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@INPROCEEDINGS{Buffat:1049209,
      author       = {Buffat, Jim and Pato, Miguel and Alonso, Kevin and Auer,
                      Stefan and Carmona, Emiliano and Müller, Rupert and
                      Rademske, Patrick and Rascher, Uwe and Scharr, Hanno},
      title        = {{L}everaging deep learning for the retrieval of sun-induced
                      fluorescence in the {O}$_2$-{A} absorption band from space},
      reportid     = {FZJ-2025-05291},
      year         = {2025},
      abstract     = {Efficiently monitoring the photosynthetic activity of
                      plants across large areas is a challenging task that has
                      been addressed in the last decades as part of an ongoing
                      research interest in the remote sensing of the environment
                      and agricultural areas. The closest measurable variable to
                      photosynthetic activity is the emission of sun-induced
                      chlorophyll fluorescence (SIF). While SIF data retrieved
                      from spaceborne hyperspectral sensors with low spatial
                      resolution (> 1 km pixel size) has been used for drought
                      monitoring, yield prediction, and the estimation of global
                      gross primary productivity in the past, these sensors
                      provide only limited insight into the fine-scale dynamics of
                      photosynthesis. This has led to the preparation of the ESA
                      Earth Explorer FLEX - the first spaceborne satellite mission
                      dedicated to SIF retrieval - which promises to address this
                      gap, offering SIF estimates at a significantly higher
                      spatial resolution (300 m). In this contribution, we
                      summarize our results on a novel deep learning based
                      methodology to retrieve SIF from hyperspectral imagery at
                      760 nm that could benefit the research in computationally
                      efficient SIF retrieval and validation of FLEX data.Our
                      approach was developed and validated using high-quality data
                      from the HyPlant FLUO sensor, the airborne demonstrator for
                      FLEX FLORIS, which has a full width at half maximum (FWHM)
                      of 0.25 nm. In this contribution, we show that the method
                      can be adapted to data acquired by the spaceborne DESIS
                      sensor (3.5 nm FWHM) onboard the International Space Station
                      (ISS) providing for the first time spaceborne SIF estimates
                      at 30 m. In a validation study conducted with a unique
                      benchmark dataset consisting of HyPlant and DESIS at-sensor
                      radiance acquired in a time interval of less than 25 minutes
                      we find r$^2$ = 0.60 and a mean absolute difference between
                      the SIF products of the two sensors of 0.78 mW nm$^{-1}$
                      sr$^{-1}$ m$^{-2}$ at 740 nm. Furthermore, we find r$^2$ =
                      0.20 in a cross-comparison of DESIS SIF with OCO-3
                      estimates.Our methodology involves the training of an
                      encoder-decoder type neural network to perform a
                      decomposition of the at-sensor radiance into surface and
                      atmospheric parameters in the spectral window around the
                      O2-A absorption band. Predicted parameters are evaluated in
                      the observation space according to a novel label-free
                      reconstruction based loss formulation. In the case of DESIS,
                      we additionally introduce supervised regularizing loss terms
                      to enhance the integration of existing L2A products. In
                      order to be able to perform radiative transfer simulation as
                      part of the training process we make use of a
                      computationally efficient radiative transfer emulator that
                      we derive from extensive hyperspectral radiative transfer
                      simulation for both HyPlant and DESIS sensor configurations.
                      This approach contrasts with traditional spectral fitting
                      methods by its adoption of a feature-based strategy,
                      enabling faster inference times.Due to the exceptional
                      performance of this approach on DESIS data as well as its
                      beneficial properties showcased in HyPlant data, our
                      contribution has the potential to enhance research on
                      computationally efficient SIF retrieval from FLEX data.
                      Furthermore, its DESIS SIF product may provide a valuable
                      high-resolution dataset for cross-comparison of FLEX SIF
                      products in regions where spatial mixing impedes other
                      validation approaches.},
      month         = {Jun},
      date          = {2025-06-23},
      organization  = {Living Planet Symposium 2025, Vienna
                       (Austria), 23 Jun 2025 - 27 Jun 2025},
      subtyp        = {Invited},
      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)24},
      doi          = {10.34734/FZJ-2025-05291},
      url          = {https://juser.fz-juelich.de/record/1049209},
}