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Preliminary Research on Snow Depth Monitoring with GPS SNR

  • Kaiyang Dai
  • Qin Zhang
  • Shuangcheng Zhang
  • Ning Zhang
  • Kai Liu
  • Xiaowei Hou
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 388)

Abstract

The GNSS observations quality can be affected by the environment around the station. Using the signal-to-noise ratios to correct the observation influenced by multipath effect has attracted attention of many scholars. Meanwhile, using GNSS-MR technology based on SNR information to study environmental changes around the station has become a new research subject. And that makes the GNSS active in the field of remote sensing. First, the relationship between GPS multipath and SNR is analyzed in this paper. Then, based on the SNR and the signal amplitude variation, the basic principle of GPS-MR technology is given. GPS observation data in P351 and SNOTEL station snow data are used to evaluate the effectiveness of the algorithm. Also, satellite reflection point trajectory and satellite selection of snow depth detection are further analyzed. The experimental results show that: GPS-MR can be used to measure the snow depth, and its detection precision is 0.08 m. GPS-MR technology not only can make full use of multipath effect, but also provides a development space for GPS technology using in the surface environment monitoring.

Keywords

GNSS-MR (GNSS multipath reflectometry) SNR (signal-to-noise ratio) Snow depth Lomb-Scargle spectrum analysis 

Notes

Acknowledgment

Thanks to the United States NSF, USGS, and NASA who launched the Earthscope and authorized the use of GPS data. Thanks to the PBO H2O team to provide reference resources. Thanks to the NRCS to provide measured snow depth data. Heartfelt thanks to the editors and anonymous referee experts provide valuable opinions and suggestions for this article.

Fund Project The National Natural Science Foundation of China (41104019, 41202189, 41304016).

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Kaiyang Dai
    • 1
  • Qin Zhang
    • 1
  • Shuangcheng Zhang
    • 1
  • Ning Zhang
    • 1
  • Kai Liu
    • 1
  • Xiaowei Hou
    • 1
  1. 1.College of Geology Engineering and GeomanticChang’an UniversityXi’anChina

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