Detection and validation of spatiotemporal snow cover variability in the himalayas using Ku-band (13.5 GHz) SCATSAT-1 data

Abstract

The present study evaluates the potential of Ku-band Scatterometer Satellite-1 (SCATSAT-1) for quantification of spatiotemporal variability in snow cover area (SCA) over Himalayas (Himachal Pradesh) India. The SCA has been measured using dual-polarized (HH and VV) backscattered SCATSAT-1 data. Two classification approaches, i.e., Linear Mixer Model (LMM) and Artificial Neural Network (ANN) model have been used for the present study. Both available backscatter coefficients sigma-naught σ0 and gamma-naught γ0 have been considered for the estimation of SCA. To compute the seasonal snow cover trends for winter (2016‒2017 and 2017‒2018), a post-classification comparison (PCC) based change detection approach has been demonstrated on the classified dataset (LMM and ANN). The SCA maps have been validated using reference snow cover maps generated from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor. The final change-category maps have effectively mapped the snow cover variations with accuracy in between 83.01% and 95.33%. The results indicate the suitability of SCATSAT-1 for estimating the magnitude of snow extent over the Himalayas.