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.

积雪密度是积雪水文势的重要指标之一。现有的利用合成孔径雷达（SAR）数据反演干雪密度的算法的应用受限于单一的散射机制、较小的地形波动或窄的入射角范围。在这项研究中，提出了一种改进的方法来从具有广泛粗糙度和局部入射角的 C 波段 SAR 数据中反演干雪密度。该方法同时考虑了雪地界面散射和体积散射。首先，基于使用高级积分方程模型（AIEM）的模拟和回归分析，获得了雪地界面的反向散射与相对介电常数之间的关系。然后将一阶体散射模型得到的体后向散射与相对介电常数之间的经典关系纳入该方法。为了比较，通过AIEM模型和回归分析重新定义了Shi算法的系数，并将最初为L波段开发的Shi算法修改为C波段。实验选取了2013年12月12-17日在玛纳斯河流域获得的RADARSAT-2数据和2018年1月17日在克兰河流域获得的C波段高分3数据，验证了所提方法的适用性。不同条件下的方法。比较了该方法、Singh算法和修正Shi算法在玛纳斯河流域的反演结果。在玛纳斯河流域的结果表明，三种方法的实测和估计干雪密度之间的相关系数 (Rs) 分别为 0.868、0.694 和 0.653。均方根误差 (RMSE) 为 31.1 kg m^-3、59.1 kg m ^-3和 64.7 kg m ^-3，平均相对误差 (MRE) 分别为 12.9%、21.9% 和 25.5%。使用所提出的方法，克兰河流域相应的 R、RMSE 和 MRE 分别为 0.717、57.2 kg m ^-3和 27.1%。结果证明，该方法优于其他两种算法，能够有效地反演不同C波段SAR数据和不同区域下的干雪密度。