Band Ratio
A band ratio divides the reflectance values of one spectral band by another to highlight specific surface properties such as vegetation health, mineral composition, or water turbidity while reducing the effects of illumination and topographic variation.
A band ratio is a simple yet powerful image processing technique in remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... where pixel values from one spectral bandSpectral BandA spectral band is a specific range of wavelengths within the electromagnetic spectrum captured by a remote sensing s... are divided by the corresponding values from another band. This mathematical operation enhances spectral differences between materials while suppressing variations caused by topography, shadows, and uneven illumination. Band ratios form the basis of many widely used spectral indices. How band ratios workBecause both the numerator and denominator are affected similarly by illumination changes and atmospheric conditions, the ratio partially cancels these effects, emphasizing inherent spectral contrasts. For example, the ratio of near-infrared to red reflectance exploits the strong contrast healthy vegetation shows between these bands, forming the basis of NDVINDVIThe Normalized Difference Vegetation Index (NDVI) is a widely used spectral index that quantifies vegetation health a... and related vegetation indices. Common applicationsBand ratios are used for vegetation analysis (NIR/Red), iron oxide mapping (Red/Blue), clay mineral detection (SWIR1/SWIR2), and water body delineation (Green/NIR). Geologists routinely use multiple band ratios displayed as color composites to map lithology and alteration zones. In agriculture, band ratios help identify nutrient stress and irrigation needs. Band ratios remain one of the most accessible and effective tools in remote sensing analysis. Their simplicity, robustness to illumination effects, and broad applicability across disciplines make them a foundational technique for extracting thematic information from multispectral imagery.
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