Ferrous Silicates Index

Geological index for detecting ferrous silicate minerals in rocks and geological formations. Useful for identifying iron-bearing silicate minerals and mafic rock compositions. 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. FSI is particularly suited for geology, mineral exploration, iron ore detection. The general formula is SWIR5 / SWIR4, which requires SWIR4 and SWIR5 spectral bands.

FSI 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 B7 / B6, while Sentinel-2 uses B12 / B11. Select a sensor above to see its specific band mapping.

FSI requires SWIR4 (1600-1700 nm), SWIR5 (2145-2185 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 detecting ferrous silicate minerals in rocks and geological formations. Useful for identifying iron-bearing silicate minerals and mafic rock compositions. 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.