Carter Stress Index 3

A vegetation stress index that uses the ratio of reflectance at 605nm to 760nm. This index is sensitive to plant stress conditions and can detect early signs of vegetation health decline. 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. Ctr3 is particularly suited for plant stress detection, early disease detection, drought stress monitoring. The general formula is orange / nir, which requires orange and nir spectral bands.

Ctr3 is supported by 13 satellite sensors in our database, including Dragonette-1, Dragonette-2/3, Gaofen-1, Gaofen-2, GeoEye-1 and 8 more. Each sensor uses different band designations — for example, Dragonette-1 uses the formula Band 8 / Band 21, while Dragonette-2/3 uses Band12 / Band25. Select a sensor above to see its specific band mapping.

Ctr3 requires orange (605), nir (760). 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, Ctr3 provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.