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@INPROCEEDINGS{Keller:1054395,
      author       = {Keller, Johannes and Dhaou, Amin and Li, Yu and Schlerf,
                      Martin and Paolucci, Jean-Baptiste and Jin, Fen and
                      Rouwette, Sander and Sulis, Mauro and Hendricks-Franssen,
                      Harrie-Jan},
      title        = {{S}cenario-{T}esting and {P}rediction {C}apabilities of the
                      {S}ave{C}rops4{EU} {A}gricultural {D}igital {T}win
                      {C}omponent},
      reportid     = {FZJ-2026-01823},
      year         = {2026},
      note         = {ESA project "SaveCrops4EU" funded as an ITT (Invitation to
                      Tender) as part of the Digital Twin Earth Programme},
      abstract     = {This abstract presents the scenario-testing and
                      forecastingcapabilities of the SaveCrops4EU DTC Agriculture
                      platform. Thescenario-based representation of crop
                      conditions relies on numericalsimulations generated with
                      eCLM, a land-surface model with advancedagronomic features
                      designed to produce alternative and plausibletrajectories of
                      crop states (e.g., LAI, biomass, phenology, yield)under
                      varying weather and management practices. The proposed
                      approachcouples the spatiotemporal coverage of EO data with
                      the mechanisticknowledge embedded in crop-growth models to
                      (i) constrain model statesusing observations and (ii)
                      quantify uncertainty through ensemblemethods. These
                      scenario-testing capabilities are demonstrated for
                      thewell-instrumented ICOS site of Selhausen (winter wheat)
                      in Germany,using Copernicus seasonal atmospheric forecasts
                      during the 2018drought year.The in-season crop-yield
                      forecasting capabilities of the DTC solutionbuild on a suite
                      of machine- and deep-learning techniques trained
                      onatmospheric conditions and land-surface features derived
                      from multipleremote-sensing products available in Copernicus
                      catalogues andgenerated in the Monitoring Pillar of the
                      platform. The data-drivenapproaches are evaluated using
                      wheat and maize yield statisticsaggregated at the NUTS3
                      level for Germany and Hungary. In addition,explainability
                      tools are integrated to support users in interpretingmodel
                      outputs. Finally, we discuss how the scenario-testing
                      andforecasting components of SaveCrops4EU can be scaled
                      across Europeanagricultural regions to enable farmers and
                      policymakers to assessadaptation strategies under changing
                      climate conditions.},
      month         = {Feb},
      date          = {2026-02-02},
      organization  = {ESA Digital Twin Earth Components:
                       Open Science Meeting 2026, Frascati
                       (Italy), 2 Feb 2026 - 4 Feb 2026},
      subtyp        = {Outreach},
      cin          = {IBG-3},
      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)24},
      doi          = {10.34734/FZJ-2026-01823},
      url          = {https://juser.fz-juelich.de/record/1054395},
}