GRACE and GRACE-FO Related Publications (no abstracts)

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Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

Shen, Yifan, Zheng, Wei, Yin, Wenjie, Xu, Aigong, Zhu, Huizhong, Wang, Qingqing, and Chen, Zhiwei, 2022. Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China. Remote Sensing, 14(3):535, doi:10.3390/rs14030535.

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@ARTICLE{2022RemS...14..535S,
       author = {{Shen}, Yifan and {Zheng}, Wei and {Yin}, Wenjie and {Xu}, Aigong and {Zhu}, Huizhong and {Wang}, Qingqing and {Chen}, Zhiwei},
        title = "{Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China}",
      journal = {Remote Sensing},
     keywords = {deep learning weight loading inversion model, TWSA, GNSS, GRACE, LSTM},
         year = 2022,
        month = jan,
       volume = {14},
       number = {3},
          eid = {535},
        pages = {535},
     abstract = "{Densely distributed Global Navigation Satellite System (GNSS) stations
        can invert the terrestrial water storage anomaly (TWSA) with
        high precision. However, the uneven distribution of GNSS
        stations greatly limits the application of TWSA inversion. The
        purpose of this study was to compensate for the spatial coverage
        of GNSS stations by simulating the vertical deformation in
        unobserved grids. First, a new deep learning weight loading
        inversion model (DWLIM) was constructed by combining the long
        short-term memory (LSTM) algorithm, inverse distance weight, and
        the crustal load model. DWLIM is beneficial for improving the
        inversion accuracy of TWSA based on the GNSS vertical
        displacement. Second, the DWLIM-based and traditional GNSS-
        derived TWSA methods were utilized to derive TWSA over mainland
        China. Furthermore, the TWSA results were compared with the TWSA
        solutions of the Gravity Recovery and Climate Experiment (GRACE)
        and Global Land Data Assimilation System (GLDAS) model. The
        results indicate that the maximum Pearson's correlation
        coefficient (PCC), Nash-Sutcliffe efficiency (NSE) coefficient,
        and root mean square error (RMSE) equal 0.81, 0.61, and 2.18 cm,
        respectively. The accuracy of DWLIM was higher than that of the
        traditional GNSS inversion method according to PCC, NSE, and
        RMSE, which were increased by 67.11, 128.15, and 22.75\%. The
        inversion strategy of DWLIM can effectively improve the accuracy
        of TWSA inversion in regions with unevenly distributed GNSS
        stations. Third, this study investigated the variation
        characteristics of TWSA based on DWLIM in 10 river basins over
        mainland China. The analysis shows that the TWSA amplitudes of
        Songhua and Liaohe River basins are significantly higher than
        those of the other basins. Moreover, TWSA sequences in each
        river basin contain annual seasonal signals, and the wave peaks
        of TWSA estimates emerge between June and July. Overall, DWLIM
        provides a useful measure to derive TWSA in regions where GNSS
        stations are uneven or sparse.}",
          doi = {10.3390/rs14030535},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022RemS...14..535S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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