Plant Water Index

A water stress index that quantifies relative water content at the leaf level. The ratio of 970nm to 900nm reflectance is sensitive to water absorption features and provides information about plant water status. Water indices exploit the strong absorption of shortwave infrared and near-infrared radiation by liquid water. They are critical for delineating water bodies, assessing moisture stress in vegetation, and monitoring hydrological changes over time. PWI is particularly suited for plant water content assessment, water stress detection, drought monitoring. The general formula is nir_970 / nir_900, which requires nir_900 and nir_970 spectral bands.

PWI is supported by 4 satellite sensors in our database, including SuperView-2, WorldView 2, WorldView 3, WorldView Legion. Each sensor uses different band designations — for example, SuperView-2 uses the formula NIR2 / NIR1, while WorldView 2 uses NIR2 / NIR1. Select a sensor above to see its specific band mapping.

PWI requires nir_900 (900), nir_970 (970). Water absorbs strongly in the near-infrared and shortwave infrared portions of the spectrum, creating a measurable contrast with shorter wavelengths that penetrate the water surface.

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.

PWI is designed to enhance water features, but dark surfaces like shadows, asphalt, and dark soils can produce similar values. For reliable water mapping, consider combining PWI with a threshold analysis and, where possible, a secondary index to reduce false positives. Time-series analysis can also help distinguish permanent water bodies from temporary dark surfaces.