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@ARTICLE{Tewes:874693,
      author       = {Tewes, Andreas and Hoffmann, Holger and Krauss, Gunther and
                      Schäfer, Fabian and Kerkhoff, Christian and Gaiser, Thomas},
      title        = {{N}ew {A}pproaches for the {A}ssimilation of {LAI}
                      {M}easurements into a {C}rop {M}odel {E}nsemble to {I}mprove
                      {W}heat {B}iomass {E}stimations},
      journal      = {Agronomy},
      volume       = {10},
      number       = {3},
      issn         = {2073-4395},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2020-01608},
      pages        = {446 -},
      year         = {2020},
      abstract     = {The assimilation of LAI measurements, repeatedly taken at
                      sub-field level, into dynamic crop simulation models could
                      provide valuable information for precision farming
                      applications. Commonly used updating methods such as the
                      Ensemble Kalman Filter (EnKF) rely on an ensemble of model
                      runs to update a limited set of state variables every time a
                      new observation becomes available. This threatens the
                      model’s integrity, as not the entire table of model states
                      is updated. In this study, we present the Weighted Mean (WM)
                      approach that relies on a model ensemble that runs from
                      simulation start to simulation end without compromising the
                      consistency and integrity of the state variables. We
                      measured LAI on 14 winter wheat fields across France,
                      Germany and the Netherlands and assimilated these
                      observations into the LINTUL5 crop model using the EnKF and
                      WM approaches, where the ensembles were created using one
                      set of crop component (CC) ensemble generation variables and
                      one set of soil and crop component (SCC) ensemble generation
                      variables. The model predictions for total aboveground
                      biomass and grain yield at harvest were evaluated against
                      measurements collected in the fields. Our findings showed
                      that (a) the performance of the WM approach was very similar
                      to the EnKF approach when SCC variables were used for the
                      ensemble generation, but outperformed the EnKF approach when
                      only CC variables were considered, (b) the difference in
                      site-specific performance largely depended on the choice of
                      the set of ensemble generation variables, with SCC
                      outperforming CC with regard to both biomass and grain
                      yield, and (c) both EnKF and WM improved accuracy of biomass
                      and yield estimates over standard model runs or the ensemble
                      mean. We conclude that the WM data assimilation approach is
                      equally efficient to the improvement of model accuracy,
                      compared to the updating methods, but it has the advantage
                      that it does not compromise the integrity and consistency of
                      the state variables.},
      cin          = {IBG-3},
      ddc          = {640},
      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},
      UT           = {WOS:000529377300132},
      doi          = {10.3390/agronomy10030446},
      url          = {https://juser.fz-juelich.de/record/874693},
}