR6)/(SWIR3 + SWIR6) --- #### NSDSI1 — Normalized Shortwave-Infrared Difference Bare Soil Moisture Index 1 URL: https://docs.geopera.com/spectral-indices/nsdsi1 Category: soil Normalized Shortwave-Infrared Difference Bare Soil Moisture Index 1 for soil applications Formula: (S1-S2)/S1 Wavelengths: S1 (1550-1750 nm), S2 (2080-2350 nm) Applications: Soil References: https://doi.org/10.1016/j.isprsjprs.2019.06.012 Sensor-specific formulas: - Landsat 8/9: (B6-B7)/B6 - Sentinel-2: (B11-B12)/B11 - WorldView 3: (SWIR3-SWIR6)/SWIR3 --- #### NSDSI2 — Normalized Shortwave-Infrared Difference Bare Soil Moisture Index 2 URL: https://docs.geopera.com/spectral-indices/nsdsi2 Category: soil Normalized Shortwave-Infrared Difference Bare Soil Moisture Index 2 for soil applications Formula: (S1-S2)/S2 Wavelengths: S1 (1550-1750 nm), S2 (2080-2350 nm) Applications: Soil References: https://doi.org/10.1016/j.isprsjprs.2019.06.012 Sensor-specific formulas: - Landsat 8/9: (B6-B7)/B7 - Sentinel-2: (B11-B12)/B12 - WorldView 3: (SWIR3-SWIR6)/SWIR6 --- #### NSDSI3 — Normalized Shortwave-Infrared Difference Bare Soil Moisture Index 3 URL: https://docs.geopera.com/spectral-indices/nsdsi3 Category: soil Normalized Shortwave-Infrared Difference Bare Soil Moisture Index 3 for soil applications Formula: (S1-S2)/(S1+S2) Wavelengths: S1 (1550-1750 nm), S2 (2080-2350 nm) Applications: Soil References: https://doi.org/10.1016/j.isprsjprs.2019.06.012 Sensor-specific formulas: - Landsat 8/9: (B6-B7)/(B6+B7) - Sentinel-2: (B11-B12)/(B11+B12) - WorldView 3: (SWIR3-SWIR6)/(SWIR3+SWIR6) --- #### RI4XS — SPOT HRV XS-based Redness Index 4 URL: https://docs.geopera.com/spectral-indices/ri4xs Category: soil SPOT HRV XS-based Redness Index 4 for soil applications Formula: (R**2.0)/(G**4.0) Wavelengths: R (630-690 nm), G (520-600 nm) Applications: Soil References: https://doi.org/10.1016/S0034-4257(98)00030-3 Sensor-specific formulas: - BJ3A: (Red**2.0)/(Green**4.0) - BJ3N: (Red**2.0)/(Green**4.0) - Dragonette-1: (Band 12**2.0)/(Band 6**4.0) - Dragonette-2/3: (Band16**2.0)/(Band10**4.0) - Gaofen-1: (Red**2.0)/(Green**4.0) - Gaofen-2: (Red**2.0)/(Green**4.0) - GeoEye-1: (Red**2.0)/(Green**4.0) - Göktürk-1: (Red**2.0)/(Green**4.0) - Jilin-1: (Red**2.0)/(Green**4.0) - Jilin-1 GF03D: (Red**2.0)/(Green**4.0) - KOMPSAT-3: (Red**2.0)/(Green**4.0) - KOMPSAT-3A: (Red**2.0)/(Green**4.0) - Landsat 8/9: (B4**2.0)/(B3**4.0) - NAIP: (Red**2.0)/(Green**4.0) - Sentinel-2: (B4**2.0)/(B3**4.0) - SuperView Neo: (Red**2.0)/(Green**4.0) - SuperView-1: (Red**2.0)/(Green**4.0) - SuperView-2: (Red**2.0)/(Green**4.0) - TripleSat: (Red**2.0)/(Green**4.0) - WorldView 2: (Red**2.0)/(Green**4.0) - WorldView 3: (Red**2.0)/(Green**4.0) - WorldView 4: (Red**2.0)/(Green**4.0) - WorldView Legion: (Red**2.0)/(Green**4.0) --- ### Fire Category (3 indices) #### NSTv1 — NIR-SWIR-Temperature Version 1 URL: https://docs.geopera.com/spectral-indices/nstv1 Category: fire NIR-SWIR-Temperature Version 1 for burn applications Formula: ((N-S2)/(N+S2))*T Wavelengths: N (770-900 nm), S2 (2080-2350 nm) Applications: Burn References: