MCARI/OSAVI750
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.
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 health 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
What the values mean
| Surface type | Typical MCARI/OSAVI750 |
|---|---|
| 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 |
|---|---|---|---|
| Wyvern | ((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)) | 550→Band 5, 705→Band 16, 750→Band 20 | |
| ESA | ((B6 - B5) - 0.2 * (B6 - B3) * (B6 / B5)) / ((1 + 0.16) * (B6 - B5) / (B6 + B5 + 0.16)) | 550→B3, 705→B5, 750→B6 |
Spectral Band Visualization — Dragonette-1
Code Examples
Adapted for Dragonette-1 bands —
Frequently Asked Questions
What is the MCARI/OSAVI750 (MCARI/OSAVI750) and when should I use it?
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. 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/OSAVI750 is particularly suited for chlorophyll content estimation, vegetation health assessment, red-edge analysis. The general formula is ((750nm - 705nm) - 0.2 * (750nm - 550nm) * (750nm / 705nm)) / ((1 + 0.16) * (750nm - 705nm) / (750nm + 705nm + 0.16)), which requires 550 and 705 and 750 spectral bands.
Which satellite sensors can I use to calculate MCARI/OSAVI750?
MCARI/OSAVI750 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 16) - 0.2 * (Band 20 - Band 5) * (Band 20 / Band 16)) / ((1 + 0.16) * (Band 20 - Band 16) / (Band 20 + Band 16 + 0.16)), while Dragonette-2/3 uses ((Band24 - Band20) - 0.2 * (Band24 - Band9) * (Band24 / Band20)) / ((1 + 0.16) * (Band24 - Band20) / (Band24 + Band20 + 0.16)). Select a sensor above to see its specific band mapping.
What spectral bands does MCARI/OSAVI750 require and why?
MCARI/OSAVI750 requires 550 (550), 705 (705), 750 (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 MCARI/OSAVI750 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/OSAVI750 compare to NDVI and other vegetation indices?
While NDVI is the most common vegetation index, MCARI/OSAVI750 provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.
MCARI/OSAVI750 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 MCARI/OSAVI750 compares to other indices for your specific use case.