Publications related to the GRACE Missions (no abstracts)

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Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques

Renshaw, Megan and Magruder, Lori A., 2025. Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques. Geosciences, 15(7):255, doi:10.3390/geosciences15070255.

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@ARTICLE{2025Geosc..15..255R,
       author = {{Renshaw}, Megan and {Magruder}, Lori A.},
        title = "{Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques}",
      journal = {Geosciences},
     keywords = {ICESat-2, GRACE-FO, machine learning, hydrology, Sentinel-2, surface water volume},
         year = 2025,
        month = jul,
       volume = {15},
       number = {7},
          eid = {255},
        pages = {255},
     abstract = "{Accurate surface water volume (SWV) estimates are crucial for effective
        water resource management and for the regional monitoring of
        hydrological trends. This study introduces a multi-resolution
        surface water volume estimation framework that integrates
        ICESat-2 altimetry, Sentinel-1 Synthetic Aperture Radar (SAR),
        and Sentinel-2 multispectral imagery via machine learning to
        improve the vertical resolution of a digital elevation model
        (DEM) to improve the accuracy of SWV estimates. The machine
        learning approach provides a significant improvement in terrain
        accuracy relative to the DEM, reducing RMSE by
        \raisebox{-0.5ex}\textasciitilde66\% and 78\% across the two
        models, respectively, over the initial data product fidelity.
        Assessing the resulting SWV estimates relative to GRACE-FO
        terrestrial water storage in parts of the Amazon Basin, we found
        strong correlations and basin-wide drying trends. Notably, the
        high correlation (r > 0.8) between our surface water estimates
        and the GRACE-FO signal in the Manaus region highlights our
        method's ability to resolve key hydrological dynamics. Our
        results underscore the value of improved vertical DEM
        availability for global hydrological studies and offer a
        scalable framework for future applications. Future work will
        focus on expanding our DEM dataset, further validation, and
        scaling this methodology for global applications.}",
          doi = {10.3390/geosciences15070255},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025Geosc..15..255R},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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