Tasselled Cap - wetness

The wetness component of the Tasselled Cap transformation, which relates to soil and canopy moisture. Positive values indicate wet conditions while negative values indicate dry conditions. 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. WET is particularly suited for soil moisture assessment, wetland mapping, irrigation monitoring. The general formula is 0.1509 * Blue + 0.1973 * Green + 0.3279 * Red + 0.3406 * NIR - 0.7112 * SWIR1 - 0.4572 * SWIR2, which requires blue and green and red and nir and swir1 and swir2 spectral bands.

WET is supported by 3 satellite sensors in our database, including Landsat 8/9, Sentinel-2, WorldView 3. Each sensor uses different band designations — for example, Landsat 8/9 uses the formula 0.1509 * B1 + 0.1973 * B3 + 0.3279 * B4 + 0.3406 * B5 - 0.7112 * B6 - 0.4572 * B7, while Sentinel-2 uses 0.1509 * B1 + 0.1973 * B3 + 0.3279 * B4 + 0.3406 * B8 - 0.7112 * B11 - 0.4572 * B12. Select a sensor above to see its specific band mapping.

WET requires blue (450-520), green (520-600), red (630-690), nir (760-900), swir1 (1550-1750), swir2 (2080-2350). 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.

WET 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 WET 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.