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@ARTICLE{Buffat:1033637,
      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        = {{R}etrieval of sun-induced plant fluorescence in the
                      {O}$_2$-{A} absorption band from {DESIS} imagery},
      publisher    = {arXiv},
      reportid     = {FZJ-2024-06509},
      year         = {2024},
      abstract     = {We provide the first method allowing to retrieve spaceborne
                      SIF maps at 30 m ground resolution with a strong correlation
                      ($r^2=0.6$) to high-quality airborne estimates of
                      sun-induced fluorescence (SIF). SIF estimates can provide
                      explanatory information for many tasks related to
                      agricultural management and physiological studies. While SIF
                      products from airborne platforms are accurate and spatially
                      well resolved, the data acquisition of such products remains
                      science-oriented and limited to temporally constrained
                      campaigns. Spaceborne SIF products on the other hand are
                      available globally with often sufficient revisit times.
                      However, the spatial resolution of spaceborne SIF products
                      is too small for agricultural applications. In view of ESA's
                      upcoming FLEX mission we develop a method for SIF retrieval
                      in the O$_2$-A band of hyperspectral DESIS imagery to
                      provide first insights for spaceborne SIF retrieval at high
                      spatial resolution. To this end, we train a simulation-based
                      self-supervised network with a novel perturbation based
                      regularizer and test performance improvements under
                      additional supervised regularization of atmospheric variable
                      prediction. In a validation study with corresponding HyPlant
                      derived SIF estimates at 740 nm we find that our model
                      reaches a mean absolute difference of 0.78 mW / nm / sr /
                      m$^2$.},
      keywords     = {Computer Vision and Pattern Recognition (cs.CV) (Other) /
                      Artificial Intelligence (cs.AI) (Other) / Geophysics
                      (physics.geo-ph) (Other) / FOS: Computer and information
                      sciences (Other) / FOS: Physical sciences (Other)},
      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)25},
      doi          = {10.48550/ARXIV.2411.08925},
      url          = {https://juser.fz-juelich.de/record/1033637},
}