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)


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.


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



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).


  1. 1.
    Zhang Q, Li J et al (2005) GPS measurement principle & application. China Science Publishing & Media Ltd, BeijingGoogle Scholar
  2. 2.
    Zhang B, Huang J, Su L (2003) A research on using SNR values to mitigate carrier phase multipath in GPS surveying. Sci Surv Mapp 28(3):32–35Google Scholar
  3. 3.
    Wu Y, Chen X, Wu C (2008) Mitigation of muti-path effect using SNR values. Geomat Inf Sci Wuhan Univ 33(8):842–845Google Scholar
  4. 4.
    Bilich A, Larson KM, Axelrad P (2004) Observations of signal-to-noise ratios (SNR) at geodetic GPS site CASA: implications for phase multipath. Proc Cent Eur Geodyn Seismol 23:77–83Google Scholar
  5. 5.
    Liu J, Shao L, Zhang X (2007) Advances in GNSS-R studies and key technologies. Geomat Inf Sci Wuhan Univ 32(11):955–960CrossRefGoogle Scholar
  6. 6.
    Larson, KM, Gutmann, ED, Zavorotny VU et al (2009) Can we measure snow depth with GPS receivers? Geophys Res Lett 36(17). doi: 10.1029/2009GL039430
  7. 7.
    Gutmann ED, Larson KM, Williams MW et al (2012) Snow measurement by GPS interferometric reflectometry: an evaluation at Niwot Ridge, Colorado. Hydrol Process 26(19):2951–2961CrossRefGoogle Scholar
  8. 8.
    Larson KM, Small EE, Gutmann ED et al (2008) Use of GPS receivers as a soil moisture network for water cycle studies. Geophys Res Lett 35(24). doi: 10.1029/2008GL036013
  9. 9.
    Small EE, Larson KM, Braun JJ (2010) Sensing vegetation growth with reflected GPS signals. Geophys Res Lett 37(12). doi: 10.1029/2010GL042951
  10. 10.
    Ao Minsi H, Hu Y, Liu Y et al (2012) Inversion of soil moisture fluctuation based on signal-to-noise ratio of global positioning system. J Geomat Sci Technol 29(2):140–143Google Scholar
  11. 11.
    Ao M, Zhu J, Hu Y, Zeng Y, Liu Y (2015) Comparative experiments on soil moisture monitoring with GPS SNR observations. Geomat Inf Sci Wuhan Univ 40(01):117–121Google Scholar
  12. 12.
    Wu J, Yang R (2012) Measuring water surface height by using reflected signal of geodetic-quality GPS receiver. J Geod Geodyn 32(6):135–138Google Scholar
  13. 13.
    Yang Y, Hao X, Peng B et al (2014) Study on vegetation coverage with GPS mutipath effects. J Geomat 39(2):52–54Google Scholar
  14. 14.
    Wan W, Larson KM, Small EE et al (2015) Using geodetic GPS receivers to measure vegetation water content. GPS Solut 19(2):237–248CrossRefGoogle Scholar
  15. 15.
    Xie G (2009) Principles of GPS and receiver design. Publishing House of Electronics Industry, BeijingGoogle Scholar
  16. 16.
    Xu B, Yang T, Tan B et al (2011) The simulate study of detection based on Lomb-Scargle algorithm. Nucl Electron Detect Technol 31(6):702–705Google Scholar

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

Personalised recommendations