Modified Chlorophyll Absorption in Reflectance Index

Vegetation index designed to estimate chlorophyll content with reduced sensitivity to non-photosynthetic vegetation and soil background effects. 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 is particularly suited for vegetation analysis, chlorophyll assessment, crop health monitoring. The general formula is ((700nm - 670nm) - 0.2 * (700nm - 550nm)) * (700nm / 670nm), which requires Green and Red and RedEdge spectral bands.

MCARI is supported by 3 satellite sensors in our database, including Dragonette-1, Dragonette-2/3, Sentinel-2. 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), while Dragonette-2/3 uses ((Band20 - Band17) - 0.2 * (Band20 - Band9)) * (Band20 / Band17). Select a sensor above to see its specific band mapping.

MCARI requires Green (550 nm), Red (670 nm), RedEdge (700 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.

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.

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