Mr. Yanlin Wei Profile

Yanlin Wei

at Jilin Univ

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Proceedings Article | 9 September 2019 Paper

Verification and analysis of passive microwave snow depth retrieve algorithm based on snow survey data in China

Yanlin Wei, Lingjia Gu, Ruizhi Ren, Fachuan He

Proceedings Volume 11127, 1112722 (2019) https://doi.org/10.1117/12.2527371

KEYWORDS: Microwave radiation, Snow cover, Particles, Data modeling, Algorithm development, Meteorology, Sensors, Atmospheric modeling

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As an important factor for global climate change, snow affects local and global radiative balances of the earth. Excessive snow can cause destroy for global hydrological cycle and climate system. In recent years, the use of passive microwave remote sensing to retrieval snow has made greatly progress. Snow deep retrieval algorithms and snow-covered products can provide spatial and temporal information on snow cover distribution, which is an important data source for snow monitoring. The accuracy validation and contrastive analysis of snow deep retrieval algorithms are helpful to further development of snow retrieval in China. Northern Xinjiang, Qinghai-Tibet Plateau and Inner Mongolia-Northeast China are stable snow areas in China. Relying on the survey project of snow cover characteristics and distribution in China, the snow survey route has been carefully designed to continuously observe whole dry snow period (December 2017 to March). FengYun3B microwave radiation imager (FY3B-MWRI) brightness temperature data and MODIS land cover product data are used in this paper. The accuracy of snow depth retrieval algorithms, including FY operational algorithm, NASA series algorithm and GlobSnow snow water equivalent product algorithm, shows that the FY operational algorithm has the best result, and the root mean square error and deviation are 8.91cm, 6.4cm, respectively. However, the accuracy of NASA series algorithms and GlobSnow snow water equivalent product algorithm is seriously influenced by land cover type.

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