Nonlinear vegetation index

A vegetation index that uses a nonlinear relationship between NIR and red bands to reduce the saturation effect at high biomass levels. The squared NIR term helps maintain sensitivity to vegetation changes in dense canopies. 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. NLI is particularly suited for dense vegetation monitoring, lai estimation with reduced saturation, forest canopy analysis. The general formula is (NIR² - Red) / (NIR² + Red), which requires red and nir spectral bands.

NLI is supported by 23 satellite sensors in our database, including BJ3A, BJ3N, Dragonette-1, Dragonette-2/3, Gaofen-1 and 18 more. Each sensor uses different band designations — for example, BJ3A uses the formula (NIR² - Red) / (NIR² + Red), while BJ3N uses (NIR² - Red) / (NIR² + Red). Select a sensor above to see its specific band mapping.

NLI requires red (640-680), nir (780-900). 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, NLI provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.