https://doi.org/10.1016/j.rse.2011.06.010 Sensor-specific formulas: - Landsat 8/9: ((B5-B7)/(B5+B7))*T - Sentinel-2: ((B8-B12)/(B8+B12))*T - WorldView 3: ((NIR1-SWIR6)/(NIR1+SWIR6))*T --- #### NSTv2 — NIR-SWIR-Temperature Version 2 URL: https://docs.geopera.com/spectral-indices/nstv2 Category: fire NIR-SWIR-Temperature Version 2 for burn applications Formula: (N-(S2+T))/(N+(S2+T)) Wavelengths: N (770-900 nm), S2 (2080-2350 nm) Applications: Burn References: https://doi.org/10.1016/j.rse.2011.06.010 Sensor-specific formulas: - Landsat 8/9: (B5-(B7+T))/(B5+(B7+T)) - Sentinel-2: (B8-(B12+T))/(B8+(B12+T)) - WorldView 3: (NIR1-(SWIR6+T))/(NIR1+(SWIR6+T)) --- #### SAVIT — Soil-Adjusted Vegetation Index Thermal URL: https://docs.geopera.com/spectral-indices/savit Category: fire Soil-Adjusted Vegetation Index Thermal for burn applications Formula: (1.0 + L) * (N - (R * T / 10000.0)) / (N + (R * T / 10000.0) + L) Wavelengths: N (770-900 nm), R (630-690 nm) Applications: Burn References: https://doi.org/10.1080/01431160600954704 Sensor-specific formulas: - BJ3A: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - BJ3N: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - Dragonette-1: (1.0 + L) * (Band 23 - (Band 12 * T / 10000.0)) / (Band 23 + (Band 12 * T / 10000.0) + L) - Dragonette-2/3: (1.0 + L) * (Band29 - (Band16 * T / 10000.0)) / (Band29 + (Band16 * T / 10000.0) + L) - Gaofen-1: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - Gaofen-2: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - GeoEye-1: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - Göktürk-1: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - Jilin-1: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - Jilin-1 GF03D: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - KOMPSAT-3: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - KOMPSAT-3A: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - Landsat 8/9: (1.0 + L) * (B5 - (B4 * T / 10000.0)) / (B5 + (B4 * T / 10000.0) + L) - NAIP: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - Sentinel-2: (1.0 + L) * (B8 - (B4 * T / 10000.0)) / (B8 + (B4 * T / 10000.0) + L) - SuperView Neo: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - SuperView-1: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - SuperView-2: (1.0 + L) * (NIR1 - (Red * T / 10000.0)) / (NIR1 + (Red * T / 10000.0) + L) - TripleSat: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - WorldView 2: (1.0 + L) * (NIR1 - (Red * T / 10000.0)) / (NIR1 + (Red * T / 10000.0) + L) - WorldView 3: (1.0 + L) * (NIR1 - (Red * T / 10000.0)) / (NIR1 + (Red * T / 10000.0) + L) - WorldView 4: (1.0 + L) * (NIR - (Red * T / 10000.0)) / (NIR + (Red * T / 10000.0) + L) - WorldView Legion: (1.0 + L) * (NIR1 - (Red * T / 10000.0)) / (NIR1 + (Red * T / 10000.0) + L) --- ### Burn Category (2 indices) #### BAI — Burn Area Index URL: https://docs.geopera.com/spectral-indices/bai Category: burn The Burn Area Index (BAI) was developed by Chuvieco et al. (2002) to identify burned areas using the red and NIR spectral bands. BAI emphasizes the charcoal signal in post-fire