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@ARTICLE{DeLannoy:917281,
      author       = {De Lannoy, Gabriëlle J. M. and Bechtold, Michel and
                      Albergel, Clément and Brocca, Luca and Calvet,
                      Jean-Christophe and Carrassi, Alberto and Crow, Wade T. and
                      de Rosnay, Patricia and Durand, Michael and Forman, Barton
                      and Geppert, Gernot and Girotto, Manuela and
                      Hendricks-Franssen, Harrie-Jan and Jonas, Tobias and Kumar,
                      Sujay and Lievens, Hans and Lu, Yang and Massari, Christian
                      and Pauwels, Valentijn R. N. and Reichle, Rolf H. and
                      Steele-Dunne, Susan},
      title        = {{P}erspective on satellite-based land data assimilation to
                      estimate water cycle components in an era of advanced data
                      availability and model sophistication},
      journal      = {Frontiers in water},
      volume       = {4},
      issn         = {2624-9375},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2023-00511},
      pages        = {981745},
      year         = {2022},
      abstract     = {The beginning of the 21st century is marked by a rapid
                      growth of land surface satellite data and model
                      sophistication. This offers new opportunities to estimate
                      multiple components of the water cycle via satellite-based
                      land data assimilation (DA) across multiple scales. By
                      resolving more processes in land surface models and by
                      coupling the land, the atmosphere, and other Earth system
                      compartments, the observed information can be propagated to
                      constrain additional unobserved variables. Furthermore,
                      access to more satellite observations enables the direct
                      constraint of more and more components of the water cycle
                      that are of interest to end users. However, the finer level
                      of detail in models and data is also often accompanied by an
                      increase in dimensions, with more state variables,
                      parameters, or boundary conditions to estimate, and more
                      observations to assimilate. This requires advanced DA
                      methods and efficient solutions. One solution is to target
                      specific observations for assimilation based on a
                      sensitivity study or coupling strength analysis, because not
                      all observations are equally effective in improving
                      subsequent forecasts of hydrological variables, weather,
                      agricultural production, or hazards through DA. This paper
                      offers a perspective on current and future land DA
                      development, and suggestions to optimally exploit advances
                      in observing and modeling systems.},
      cin          = {IBG-3},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000862463100001},
      doi          = {10.3389/frwa.2022.981745},
      url          = {https://juser.fz-juelich.de/record/917281},
}