Article (Scientific journals)
Statistical Comparison and Combination of GPS, GLONASS, and Multi-GNSS Multipath Reflectometry Applied to Snow Depth Retrieval
Tabibi, Sajad; Geremia-Nievinski, Felipe; van Dam, Tonie
2017In IEEE Transactions on Geoscience and Remote Sensing, (99)
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Keywords :
Global navigation satellite system (GNSS); GLONASS; GPS; multipath; reflectometry; signal-to-noise ratio (SNR); snow depth
Abstract :
[en] Global navigation satellite system (GNSS) multipath reflectometry (MR) has emerged as a new technique that uses signals of opportunity broadcast by GNSS satellites and tracked by ground-based receivers to retrieve environmental variables such as snow depth. The technique is based on the simultaneous reception of direct or line-of-sight (LOS) transmissions and corresponding coherent surface reflections (non-LOS). Until recently, snow depth retrieval algorithms only used legacy and modernized GPS signals. Using multiple GNSS constellations for reflectometry would improve GNSS-MR applications by providing more observations from more satellites and independent signals (carrier frequencies and code modulations). We assess GPS and GLONASS for combined multi-GNSS-MR using simulations as well as field measurements. Synthetic observations for different signals indicated a lack of detectable interfrequency and intercode biases in GNSS-MR snow depth retrievals. Received signals from a GNSS station continuously operating in France for a two-winter period are used for experimental snow depth retrieval. We perform an internal validation of various GNSS signals against the proven GPS-L2-C signal, which was validated externally against in situ snow depth in previous studies. GLONASS observations required a more complex handling to account for topography because of its particular ground track repeatability. Signal intercomparison show an average correlation of 0.922 between different GPS snow depths and GPS-L2-CL, while GLONASS snow depth retrievals have an average correlation that exceeds 0.981. In terms of precision and accuracy, legacy GPS signals are worse, while GLONASS signals and modernized GPS signals are of comparable quality. Finally, we show how an optimal multi-GNSS combined daily snow depth time series can be formed employing variance factors with a ~59%-90% precision improvement compared to individual signal snow depth retrievals, resulting in snow depth retrieval with uncertainty of 1.3 cm. The developed combination strategy can also be applied for the European Galileo and the Chines BeiDou navigation systems.
Disciplines :
Earth sciences & physical geography
Author, co-author :
Tabibi, Sajad ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Geremia-Nievinski, Felipe;  Federal University of Rio Grande do Sul > Department of Geodesy
van Dam, Tonie ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
External co-authors :
yes
Language :
English
Title :
Statistical Comparison and Combination of GPS, GLONASS, and Multi-GNSS Multipath Reflectometry Applied to Snow Depth Retrieval
Publication date :
07 April 2017
Journal title :
IEEE Transactions on Geoscience and Remote Sensing
ISSN :
0196-2892
eISSN :
1558-0644
Publisher :
Institute of Electrical and Electronics Engineers, United States
Issue :
99
Peer reviewed :
Peer Reviewed verified by ORBi
FnR Project :
FNR1351102 - Gps Estimates Of Soil Moisture In Luxembourg (Luxsm), 2011 (15/04/2012-14/04/2016) - Sajad Tabibi
Available on ORBilu :
since 02 May 2017

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