images by considering the spectral distance from each pixel to a reference spectral point where recently burned areas tend to converge. Formula: 1 / ((0.1 - RED)^2 + (0.06 - NIR)^2) Wavelengths: RED (640-760), NIR (780-1400) Applications: burned area detection, fire severity assessment, post-fire monitoring, charcoal signal detection, fire damage mapping, wildfire impact assessment References: Chuvieco, E., Martin, M.P., and Palacios, A. (2002) - Assessment of Different Spectral Indices in the Red-Near-Infrared Spectral Domain for Burned Land Discrimination. Remote Sensing of Environment, 112, 2381-2396; Martín, M.P. (1998) - Cartografía e inventario de incendios forestales en la Península Ibérica a partir de imágenes NOAA-AVHRR Sensor-specific formulas: - BJ3A: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - BJ3N: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - Dragonette-1: 1 / ((0.1 - Band 16)^2 + (0.06 - Band 23)^2) - Dragonette-2/3: 1 / ((0.1 - Band20)^2 + (0.06 - Band31)^2) - Gaofen-1: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - Gaofen-2: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - GeoEye-1: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - Göktürk-1: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - Jilin-1: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - Jilin-1 GF03D: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - KOMPSAT-3: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - KOMPSAT-3A: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - Landsat 8/9: 1 / ((0.1 - B4)^2 + (0.06 - B5)^2) - NAIP: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - Sentinel-2: 1 / ((0.1 - B4)^2 + (0.06 - B8)^2) - SuperView Neo: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - SuperView-1: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - SuperView-2: 1 / ((0.1 - Red)^2 + (0.06 - NIR1)^2) - TripleSat: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - WorldView 2: 1 / ((0.1 - Red)^2 + (0.06 - NIR1)^2) - WorldView 3: 1 / ((0.1 - Red)^2 + (0.06 - NIR1)^2) - WorldView 4: 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2) - WorldView Legion: 1 / ((0.1 - Red)^2 + (0.06 - NIR1)^2) --- #### VI6T — VI6T Index URL: https://docs.geopera.com/spectral-indices/vi6t Category: burn VI6T Index - A spectral index for burn applications. Formula: (N - T/10000.0)/(N + T/10000.0) Wavelengths: N (850) Applications: burn References: https://doi.org/10.1080/01431160500239008 Sensor-specific formulas: - BJ3A: (NIR - T/10000.0)/(NIR + T/10000.0) - BJ3N: (NIR - T/10000.0)/(NIR + T/10000.0) - Dragonette-2/3: (Band30 - T/10000.0)/(Band30 + T/10000.0) - Gaofen-1: (NIR - T/10000.0)/(NIR + T/10000.0) - Gaofen-2: (NIR - T/10000.0)/(NIR + T/10000.0) - GeoEye-1: (NIR - T/10000.0)/(NIR + T/10000.0) - Göktürk-1: (NIR - T/10000.0)/(NIR + T/10000.0) - Jilin-1: (NIR - T/10000.0)/(NIR + T/10000.0) - Jilin-1 GF03D: (NIR - T/10000.0)/(NIR + T/10000.0) - KOMPSAT-3: (NIR - T/10000.0)/(NIR + T/10000.0) - KOMPSAT-3A: (NIR - T/10000.0)/(NIR + T/10000.0) - Landsat 8/9: (B5 - T/10000.0)/(B5 + T/10000.0) - NAIP: (NIR - T/10000.0)/(NIR + T/10000.0) - Sentinel-2: (B8 - T/10000.0)/(B8 + T/10000.0) - SuperView Neo: (NIR - T/10000.0)/(NIR + T/10000.0) - SuperView-1: (NIR - T/10000.0)/(NIR + T/10000.0) - SuperView-2: (NIR1 - T/10000.0)/(NIR1 + T/10000.0) - TripleSat: (NIR - T/10000.0)/(NIR + T/10000.0) - WorldView 1: (Panchromatic - T/10000.0)/(Panchromatic + T/10000.0) - WorldView 2: (NIR1 - T/10000.0)/(NIR1 + T/10000.0) - WorldView 3: (NIR1 - T/10000.0)/(NIR1 + T/10000.0) - WorldView 4: (NIR - T/10000.0)/(NIR + T/10000.0) - WorldView Legion: (NIR1 - T/10000.0)/(NIR1 + T/10000.0) --- ### Environmental Category (2 indices) #### S3 — S3 Snow Index URL: https://docs.geopera.com/spectral-indices/s3 Category: environmental S3 Snow Index for snow applications Formula: (N * (R - S1)) / ((N + R) * (N + S1)) Wavelengths: N (770-900 nm), R (630-690 nm), S1 (1550-1750 nm) Applications: Snow References: https://doi.org/10.3178/jjshwr.12.28 Sensor-specific formulas: - Landsat 8/9: (B5 * (B4 - B6)) / ((B5 + B4) * (B5 + B6)) - Sentinel-2: (B8 * (B4 - B11)) / ((B8 + B4) * (B8 + B11)) - SuperView-2: (NIR1 * (Red - SWIR)) / ((NIR1 + Red) * (NIR1 + SWIR)) - WorldView 3: (NIR1 * (Red - SWIR3)) / ((NIR1 + Red) * (NIR1 + SWIR3)) --- #### SWI — Snow Water Index URL: https://docs.geopera.com/spectral-indices/swi Category: environmental Snow Water Index for snow applications Formula: (G * (N - S1)) / ((G + N) * (N + S1)) Wavelengths: G (520-600 nm), N (770-900 nm), S1 (1550-1750 nm) Applications: Snow References: https://doi.org/10.3390/rs11232774 Sensor-specific formulas: - Landsat 8/9: (B3 * (B5 - B6)) / ((B3 + B5) * (B5 + B6)) - Sentinel-2: (B3 * (B8 - B11)) / ((B3 + B8) * (B8 + B11)) - SuperView-2: (Green * (NIR1 - SWIR)) / ((Green + NIR1) * (NIR1 + SWIR)) - WorldView 3: (Green * (NIR1 - SWIR3)) / ((Green + NIR1) * (NIR1 + SWIR3)) --- ### Radar Category (9 indices) #### QpRVI — Quad-Polarized Radar Vegetation Index URL: https://docs.geopera.com/spectral-indices/qprvi Category: radar Quad-Polarized Radar Vegetation Index for radar applications Formula: (8.0 * HV)/(HH + VV + 2.0 * HV) Wavelengths: Applications: Radar References: https://doi.org/10.1109/IGARSS.2001.976856 --- #### RFDI — Radar Forest Degradation Index URL: https://docs.geopera.com/spectral-indices/rfdi Category: radar Radar Forest Degradation Index for radar applications Formula: (HH - HV)/(HH + HV) Wavelengths: Applications: Radar References: https://doi.org/10.5194/bg-9-179-2012 --- #### VDDPI — Vertical Dual De-Polarization Index URL: https://docs.geopera.com/spectral-indices/vddpi Category: radar Vertical Dual De-Polarization Index - A spectral index for radar applications. Formula: (VV + VH)/VV Wavelengths: VV (SAR VV), VH (SAR VH) Applications: radar References: https://doi.org/10.1016/j.rse.2018.09.003 --- #### VHVVD — VH-VV Difference URL: https://docs.geopera.com/spectral-indices/vhvvd Category: radar VH-VV Difference - A spectral index for radar applications. Formula: VH - VV Wavelengths: VH (SAR VH), VV (SAR VV) Applications: radar References: https://doi.org/10.3390/app9040655 --- #### VHVVP — VH-VV Product