Land surface characterization using BeiDou signal-to-noise ratio observations
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China’s BeiDou Navigation Satellite System (BDS) is providing new opportunities for GNSS reflectometry-related applications. We give the first and comprehensive description of the feasibility and potential of using BDS signal-to-noise ratio (SNR) data to characterize land surface in terms of the volumetric soil moisture (VSM), vegetation water content (VWC) and snow depth. BDS SNR-derived interferogram metrics (phase φ, amplitude A, and effective reflector height h) are investigated, and their correlations to the corresponding land surface parameters are established. Data collected from a geodetic-quality BDS/GPS compatible receiver for approximately 300-day period were used to validate the VSM retrieval. Results show that both BDS B1 and B2 frequencies can perform well to reflect the fluctuations of the VSM. Specifically, the B2-derived phase φ exhibits a slightly higher correlation with in situ VSM than that of B1 (R = 0.83 vs. R = 0.80), and the B2-derived amplitude A also exhibits a higher correlation with MODIS NDVI than that of B1 (R = 0.49 vs. R = 0.53); whilst for snow, the B1 and B2 results indicate qualitative agreement with concurrent in situ snow depth measurements. Furthermore, similar estimation performance can be obtained by comparing the results of BDS B1 and B2 against GPS L2C and L5. Therefore, BDS could be a new and powerful data source with comparable potential as GPS for effectively characterizing high-temporal resolution land surface.
KeywordsBeiDou Navigation Satellite System (BDS) Signal-to-noise ratio (SNR) Volumetric soil moisture (VSM) Vegetation water content (VWC) Snow depth
This study was jointly supported by the National Natural Science Foundation of China (NSFC) projects (Grant Nos. 41501360, 91437214, and 41401377), the Open Research Fund of Key Laboratory of Tibetan Environmental Changes and Land Surface Processes, Chinese Academy of Sciences (Grant No. TEL201503), and the Open Research Fund of State Key Laboratory of Hydroscience and Engineering, Tsinghua University (sklhse-2017-A-02).
- Clarizia MP, Gommenginger C, Gleason S, Galdi C, Unwin M (2008) Global navigation satellite system-reflectometry (GNSS-R) from the UK-DMC satellite for remote sensing of the ocean surface. In: Geoscience and remote sensing symposium, 2008. IGARSS 2008, vol 1. IEEE, Boston, Mass, pp I-276–I-279Google Scholar
- Larson KM, Small EE (2016) Estimation of snow depth using L1 GPS signal-to-noise ratio data. IEEE J STARS 9(10):4802–4808Google Scholar
- Larson KM, Braun JJ, Small EE, Zavorotny VU, Gutmann ED, Bilich AL (2010) GPS multipath and its relation to near-surface soil moisture content. IEEE J STARS 3(1):91–99Google Scholar
- Li W, Yang D, Fabra F, Cao Y, Yang W (2014) Typhoon wind speed observation utilizing reflected signals from BeiDou GEO satellites. In: China satellite navigation conference (CSNC) 2014 proceedings, vol I. Springer, Berlin, pp 191–200Google Scholar
- Masters D, Zavorotny V, Katzberg S, Emery W (2000) GPS signal scattering from land for moisture content determination. In: Geoscience and remote sensing symposium, 2000, vol 7. IEEE, Honolulu, HI, pp 3090–3092Google Scholar
- Qian X, Jin S (2016) Estimation of snow depth from GLONASS SNR and phase-based multipath reflectometry. IEEE J STARS 9(10):4817–4823Google Scholar
- Roussel N, Frappart F, Ramillien G, Darrozes J, Baup F, Lestarquit L, Ha MC (2016) Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2 to 70. IEEE J STARS 9(10):4781–4794Google Scholar
- Wang DW, Huang CL, Gu J (2016) Impact of pentration depth on L-band microwave brightness temperature in arid region based on L-MEB model. Remote Sens Technol Appl 31(3):580–589Google Scholar