% 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”.

@INPROCEEDINGS{Buffat:1033639,
      author       = {Buffat, Jim and Pato, Miguel and Auer, Stefan and Alonso,
                      Kevin and Carmona, Emiliano and Maier, Stefan and Müller,
                      Rupert and Rademske, Patrick and Rascher, Uwe and Scharr,
                      Hanno},
      title        = {{L}everaging a large-scale radiative transfer simulation
                      for an emulator based retrieval scheme of sun-induced
                      fluorescence in {H}y{P}lant imagery},
      reportid     = {FZJ-2024-06511},
      year         = {2024},
      abstract     = {The prediction of sun-induced fluorescence (SIF) from
                      hyperspectral radiance has been identified as a corner stone
                      to assess plants’ photosynthetic efficiency remotely. It
                      is widely accepted that remotely sensed SIF offers great
                      potential for a variety of applications. To provide such
                      estimates, top-of-canopy SIF products derived from passively
                      sensed radiance measurements of various airborne and
                      spaceborne sensors have been developed over the last
                      decades. To date, however, physically based SIF retrieval
                      schemes require a prohibitive use of computationally costly
                      radiative transfer simulations especially when used in
                      complex observational conditions such as in hilly terrain.
                      In this contribution we report on our on-going work to
                      develop a lightweight self-supervised neural network to
                      retrieve SIF in the O$_2$-A absorption band of HyPlant
                      acquisitions. We aim at a tight integration of a physical
                      radiative transfer model with the network to ensure
                      physically sound predictions by leveraging large scale
                      simulation and emulation of HyPlant at-sensor radiance
                      observations. We report on first results that we achieve on
                      a dedicated data set.},
      month         = {Apr},
      date          = {2024-04-16},
      organization  = {13th EARSeL Workshop on Imaging
                       Spectroscopy, Valencia (Spain), 16 Apr
                       2024 - 18 Apr 2024},
      subtyp        = {After Call},
      cin          = {IAS-8 / IBG-2},
      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)6},
      doi          = {10.34734/FZJ-2024-06511},
      url          = {https://juser.fz-juelich.de/record/1033639},
}