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@INPROCEEDINGS{Buffat:1033642,
      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        = {{T}owards fast and sensor-independent retrieval of
                      sun-induced fluorescence fromspaceborne hyperspectral data},
      reportid     = {FZJ-2024-06514},
      year         = {2024},
      abstract     = {A corner stone to mapping photosynthetic dynamics
                      efficiently over large areas of land is theretrieval of
                      sun-induced fluorescence (SIF) from passive remote sensing
                      data. In this contributionwe present a novel method to
                      retrieve SIF from hyperspectral imagery that tightly
                      integratesradiative transfer simulations and self-supervised
                      neural network training. Differently to otherphysically
                      constrained retrieval methods that optimize the parameters
                      to a radiative transfermodel (RTM), it reduces the
                      prohibitive computational cost of a physical model deployed
                      tocontinuous data streams. To achieve this, it couples an
                      emulator of large-scale radiative transfersimulations with a
                      lightweight encoder-decoder neural network architecture and
                      is trained byoptimizing a constraint based loss
                      formulation.This method was developed and tested in the
                      spectral region around the O2-A absorption bandon
                      high-quality data acquired by the HyPlant sensor, the
                      airborne demonstrator sensor for ESA’supcoming Earth
                      Explorer satellite mission FLEX that aims to provide global
                      hyperspectral imageryfor SIF retrieval. In a validation
                      study with in-situ SIF measurements we find better
                      performancethan the traditional Spectral Fitting Method
                      (Cogliati et al. 2019). Furthermore, an adapted versionof
                      our approach yields consistent SIF estimates on
                      hyperspectral data of the spaceborne DESISsensor onboard the
                      International Space Station (ISS). This result is
                      encouraging since DESIS onlyprovides spectrally low-resolved
                      imagery (2.55 nm SSD, 3.5 nm FWHM) compared to HyPlant(0.11
                      nm SSD, 0.25 nm FWHM). In a unique data set consisting of
                      quasi-simultaneous, spatiallymatching DESIS and HyPlant
                      acquisitions, the DESIS SIF estimates achieve a mean
                      absolutedifference of less than 0.5 mW nm-1 sr-1 m-2 with
                      respect to HyPlant derived estimates.Furthermore, the method
                      yields SIF estimates that align well with the equally
                      ISS-based OCO-3SIF product.The proposed methodology could
                      benefit research in computationally efficient full-spectrum
                      SIFprediction from FLEX data. While our method has been
                      tested only in the O2-A absorption bandof HyPlant and DESIS
                      acquisitions, principally it can be adapted in a
                      straightforward fashion forretrieval in other spectral
                      regions and in data from different sensors. Future work will
                      thus includerecently published simulated FLEX imagery and
                      our simulation tool developed for DESIS SIFprediction to
                      gauge the method’s applicability in FLEX-like data.},
      month         = {Nov},
      date          = {2024-11-13},
      organization  = {3rd WORKSHOP ON INTERNATIONAL
                       COOPERATION IN SPACEBORNE IMAGING
                       SPECTROSCOPY, Noordwijk (Netherlands),
                       13 Nov 2024 - 15 Nov 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)24},
      url          = {https://juser.fz-juelich.de/record/1033642},
}