Carotenoid Reflectance Index 550

The Carotenoid Reflectance Index 550 (CRI550) was developed by Gitelson et al. (2002) to assess carotenoid content in plant leaves. It uses reciprocal reflectance at 510nm (sensitive to carotenoids) and 550nm (to remove chlorophyll effects), providing a non-destructive method for carotenoid estimation. 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. CRI550 is particularly suited for carotenoid content estimation, plant stress detection, photosynthetic efficiency assessment. The general formula is (1 / 510nm) - (1 / 550nm), which requires 510 and 550 spectral bands.

CRI550 is supported by 22 satellite sensors in our database, including BJ3A, BJ3N, Dragonette-1, Dragonette-2/3, Gaofen-1 and 17 more. Each sensor uses different band designations — for example, BJ3A uses the formula (1 / Blue) - (1 / Green), while BJ3N uses (1 / Blue) - (1 / Green). Select a sensor above to see its specific band mapping.

CRI550 requires 510 (510), 550 (550). 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, CRI550 provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.