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Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation

Khaki, M., Hendricks Franssen, H. -J., and Han, S. C., 2020. Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation. Scientific Reports, 10:18791, doi:10.1038/s41598-020-75710-5.

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@ARTICLE{2020NatSR..1018791K,
       author = {{Khaki}, M. and {Hendricks Franssen}, H. -J. and {Han}, S.~C.},
        title = "{Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation}",
      journal = {Scientific Reports},
         year = 2020,
        month = nov,
       volume = {10},
          eid = {18791},
        pages = {18791},
     abstract = "{Satellite remote sensing offers valuable tools to study Earth and
        hydrological processes and improve land surface models. This is
        essential to improve the quality of model predictions, which are
        affected by various factors such as erroneous input data, the
        uncertainty of model forcings, and parameter uncertainties.
        Abundant datasets from multi-mission satellite remote sensing
        during recent years have provided an opportunity to improve not
        only the model estimates but also model parameters through a
        parameter estimation process. This study utilises multiple
        datasets from satellite remote sensing including soil moisture
        from Soil Moisture and Ocean Salinity Mission and Advanced
        Microwave Scanning Radiometer Earth Observing System,
        terrestrial water storage from the Gravity Recovery And Climate
        Experiment, and leaf area index from Advanced Very-High-
        Resolution Radiometer to estimate model parameters. This is done
        using the recently proposed assimilation method, unsupervised
        weak constrained ensemble Kalman filter (UWCEnKF). UWCEnKF
        applies a dual scheme to separately update the state and
        parameters using two interactive EnKF filters followed by a
        water balance constraint enforcement. The performance of
        multivariate data assimilation is evaluated against various
        independent data over different time periods over two different
        basins including the Murray-Darling and Mississippi basins.
        Results indicate that simultaneous assimilation of multiple
        satellite products combined with parameter estimation strongly
        improves model predictions compared with single satellite
        products and/or state estimation alone. This improvement is
        achieved not only during the parameter estimation period
        ({\ensuremath{\sim}}? 32\% groundwater RMSE reduction and soil
        moisture correlation increase from {\ensuremath{\sim}}? 0.66 to
        {\ensuremath{\sim}}? 0.85) but also during the forecast period
        ({\ensuremath{\sim}}? 14\% groundwater RMSE reduction and soil
        moisture correlation increase from {\ensuremath{\sim}}? 0.69 to
        {\ensuremath{\sim}}? 0.78) due to the effective impacts of the
        approach on both state and parameters.}",
          doi = {10.1038/s41598-020-75710-5},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020NatSR..1018791K},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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