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@INPROCEEDINGS{Pato:1022048,
      author       = {Pato, Miguel and Alonso, Kevin and Auer, Stefan and Buffat,
                      Jim and Carmona, Emiliano and Maier, Stefan and Müller,
                      Rupert and Rademske, Patrick and Rascher, Uwe and Scharr,
                      Hanno},
      title        = {{F}ast {M}achine {L}earning {S}imulator of {A}t-{S}ensor
                      {R}adiances for {S}olar-{I}nduced {F}luorescence {R}etrieval
                      with {DESIS} and {H}yplant},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-01185},
      pages        = {7563-7566},
      year         = {2023},
      abstract     = {In many remote sensing applications the measured radi-ance
                      needs to be corrected for atmospheric effects to
                      studysurface properties such as reflectance, temperature or
                      emis-sion features. The correction often applies radiative
                      transferto simulate atmospheric propagation, a
                      time-consuming stepusually done offline. In principle, an
                      efficient machine learn-ing (ML) model can accelerate the
                      simulation step. This is thegoal pursued here in the context
                      of solar-induced fluorescence(SIF) emitted by vegetation
                      around the O2-A band using thespaceborne DESIS and airborne
                      HyPlant spectrometers. Wepresent an ML simulator of
                      at-sensor radiances trained onsynthetic spectra and describe
                      its performance in detail. Thesimulator is fast and
                      accurate, constituting a promising alter-native to a
                      full-fledged, lengthy radiative transfer code for
                      SIFretrieval in the O2-A band with DESIS and HyPlant.Index
                      Terms— solar-induced fluorescence, hyperspectralsensors,
                      radiative transfer, machine learning},
      month         = {Jul},
      date          = {2023-07-16},
      organization  = {IGARSS 2023 - 2023 IEEE International
                       Geoscience and Remote Sensing
                       Symposium, Pasadena (CA), 16 Jul 2023 -
                       21 Jul 2023},
      cin          = {IAS-8},
      cid          = {I:(DE-Juel1)IAS-8-20210421},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:001098971607156},
      doi          = {10.1109/IGARSS52108.2023.10281579},
      url          = {https://juser.fz-juelich.de/record/1022048},
}