Host Rock Index

Geological index for characterizing host rock properties and lithologic mapping. Uses SWIR bands to identify rock types and mineral compositions, particularly useful for geological surveys. Geological indices identify mineral compositions and lithological features by targeting diagnostic absorption features in shortwave infrared wavelengths. Different minerals produce unique spectral signatures that these indices isolate. HRI is particularly suited for geology, rock identification, metal detection. The general formula is SWIR5 / SWIR6, which requires SWIR5 and SWIR6 spectral bands.

HRI is supported by 1 satellite sensor in our database, including WorldView 3. Each sensor uses different band designations — for example, WorldView 3 uses the formula SWIR5 / SWIR6. Select a sensor above to see its specific band mapping.

HRI requires SWIR5 (2145-2185 nm), SWIR6 (2185-2225 nm). These specific wavelength regions correspond to diagnostic mineral absorption features caused by electronic transitions and vibrational overtones in crystal lattices.

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.

Geological index for characterizing host rock properties and lithologic mapping. Uses SWIR bands to identify rock types and mineral compositions, particularly useful for geological surveys. For accurate mineral identification, this index should be used alongside other geological indices and validated with field samples or known geology maps. Spectral unmixing or supervised classification using multiple indices typically yields more reliable results than any single index alone.