Modified Normalized Difference Water Index

The Modified Normalized Difference Water Index (MNDWI) was developed by Xu (2006) as an improvement over the original NDWI. By substituting the NIR band with SWIR, MNDWI can enhance open water features while efficiently suppressing noise from built-up areas, vegetation, and soil. This makes it particularly suitable for water detection in urban environments. 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. MNDWI is particularly suited for open water feature detection, water body mapping, urban water monitoring. The general formula is (Green - SWIR) / (Green + SWIR), which requires Green and SWIR spectral bands.

MNDWI is supported by 4 satellite sensors in our database, including Landsat 8/9, Sentinel-2, SuperView-2, WorldView 3. Each sensor uses different band designations — for example, Landsat 8/9 uses the formula (B3 - B6) / (B3 + B6), while Sentinel-2 uses (B3 - B11) / (B3 + B11). Select a sensor above to see its specific band mapping.

MNDWI requires Green (520-600), SWIR (1550-1750). 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.

MNDWI 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 MNDWI 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.