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Applying atmospheric corrections on satellite images is an important step before using the satellite image for any further processing. These atmospheric corrections are broadly classified as either image-based or physics-based atmospheric corrections. From a plethora of such corrections, which is best suited for vegetation and snow mapping in the mountainous Himalayan region needs to be decided. Hence, in this work, we evaluated a total of eight atmospheric corrections models including 5 image-based namely DOS (dark object subtraction method), improved dark object subtraction method (DOS3), COST (cosine theta), apparent reflectance (Aref), QUAC (QUick Atmospheric Correction), and 3 physics-based methods, namely SIAC (Sensor Invariant Atmospheric Correction), 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum) and FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral …