Snow Depth Retrieval Based on a Multifrequency Dual-Polarized Passive Microwave Unmixing Method From Mixed Forest Observations

Due to their low spatial resolution (≥ 10 km), passive microwave data from satellites often contain many mixed pixels. This is one of the main reasons that the retrieval accuracy of snow depth data is unsatisfactory for current practical demands. This paper proposes a multifrequency dual-polarized passive microwave unmixing method for snow. The land cover type of the observation area is partitioned into broadleaf forest, needleleaf forest, annual grass vegetation, and urban types. The component brightness temperature (CBT) of each land cover type, that is, unmixed data, with a 500-m data resolution, is obtained using the proposed unmixing method. Considering that the actual snow grain size was larger than 0.4 mm in the study area, Foster's snow depth retrieval algorithm, which refines the empirical coefficient, is employed in this paper. Compared with Chang's snow depth retrieval algorithm, the overall accuracy of snow depth data improved by approximately 29.2% using Foster's algorithm. Based on Foster's algorithm, the obtained results indicate that the overall accuracy of the snow depth data improved by approximately 22.9% when using the CBT compared with the original mixed-pixel method. The accuracy of snow depth data in annual grass vegetation is improved by approximately 38%, whereas those in needleleaf forest and broadleaf forest are improved by approximately 4.6% and 4.3%, respectively. The experimental results demonstrate that the CBT effectively improves the retrieval accuracy of snow depth data when compared with the case of using only the mixed-pixel method.