MCARI/MTVI2 — MCARI/MTVI2 | Spectral Index Calculator

((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))

Bands: 550, 670, 700, 800
Range: -1 to 1
Typical: Most vegetation 0.2 to 0.8

Used in crop monitoring.

When to use

Time-series monitoring of crop health, growth stages, and stress detection
Land cover classification and vegetation type discrimination
Biomass estimation and net primary productivity studies
Drought impact assessment over agricultural and forest areas
Phenology tracking — green-up, peak season, and senescence
agriculture
vegetation chlorophyll assessment

Limitations

Saturates in dense canopies (LAI > 3) — values plateau and lose discrimination ability
Sensitive to atmospheric scattering, especially blue-band haze
Soil background contaminates measurements in sparsely vegetated areas
Sun-sensor geometry (BRDF effects) introduces variability across acquisitions
Cloud cover and shadows produce invalid pixels that need masking
Requires four or more bands — limits portability across simpler sensors

What the values mean

-1 Water / Snow

-0.1 Bare ground / Built-up

0.1 Sparse / Stressed

0.3 Moderate vegetation

0.5 Healthy vegetation

0.7 Dense canopy

Surface type	Typical MCARI/MTVI2
Open water, snow	-0.3 to -0.1
Bare soil, urban	-0.1 to 0.2
Sparse or stressed crops	0.2 to 0.4
Healthy crops, grassland	0.4 to 0.7
Dense forest, peak season	0.7 to 0.9

General Formula

550 550

670 670

700 700

800 800

Sensor-Specific Formulas

Most-used sensors — click to show code below

Sensor	Provider	Formula	Band Mapping
Dragonette-1	Wyvern	`((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))`	550→Band 5, 670→Band 13, 700→Band 16, 800→Band 23
Sentinel-2	ESA	`((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))`	550→B3, 670→B4, 700→B5, 800→B7
WorldView 3	MAXAR	`((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))`	550→Green, 670→Red, 700→Red Edge, 800→NIR1
WorldView Legion	MAXAR	`((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))`	550→Green, 670→Red, 700→Red_Edge, 800→NIR1

Spectral Band Visualization — Dragonette-1

Code Examples

Adapted for Dragonette-1 bands —

mcari_mtvi2_dragonette-001.py

Frequently Asked Questions

What is the MCARI/MTVI2 (MCARI/MTVI2) and when should I use it?

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. Vegetation indices quantify plant health, biomass, and photosynthetic activity by exploiting the contrast between how plants absorb visible light for photosynthesis and reflect near-infrared radiation from their cellular structure. MCARI/MTVI2 is particularly suited for agriculture, vegetation chlorophyll assessment, crop nitrogen status prediction. The general formula is ((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)), which requires 550 and 670 and 700 and 800 spectral bands.

Which satellite sensors can I use to calculate MCARI/MTVI2?

MCARI/MTVI2 is supported by 7 satellite sensors in our database, including Dragonette-1, Dragonette-2/3, Sentinel-2, SuperView-2, WorldView 2 and 2 more. Each sensor uses different band designations — for example, Dragonette-1 uses the formula ((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)), while Dragonette-2/3 uses ((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)). Select a sensor above to see its specific band mapping.

What spectral bands does MCARI/MTVI2 require and why?

MCARI/MTVI2 requires 550 (550), 670 (670), 700 (700), 800 (800). Vegetation strongly absorbs red light for photosynthesis while reflecting near-infrared light from its mesophyll cell structure, making this contrast a reliable indicator of plant vigour.

How do I calculate MCARI/MTVI2 in Python or R?

Both Python and R code samples are provided above. In Python, use rasterio to load individual band GeoTIFF files and numpy for the arithmetic. In R, the terra package handles raster operations efficiently. The key is to load bands as floating-point arrays to avoid integer division, and to handle division-by-zero cases where the denominator equals zero. For production use, consider applying a valid data mask to exclude no-data pixels before calculation.

How does MCARI/MTVI2 compare to NDVI and other vegetation indices?

While NDVI is the most common vegetation index, MCARI/MTVI2 provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.

MCARI/MTVI2 vs other vegetation indices

Index	Name	How it differs
ARI	Anthocyanin Reflectance Index	Alternative vegetation index — different band combination
mARI	Modified Anthocyanin Reflectance Index	Refined formulation for specific conditions
ARVI	Atmospherically Resistant Vegetation Index	Atmospherically corrected version
ARVI2	Atmospherically Resistant Vegetation Index 2	Atmospherically corrected version

Related Vegetation Indices

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

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