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Snow Depth Estimation with GNSS-R Dual Receiver Observation

1
School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 2056; https://doi.org/10.3390/rs11172056
Received: 1 July 2019 / Revised: 25 August 2019 / Accepted: 27 August 2019 / Published: 1 September 2019
Two estimation methods using a dual GNSS (Global Navigation Satellite System) receiver system are proposed. The dual-frequency combination method combines the carrier phase observations of dual-frequency signals, whereas the single-frequency combination method combines the pseudorange and carrier phase observations of a single-frequency signal, both of which are geometry-free strictly combination and free of the effect of ionospheric delay. Theoretical models are established in the offline phase to describe the relationship between the spectral peak frequency of the combined sequence and the antenna height. A field experiment was conducted recently and the data processing results show that the root mean squared error (RMSE) of the dual-frequency combination method is 5.04 cm with GPS signals and 6.26 cm with BDS signals, which are slightly greater than the RMSE of 4.16 cm produced by the single-frequency combination method of L1 band with GPS signals. The results also demonstrate that the proposed two combination methods and the SNR method achieve similar performance. A dual receiver system enables the better use of GNSS signal carrier phase observations for snow depth estimation, achieving increased data utilization. View Full-Text
Keywords: dual GNSS receiver; GNSS Reflectometry; GNSS observation combination; snow depth estimation dual GNSS receiver; GNSS Reflectometry; GNSS observation combination; snow depth estimation
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MDPI and ACS Style

Yu, K.; Wang, S.; Li, Y.; Chang, X.; Li, J. Snow Depth Estimation with GNSS-R Dual Receiver Observation. Remote Sens. 2019, 11, 2056.

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