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Triple-frequency multi-GNSS reflectometry snow depth retrieval by using clustering and normalization algorithm to compensate terrain variation

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Abstract

Snow is an important water resource and plays a critical role in the hydrologic cycle. Accurate measurements of snow depth are needed by scientists to set up a more refined meteorology–hydrology model. Recently, the Global Navigation Satellite System Reflectometry (GNSS-R) has been developed and applied for snow depth monitoring, with low cost and high resolution. We propose an improved snow depth retrieval method using a combination of GNSS triple-frequency carrier phase. The topographic feature of the reflecting surface is considered for estimating the snow depth by using the density-based spatial clustering of applications with noise algorithm and normalization method. Observables from the GNSS station in Alaska, USA, are used to monitor snow depth and compared with the ground-truth measurements. Compared with the traditional triple-frequency snow depth retrieval method, the new approach has better performance for Galileo and BDS. The RMSE of the snow depth estimate reduces by nearly 40%, and the correlation coefficient increases from 0.93 to 0.97 for Galileo and from 0.91 to 0.95 for BDS, respectively. The research findings show no notable deviations on snow depth average estimation between Galileo and BDS observations compared to the GPS ones. Moreover, the solution with the proposed method results in improving spatial resolution due to the increasing number of satellites and better azimuth coverage.

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(Credit: Larson and Small 2014; Yu et al. 2015)

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Acknowledgements

We are grateful to the GNSS data provided by the Plate Boundary Observatory operated by UNAVCO (UNAVCO Community 2002) and the DEM data provided by USGS. We would also like to express our gratitude to National Operational Hydrologic Remote Sensing Center and NOAA for providing climate data. We are very grateful for the help of language editing from Mohamed Freeshah. Two anonymous reviewers are also appreciated for their valuable comments and careful checks. This research was funded by the National Natural Science Foundation of China (Grant No. 41774034), the National Science Fund for Distinguished Young Scholars (Grant No. 41825009) and the Fund for Creative Research Groups of China (Grant No. 41721003).

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Authors and Affiliations

  1. School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China

    Zhiyu Zhang, Fei Guo & Xiaohong Zhang

  2. Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan, 430079, China

    Fei Guo & Xiaohong Zhang

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  1. Zhiyu Zhang
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  2. Fei Guo
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Correspondence to Fei Guo.

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Zhang, Z., Guo, F. & Zhang, X. Triple-frequency multi-GNSS reflectometry snow depth retrieval by using clustering and normalization algorithm to compensate terrain variation. GPS Solut 24, 52 (2020). https://doi.org/10.1007/s10291-020-0966-4

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