Development of a novel approach for snow wetness estimation using hybrid polarimetric RISAT-1 SAR datasets in North-Western Himalayan region

Snow wetness estimates are critical inputs in the understanding of snow hydrological processes. With early warming in winters in the Himalayas, snowpack shows early signs of melting in February with increased snow wetness. Typically, in February, the snowpack comprises a mix of dry and wet snow layers. In the literature, methods for quantitative analysis of snow wetness are mostly based on fully polarimetric synthetic aperture radar (SAR) data. Methods based on the hybrid polarimetric SAR (PolSAR) data for snow monitoring are virtually inexistent. This study proposes a novel methodology for the estimation of snow wetness utilizing the C-band hybrid polarimetric RISAT-1 SAR dataset. Using radar remote sensing to analyze the behavior of such a snowpack requires information on the surface and volume scattering characteristics. The modeled generalized surface and volume scattering parameters (alpha), and (gamma), based on the X-Bragg's reflection coefficients and Fresnel transmission coefficients, are used for the inversion of surface and volume snow permittivities, respectively. The investigations are carried out for February 2014 for a study area in the Manali region in Himachal Pradesh, India. The retrieved snow estimates showed a coefficient of determination of 0.86 and a root mean square error of 0.667 with respect to in-situ measurements. Further, it was observed that the snow wetness estimates derived from the proposed method using RISAT-1 dataset outperformed the estimates based on fully polarimetric RADARSAT-2 dataset using the conventional Shi and Dozier method.