Carter Stress Index 4

A vegetation stress index using the ratio of red edge (710nm) to NIR (760nm) reflectance. This index is particularly sensitive to changes in chlorophyll content and plant stress. 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. Ctr4 is particularly suited for plant stress monitoring, chlorophyll-related stress detection, vegetation health assessment. The general formula is re1 / nir, which requires re1 and nir spectral bands.

Ctr4 is supported by 15 satellite sensors in our database, including Dragonette-1, Dragonette-2/3, Gaofen-1, Gaofen-2, GeoEye-1 and 10 more. Each sensor uses different band designations — for example, Dragonette-1 uses the formula Band 17 / Band 21, while Dragonette-2/3 uses Band21 / Band25. Select a sensor above to see its specific band mapping.

Ctr4 requires re1 (710), 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, Ctr4 provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.