% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Pato:1030920,
      author       = {Pato, Miguel and Buffat, Jim and Alonso, Kevin and Auer,
                      Stefan and Carmona, Emiliano and Maier, Stefan and Müller,
                      Rupert and Rademske, Patrick and Rascher, Uwe and Scharr,
                      Hanno},
      title        = {{P}hysics-based {M}achine {L}earning {E}mulator of
                      {A}t-sensor {R}adiances for {S}olar-induced {F}luorescence
                      {R}etrieval in the {O}-{A} {A}bsorption {B}and},
      journal      = {IEEE journal of selected topics in applied earth
                      observations and remote sensing},
      volume       = {17},
      issn         = {1939-1404},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-05513},
      pages        = {18566 - 18576},
      year         = {2024},
      abstract     = {The successful operation of airborne and space-based
                      spectrometers in recent years holds the promise to map
                      solar-induced fluorescence (SIF) accurately across the
                      globe. Machine learning (ML) can play an important role in
                      this effort, but its application to SIF retrieval methods is
                      in part hindered by the need for time-consuming radiative
                      transfer modelling to account for atmospheric effects. In
                      this work, we address this difficulty and develop a fast and
                      accurate physics-based ML emulator of at-sensor radiances
                      around the O 2 -A absorption band for the space-based DESIS
                      and the airborne HyPlant spectrometers. Different ML models
                      are trained on an extensive set of simulated spectra
                      encompassing a wide range of atmosphere, geometry, surface
                      and sensor configurations. A fourth-degree polynomial model
                      is found to perform best, presenting errors at or below
                      $10\%$ of typical SIF at-sensor radiances and a prediction
                      time per sample spectrum of 10-20 μ s. Using data acquired
                      with the HyPlant instrument, the proposed model is also
                      shown to be able to match very closely the measured spectra.
                      We illustrate how to improve further the accuracy of the
                      emulator and how to generalize it to other sensors using the
                      particular case of ESA's FLEX space mission. Our findings
                      suggest that physics-based emulators can be efficiently used
                      for the development of ML-based SIF retrieval methods by
                      generating large training data sets in short time and by
                      enabling a fast simulation step for self-supervised
                      retrieval schemes.},
      cin          = {IAS-8 / IBG-2},
      ddc          = {520},
      cid          = {I:(DE-Juel1)IAS-8-20210421 / I:(DE-Juel1)IBG-2-20101118},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 2173 - Agro-biogeosystems:
                      controls, feedbacks and impact (POF4-217)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001339129900006},
      doi          = {10.1109/JSTARS.2024.3457231},
      url          = {https://juser.fz-juelich.de/record/1030920},
}