The spectral characterization of geographic landscapes is vital for their accurate mapping using remote sensing data. This can be done through spectral profiling, as demonstrated here, to characterize the surface facies of the Gangotri and neighbouring glaciers, central Himalaya. The satellite-derived reflectance curves were compared with the in-situ and published (validation) data. The study attempts to understand the influence of certain parameters such as the satellite sensor’s radiometric resolution, timing of data acquisition (seasonality), and surface morphology on glacier/snow–ice facies identification. Results show that the first two parameters complement each other in identifying the snow–ice facies accurately. High radiometric resolution (HRR) data concurred closely with the validation dataset and had higher mean entropy values over the glaciated areas than low radiometric resolution (LRR) ablation data. Presence of seasonal snow and degree of surface melting show considerable influence on satellite-derived reflectances of glacier facies. Our findings assert the usage of HRR ablation data in appraising the interannual and seasonal variability of glacier facies. While HRR post-ablation data overestimates the reflectance of snow–ice facies, LRR post-ablation data have limitations in their discrimination. Certain morphology and resultant features, such as crevasses and shadows, induce underestimation of the satellite-derived reflectances, creating confusion among the snow and ice facies. This spectral confusion can, however, be resolved by the use of ancillary data. Elevation, temperature, and band ratios/spectral indices are helpful in segregating snow–ice facies, while slope, band ratios, temperature, and texture measures effectively discriminate the other facies.
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