reNDVI — Red Edge NDVI | Spectral Index Calculator

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.

(re2 - re1) / (re2 + re1)

Bands: re1, re2
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
Chlorophyll content estimation
Vegetation stress detection

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

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 reNDVI
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

re1 710

re2 750

Sensor-Specific Formulas

Most-used sensors — click to show code below

Sensor	Provider	Formula	Band Mapping
Dragonette-1	Wyvern	`(Band 20 - Band 17) / (Band 20 + Band 17)`	re1→Band 17, re2→Band 20
Sentinel-2	ESA	`(B6 - B5) / (B6 + B5)`	re1→B5, re2→B6

Spectral Band Visualization — Dragonette-1

Code Examples

Adapted for Dragonette-1 bands —

rendvi_dragonette-001.py

Frequently Asked Questions

What is the reNDVI (Red Edge NDVI) and when should I use it?

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. 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. reNDVI is particularly suited for chlorophyll content estimation, vegetation stress detection, crop health monitoring. The general formula is (re2 - re1) / (re2 + re1), which requires re1 and re2 spectral bands.

Which satellite sensors can I use to calculate reNDVI?

reNDVI is supported by 4 satellite sensors in our database, including Dragonette-1, Dragonette-2/3, GeoEye-1, Sentinel-2. Each sensor uses different band designations — for example, Dragonette-1 uses the formula (Band 20 - Band 17) / (Band 20 + Band 17), while Dragonette-2/3 uses (Band24 - Band21) / (Band24 + Band21). Select a sensor above to see its specific band mapping.

What spectral bands does reNDVI require and why?

reNDVI requires re1 (710), re2 (750). 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 reNDVI 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 reNDVI compare to NDVI and other vegetation indices?

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

reNDVI 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

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.

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