Normalized Difference Water Index (McFeeters)

The Normalized Difference Water Index (NDWI) proposed by McFeeters (1996) is designed to delineate open water features and enhance their presence in remotely-sensed digital imagery. It uses reflected near-infrared radiation and visible green light to enhance water features while eliminating soil and terrestrial vegetation features. 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. NDWI is particularly suited for open water feature detection, water body mapping, water content monitoring. The general formula is (Green - NIR) / (Green + NIR), which requires Green and NIR spectral bands.

NDWI is supported by 21 satellite sensors in our database, including BJ3A, BJ3N, Dragonette-2/3, Gaofen-1, Gaofen-2 and 16 more. Each sensor uses different band designations — for example, BJ3A uses the formula (Green - NIR) / (Green + NIR), while BJ3N uses (Green - NIR) / (Green + NIR). Select a sensor above to see its specific band mapping.

NDWI requires Green (560), NIR (830). 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.

NDWI 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 NDWI 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.