ZHANG Shuangcheng, DAI Kaiyang, NAN Yang, ZHANG Qin, QU Wei, LI Zhenyu, ZHAO Yinghui. Preliminary Research on GNSS-MR for Snow Depth[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2): 234-240. DOI: 10.13203/j.whugis20150236
Citation: ZHANG Shuangcheng, DAI Kaiyang, NAN Yang, ZHANG Qin, QU Wei, LI Zhenyu, ZHAO Yinghui. Preliminary Research on GNSS-MR for Snow Depth[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2): 234-240. DOI: 10.13203/j.whugis20150236

Preliminary Research on GNSS-MR for Snow Depth

Funds: 

The National Natural Science Foundation of China 41104019

The National Natural Science Foundation of China 41674001

The National Natural Science Foundation of China 41731066

the Special Fund for Basic Scientific Research of Central Colleges 310826172202

More Information
  • Author Bio:

    ZHANG Shuangcheng, PhD, specializes in the theories and methods of GNSS ground environment monitoring. E-mail: shuangcheng369@vip.163.com

  • Received Date: January 14, 2016
  • Published Date: February 04, 2018
  • Snow is a critical storage component in the hydrologiccycle; accurate measurements of snow depth and snow water storage are very important for global climate research. As research and application of GNSS deepens, GNSS-MR based multipath has emerged as a new technology of remote sensing for environmental monitoring of vegetation, soil moisture, snow depth, sea levels, ,and so on. The characteristics of change in SNR data are analyzed and the fundamental principles of GNSS-MR based SNR and calculation flow chart are introduced. Experiments with GPS data collected for 17 days at the NWOT station, Colorado, verify the validity of GNSS-MR based SNR. The retrieval result was consistent with the snow depth recorder; the standard deviation was 0.07m. Results also show that the GNSS-MR for snow depth is very effective and useful for environmental monitoring with abundantglobalGNSS stations.
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