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@ARTICLE{Khaki:890098,
      author       = {Khaki, M. and Han, S. C. and Hendricks-Franssen,
                      Harrie-Jan},
      title        = {{M}ulti-mission satellite remote sensing data for improving
                      land hydrological models via data assimilation},
      journal      = {Scientific reports},
      volume       = {10},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2021-00687},
      pages        = {18791},
      year         = {2020},
      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 (∼ $32\%$ groundwater RMSE reduction and
                      soil moisture correlation increase from ∼ 0.66 to ∼
                      0.85) but also during the forecast period (∼ $14\%$
                      groundwater RMSE reduction and soil moisture correlation
                      increase from ∼ 0.69 to ∼ 0.78) due to the effective
                      impacts of the approach on both state and parameters.},
      cin          = {IBG-3},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33139783},
      UT           = {WOS:000589616100006},
      doi          = {10.1038/s41598-020-75710-5},
      url          = {https://juser.fz-juelich.de/record/890098},
}