Soil Adjusted and Atmospherically Resistant Vegetation Index
Soil Adjusted and Atmospherically Resistant Vegetation Index for vegetation applications
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
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
| Surface type | Typical SARVI |
|---|---|
| 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
Sensor-Specific Formulas
Most-used sensors — click to show code below
| Sensor | Provider | Formula | Band Mapping |
|---|---|---|---|
| 21AT | (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) | N→NIR, R→Red, B→Blue | |
| CG Satellite | (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) | N→NIR, R→Red, B→Blue | |
| USGS/NASA | (1 + L)*(B5 - (B4 - (B4 - B2))) / (B5 + (B4 - (B4 - B2)) + L) | N→B5, R→B4, B→B2 | |
| USDA | (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L) | N→NIR, R→Red, B→Blue | |
| ESA | (1 + L)*(B8 - (B4 - (B4 - B2))) / (B8 + (B4 - (B4 - B2)) + L) | N→B8, R→B4, B→B2 | |
| MAXAR | (1 + L)*(NIR1 - (Red - (Red - Blue))) / (NIR1 + (Red - (Red - Blue)) + L) | N→NIR1, R→Red, B→Blue | |
| MAXAR | (1 + L)*(NIR1 - (Red - (Red - Blue))) / (NIR1 + (Red - (Red - Blue)) + L) | N→NIR1, R→Red, B→Blue |
Spectral Band Visualization — BJ3A
Code Examples
Adapted for BJ3A bands —
Frequently Asked Questions
What is the SARVI (Soil Adjusted and Atmospherically Resistant Vegetation Index) and when should I use it?
Soil Adjusted and Atmospherically Resistant Vegetation Index for vegetation 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. SARVI is particularly suited for vegetation. The general formula is (1 + L)*(N - (R - (R - B))) / (N + (R - (R - B)) + L), which requires N and R and B spectral bands.
Which satellite sensors can I use to calculate SARVI?
SARVI is supported by 23 satellite sensors in our database, including BJ3A, BJ3N, Dragonette-1, Dragonette-2/3, Gaofen-1 and 18 more. Each sensor uses different band designations — for example, BJ3A uses the formula (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L), while BJ3N uses (1 + L)*(NIR - (Red - (Red - Blue))) / (NIR + (Red - (Red - Blue)) + L). Select a sensor above to see its specific band mapping.
What spectral bands does SARVI require and why?
SARVI requires N (770-900 nm), R (630-690 nm), B (450-520 nm). 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 SARVI 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 SARVI compare to NDVI and other vegetation indices?
While NDVI is the most common vegetation index, SARVI provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.
SARVI 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
Need help choosing?
Ask our AI assistant for sensor recommendations, code examples, or how SARVI compares to other indices for your specific use case.