Evaluation of SCATSAT-1 data for snow cover area mapping over a part of Western Himalayas

Abstract

In the present work, the potential of Scatterometer Satellite (SCATSAT-1) data (operated at Ku-band 13.515 GHz) is evaluated to estimate the binary Snow Cover Maps (SCMs) over a part of the Western Himalayas, India. Three classifiers namely, K-Means Clustering (KMC), Support Vector Machine (SVM), and Linear Spectral Mixing (LSM) has been implemented on time-series backscattered dataset during the period 2017-18. Moreover, Super-Resolution Mapping (SRM) technique has also been tested over SCATSAT-1 data with the help of LSM, termed LSM-SRM. To validate the outcomes, the Normalized Difference Snow Index (NDSI) maps were generated from Landsat-8 and Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experimental results have shown that LSM achieved higher accuracy (75.61-91.36%) as compared to SVM (72.07-85.71%) and KMC (71.79-85.47%). On the other hand, LSM-SRM offers better class category estimation (89.30�96.93%) as compared to LSM (75.61�91.36%) or other classifiers (71.79-85.71%). The present study investigates the sensitivity of Ku-band backscatter for snow conditions and performance analysis of geospatial techniques in the interpretation of backscatter data.