An improved approach of dry snow density estimation using C-band synthetic aperture radar data

Abstract

Snow density is one of the important indicators of snow cover hydrological potential. The application of existing algorithms for retrieving dry snow density using synthetic aperture radar (SAR) data is limited by single scattering mechanism, small terrain fluctuation or narrow incidence angle range. In the study, an improved approach was proposed to retrieve dry snow density from C-band SAR data with a wide range of roughness and local incidence angles. Both the snow-ground interface scattering and volume scattering were considered in the approach. First, the relationship between the backscattering at the snow-ground interface and relative permittivity was obtained based on simulation using the Advanced Integral Equation Model (AIEM) and regression analysis. Then the classical relationship between the volume backscattering and relative permittivity obtained by the first-order volume scattering model was incorporated into the approach. For comparison, the coefficients of the Shi algorithm were redefined by the AIEM model and regression analysis, and the Shi algorithm initially developed for L-band was modified for C-band. In experiments, the RADARSAT-2 data obtained in the Manasi River Basin on December 12–17, 2013 and the C-band GaoFen-3 data obtained in the Kelan River Basin on January 17, 2018 were selected to validate the applicability of the proposed approach under different conditions. The inversion results in the Manasi River Basin using the proposed approach, Singh algorithm, and modified Shi algorithm were compared. The results in the Manasi River Basin show that the correlation coefficients (Rs) between the measured and estimated dry snow density are 0.868, 0.694, and 0.653 for the three methods, respectively. The root mean square errors (RMSEs) are 31.1 kg m−3, 59.1 kg m−3, and 64.7 kg m−3, respectively, and the mean relative errors (MREs) are 12.9%, 21.9%, and 25.5%, respectively. The corresponding R, RMSE, and MRE in the Kelan River Basin using the proposed approach are 0.717, 57.2 kg m−3, and 27.1%, respectively. The results prove that the dry snow density under different C-band SAR data and different areas can be effectively retrieved using the proposed approach superior to the other two algorithms.