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@INPROCEEDINGS{Buffat:1018125,
      author       = {Buffat, Jim and Pato, Miguel 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        = {{DEEP} {LEARNING} {BASED} {PREDICTION} {OF} {SUN}-{INDUCED}
                      {FLUORESCENCE} {FROM} {HYPLANT} {IMAGERY}},
      publisher    = {IEEE},
      reportid     = {FZJ-2023-04569},
      pages        = {2993 - 2996},
      year         = {2023},
      note         = {This work is funded by the Helmholtz Initiative and
                      Networking Fund, Helmholtz AI, Deutsches Zentrum für Luft-
                      und Raumfahrt and Forschungszentrum Jülich GmbH. The
                      authors gratefully acknowledge the computing time granted by
                      the JARA Vergabegremium and provided on the JARA Partition
                      part of the supercomputer JURECA [1] at Forschungszentrum
                      Jülich.Jülch Supercomputing Centre, “JURECA: Data
                      Centric and Booster Modules implementing the Modular
                      Supercomputing Architecture at Jülch Supercomputing
                      Centre,” Journal of large-scale research facilities, vol.
                      7, no. A182, 2021. [Online]. Available:
                      http://dx.doi.org/10.17815/jlsrf-7-182},
      abstract     = {The retrieval of sun-induced fluorescence (SIF) from hyper-
                      spectral imagery is an ill-posed problem that has been
                      tackled in different ways. We present a novel retrieval
                      method com- bining semi-supervised deep learning with an
                      existing spec- tral fitting method. A validation study with
                      in-situ SIF mea- surements shows high sensitivity of the
                      deep learning method to SIF changes even though systematic
                      shifts deteriorate its absolute prediction accuracy. A
                      detailed analysis of diurnal SIF dynamics and SIF prediction
                      in topographically variable terrain highlights the benefits
                      of this deep learning approach.},
      month         = {Jun},
      date          = {2023-06-21},
      organization  = {International Geoscience and Remote
                       Sensing Symposium, Pasadena (USA), 21
                       Jun 2023 - 21 Jun 2023},
      cin          = {IBG-2 / IAS-8},
      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)8},
      UT           = {WOS:001098971603050},
      doi          = {10.1109/IGARSS52108.2023.10282828},
      url          = {https://juser.fz-juelich.de/record/1018125},
}