A Novel Method for Snow Depth Retrieval Using Improved Dense Medium RVoG Model

Snow depth is a fundamental parameter in hydrological and climate models, which is crucial for studying climate change, hydrological cycles, and ecosystem changes. Polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the most promising snow depth retrieval methods, which is sensitive to the shape, direction, and vertical distribution of targets. The dense medium random-volume-over-ground (DM-RVoG) model for PolInSAR has been shown to be workable for snow depth retrieval, it still suffers from the inaccuracy of the parameters representing the phase center and decorrelation. In this study, based on the backscattering mechanism of snow, a novel snow depth retrieval method is proposed to improve the DM-RVoG model using polarization decomposition and decorrelation optimization. First, the polarization decomposition is extended to obtain the ground scattering phase. Then, the coherence region boundary estimation method is put forward to obtain pure volume decorrelation. Finally, the proposed method is validated using Ku-band UAV SAR data, and the accuracy is assessed using in situ data. The correlation coefficient, root mean square error, and mean absolute error of the proposed method are 0.88, 4.98, and 4.08 cm, respectively, demonstrating significant improvements compared with the original method.