Abstract:

Due to the influence of complex terrain and canopy structure in forest, the accuracy of snow depth retrieval based on passive microwave remote sensing data is generally low. Based on the representative semi- empirical snow depth retrieval algorithm and combined with meteorological observation data, an optimization algorithm of semi-empirical snow depth retrieval in forest area in Northeast China was established in this paper. In this algorithm, the permittivity of vegetation varies with temperature and the accuracy of snow depth retrieval in forest is greatly improved. Compared with other representative semi-empirical algorithms, the RMSE(Root- Mean-Square Error) of the proposed algorithm is reduced by 2. 3 cm, Bias by 3. 7 cm on average and correlation (R) improved by 0. 11 on average. Compared with the commonly used snow depth retrieval algorithm based on machine learning, the RMSE of the proposed algorithm is reduced by 2. 17 cm, Bias by 1. 67 cm on average and R improved by 0. 22 on average.

Key words: passive microwave, snow depth retrieval, forest, air temperature