URL: https://docs.geopera.com/spectral-indices/vhvvp Category: radar VH-VV Product - A spectral index for radar applications. Formula: VH * VV Wavelengths: VH (SAR VH), VV (SAR VV) Applications: radar References: https://doi.org/10.1109/IGARSS47720.2021.9554099 --- #### VHVVR — VH-VV Ratio URL: https://docs.geopera.com/spectral-indices/vhvvr Category: radar VH-VV Ratio - A spectral index for radar applications. Formula: VH/VV Wavelengths: VH (SAR VH), VV (SAR VV) Applications: radar References: https://doi.org/10.1109/IGARSS47720.2021.9554099 --- #### VVVHD — VV-VH Difference URL: https://docs.geopera.com/spectral-indices/vvvhd Category: radar VV-VH Difference - A spectral index for radar applications. Formula: VV - VH Wavelengths: VV (SAR VV), VH (SAR VH) Applications: radar References: https://doi.org/10.1109/IGARSS47720.2021.9554099 --- #### VVVHR — VV-VH Ratio URL: https://docs.geopera.com/spectral-indices/vvvhr Category: radar VV-VH Ratio - A spectral index for radar applications. Formula: VV/VH Wavelengths: VV (SAR VV), VH (SAR VH) Applications: radar References: https://doi.org/10.3390/app9040655 --- #### VVVHS — VV-VH Sum URL: https://docs.geopera.com/spectral-indices/vvvhs Category: radar VV-VH Sum - A spectral index for radar applications. Formula: VV + VH Wavelengths: VV (SAR VV), VH (SAR VH) Applications: radar References: https://doi.org/10.1109/IGARSS47720.2021.9554099 --- ### Kernel Category (5 indices) #### kEVI — Kernel Enhanced Vegetation Index URL: https://docs.geopera.com/spectral-indices/kevi Category: kernel Kernel Enhanced Vegetation Index - A spectral index for kernel applications. Formula: g * (kNN - kNR) / (kNN + C1 * kNR - C2 * kNB + kNL) Wavelengths: Applications: kernel References: https://doi.org/10.1126/sciadv.abc7447 --- #### kIPVI — Kernel Infrared Percentage Vegetation Index URL: https://docs.geopera.com/spectral-indices/kipvi Category: kernel Kernel Infrared Percentage Vegetation Index - A spectral index for kernel applications. Formula: kNN/(kNN + kNR) Wavelengths: Applications: kernel References: https://doi.org/10.1126/sciadv.abc7447 --- #### kNDVI — Kernel Normalized Difference Vegetation Index URL: https://docs.geopera.com/spectral-indices/kndvi Category: kernel Kernel Normalized Difference Vegetation Index - A spectral index for kernel applications. Formula: (kNN - kNR)/(kNN + kNR) Wavelengths: Applications: kernel References: https://doi.org/10.1126/sciadv.abc7447 --- #### kRVI — Kernel Ratio Vegetation Index URL: https://docs.geopera.com/spectral-indices/krvi Category: kernel Kernel Ratio Vegetation Index - A spectral index for kernel applications. Formula: kNN / kNR Wavelengths: Applications: kernel References: https://doi.org/10.1126/sciadv.abc7447 --- #### kVARI — Kernel Visible Atmospherically Resistant Index URL: https://docs.geopera.com/spectral-indices/kvari Category: kernel Kernel Visible Atmospherically Resistant Index - A spectral index for kernel applications. Formula: (kGG - kGR) / (kGG + kGR - kGB) Wavelengths: Applications: kernel References: https://doi.org/10.1126/sciadv.abc7447 ---