The Potential to Estimate Soil Moisture Based on sn_rnx Data

  • Juan Shan
  • Songhua Yan
  • Xueqian Luo
  • Xingxing Li
  • Hancheng Yuan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 388)


Using Global Navigation Satellite System (GNSS) signals of BeiDou navigation satellite system (BDS) and global position system (GPS) for soil moisture estimation is an emerging technology in microwave remote sensing since soil moisture is an important index in regional water cycle research. Based on the developed forward multipath model, this study first investigates the feasibility of soil moisture estimation based on GNSS interference signals; then compares the difference between the Signal-to-Ratio (SNR) data and the signal strength (sn_rnx) data from GPS. After that, this paper analyzes the experimental data from BDS receivers and CORS stations. The analyzed results show that both fitted phase of BDS SNR data and GPS sn_rnx data will increase when the soil moisture increases, and the phase will decrease when soil moisture decreases, and the method of using sn_rnx data can obtain the change trend of soil moisture and can be used as an alternative to solve the problem that the SNR data is unavailable in many GNSS data sets.


GNSS Soil moisture Interference signal SNR Sn_rnx 



The authors would like to acknowledge the support of the national natural science foundation of China (NSFC 41571420).


  1. 1.
    Jin SG, Feng GP, Gleason S (2011) Remote sensing using GNSS signals: current status and future directions. Adv Space Res 47(10):1645–1653CrossRefGoogle Scholar
  2. 2.
    Jacobson MD (2010) Snow-covered lake ice in GPS multipath reception—theory and measurement. Adv Space Res 46(2):221–227CrossRefGoogle Scholar
  3. 3.
    Yang Y, Li J, Wang A, Xu J, He H, Guo H et al (2014) Preliminary assessment of the navigation and positioning performance of BeiDou regional navigation satellite system. Sci Chin (Earth Sci) 01:144–152Google Scholar
  4. 4.
    Xu A, Xu Z, Ge M, Xu X, Zhu H, Sui X (2013) Estimating zenith tropospheric delays from BeiDou navigation satellite system observations. Sens Basel 13(4):4514–4526CrossRefGoogle Scholar
  5. 5.
    Guo P, Shi J, Du J, Liu Q (2012) A new method for estimation of bare surface soil moisture with L-band radiometer. IEEE, pp 658–661Google Scholar
  6. 6.
    Xiang Z, Chen N, Chen Z (2014) Spatial pattern and temporal variation law-based multi-sensor collaboration method for improving regional soil moisture monitoring capabilities. Remote Sens Basel. 6(12):12309–12333CrossRefGoogle Scholar
  7. 7.
    Rodriguez-Alvarez N, Bosch-Lluis X, Camps A, Vall-Llossera M, Valencia E, Marchan-Hernandez JF et al (2009) Soil moisture retrieval using GNSS-R techniques: experimental results over a bare soil field. IEEE Trans Geosci Remote Sens 47(11):3616–3624CrossRefGoogle Scholar
  8. 8.
    Zavorotny VU, Larson KM, Braun JJ, Small EE, Gutmann ED, Bilich AL (2010) A physical model for GPS multipath caused by land reflections: toward bare soil moisture retrievals. IEEE J Sel Topics Appl Earth Obs Remote Sens 3(1):100–110CrossRefGoogle Scholar
  9. 9.
    Chew CC, Small EE, Larson KM, Zavorotny VU (2014) Effects of near-surface soil moisture on GPS SNR data: development of a retrieval algorithm for soil moisture. IEEE Trans Geosci Remote 52:537–543CrossRefGoogle Scholar
  10. 10.
    Wu ASS, Yang L (2012) Soil moisture retrieval by active/passive microwave remote sensing data. SPIE Remote SensingGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Juan Shan
    • 1
  • Songhua Yan
    • 1
  • Xueqian Luo
    • 1
  • Xingxing Li
    • 1
  • Hancheng Yuan
    • 1
  1. 1.School of Electronic InformationWuhan UniversityWuhanChina

Personalised recommendations