Estimation of Snow Density and Permittivity Using ALOS PALSAR-2 Fully Polarimetry

Snow plays an important role in Earth’s energy budget and a critical component for seasonal snow melt supply in the Himalayas region. Snow density and permittivity are particularly important for retrieving snowpack characteristics during early to peak winter. However, estimating snow density cause to early melt and refreeze cycles, is challenging compared to winter fresh snowfall. In conventional methods, snow density estimation is typically based on fully polarimetry synthetic aperture radar (SAR) datasets, which do not account the surface scattering component for the inversion of permittivity. In this study, we estimate the snow density and permittivity using fully polarimetric L-band ALOS (Advanced Land Observing Satellite) PALSAR-2 dataset by synthesizing hybrid polarimetric surface and volume characteristics. This study incorporates the surface and volume scattering parameters based on Stoke’s formulation for the inversion of surface and volume snow permittivity. The results for the modeled snow density and permittivity indicated a coefficient of determination $\left(\mathrm{R}^{2}\right)$ of 0.70 and 0.79, respectively, with root mean square errors (RMSE) of 0.09 and 0.21, respectively, compared to field observations. The field campaign was conducted on February 5, 2023, corresponding to satellite passes at Natha-Top (lower-mid altitude region), Jammu & Kashmir, India.