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@ARTICLE{Pato:1048174,
      author       = {Pato, Miguel and Alonso, Kevin and Buffat, Jim and Auer,
                      Stefan and Carmona, Emiliano and Maier, Stefan and Müller,
                      Rupert and Rademske, Patrick and Rascher, Uwe and Scharr,
                      Hanno},
      title        = {{S}imulation framework for solar-induced fluorescence
                      retrieval and application to {DESIS} and {H}y{P}lant},
      journal      = {Remote sensing of environment},
      volume       = {330},
      issn         = {0034-4257},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2025-04536},
      pages        = {114944 -},
      year         = {2025},
      abstract     = {Fluorescence light emitted by chlorophyll in plants is a
                      direct probe of the photosynthetic process and can be used
                      to continuously monitor vegetation status. Retrieving
                      solar-induced fluorescence (SIF) using a machine learning
                      (ML) approach promises to take full advantage of airborne
                      and satellite-based instruments to map expected vegetation
                      function over wide areas on a regular basis. This work takes
                      a first step towards developing a ML-based SIF retrieval
                      method. A general-purpose framework for the simulation of
                      at-sensor radiances is introduced and applied to the case of
                      SIF retrieval in the oxygen absorption band O2-A with the
                      spaceborne DESIS and airborne HyPlant spectrometers. The
                      sensor characteristics are modelled carefully based on
                      calibration and in-flight data and can be extended to other
                      instruments including the upcoming FLEX mission. A
                      comprehensive dataset of simulated at-sensor radiance
                      spectra is then assembled encompassing the most important
                      atmosphere, geometry, surface and sensor properties. The
                      simulated dataset is employed to train emulators capable of
                      generating at-sensor radiances with sub-percent errors in
                      tens of μs, opening the way for their routine use in SIF
                      retrieval. The simulated spectra are shown to closely
                      reproduce real data acquired by DESIS and HyPlant and can
                      ultimately be used to develop a robust ML-based SIF
                      retrieval scheme for these and other remote sensing
                      spectrometers. Finally, the SIF retrieval performance of the
                      3FLD method is quantitatively assessed for different on- and
                      off-band configurations in order to identify the best band
                      combinations. This highlights how our simulation framework
                      enables the optimization of SIF retrieval methods to achieve
                      the best possible performance for a given instrument.},
      cin          = {IBG-2 / IAS-8},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-2-20101118 / I:(DE-Juel1)IAS-8-20210421},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / 5112 - Cross-Domain Algorithms, Tools, Methods
                      Labs (ATMLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.rse.2025.114944},
      url          = {https://juser.fz-juelich.de/record/1048174},
}