Study on spatiotemporal dynamics of snow cover based on FY-3 meteorological satellite in typical regions of China
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1.Research Center for Remote Sensing Information and Digital Earth, College of Computer Science and Technology, Qingdao University;2.Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences;3.Max-Planck-Institute for Meteorology, 20146, Hamburg, Germany

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The Key Program of the Chinese Academy of Sciences

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    Abstract:

    As an important part of the cryosphere, snow cover has an important impact on atmospheric circulation and the hydrologic cycle. 98% of the seasonal snow in the world is located in the northern hemisphere. Eurasia continent is the main snow area in the Northern Hemisphere. China has a vast territory with nearly 50 degrees latitude across from north to south, and snow is widely distributed, which is important for studying spatial and temporal dynamics of snow cover. This paper proposes a trend extraction method based on empirical mode decomposition (EMD) based on the Snow Cover (SNC) data of FY-3 meteorological satellite and discusses the temporal and spatial characteristics and variation trends of Snow Cover in China during the past decade (2010-2019). The results show that: 1) China"s Snow Cover Frequency (SCF) has significant seasonal characteristics, which increases first and then decreases, and reaches the maximum SCF in February and March each year. The inter-annual SCF in northeast China has a significant trend of decline, but there is little change in other regions. 2) Snow Cover Rate (SCR) in Inner Mongolia and Xinjiang has decreased by 1.6% and 1.5% in the last decade, respectively. SCR in other areas has not changed significantly. SCR in main snow covered areas changed from increase to decrease in 2016.This study is of great significance to understand the spatial and temporal dynamics of typical snow cover areas in China and to further explore the relationship between snow cover and air temperature, precipitation, and other influential factors. At the same time, relevant results are obtained from the data of the Wind Cloud Meteorological Satellite, and the application of the observation data of the FY-3 satellite is strengthened.

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History
  • Received:December 28,2021
  • Revised:September 02,2022
  • Adopted:September 07,2022
  • Online: September 07,2022
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