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@INPROCEEDINGS{Buffat:1018123,
      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        = {{E}mulator-{B}ased {N}eural {N}etwork {P}rediction for the
                      {R}etrieval {O}f {S}un-{I}nduced {F}luorescence in the
                      {O}2-{A} {A}bsorption {B}and},
      reportid     = {FZJ-2023-04567},
      year         = {2023},
      note         = {The authors gratefully acknowledge the computing time
                      granted through JARA on the supercomputer JURECA[1] at
                      Forschungszentrum Jülich.[1] Jülich Supercomputing Centre.
                      (2021). JURECA: Data Centric and Booster Modules
                      implementing the Modular Supercomputing Architecture at
                      Jülich Supercomputing Centre Journal of large-scale
                      research facilities, 7, A182.
                      http://dx.doi.org/10.17815/jlsrf-7-182},
      abstract     = {Remote sensing applications rely on precise and efficient
                      corrections of atmospheric effects to retrieve surface
                      parameters such as reflectance, physiological quantities or
                      emission features. Often the retrieval is made more
                      difficult by the need for additional correction of the
                      illumination-viewing geometry. A well known approach to
                      solve such inversion tasks is to generate look-up tables of
                      simulated at-sensor radiance spectra with precise radiative
                      transfer models. The retrieval may then be expressed as a
                      spectral fitting of the observations. This contribution
                      investigates the performance of an encoder-decoder neural
                      network architecture to learn this optimization step in the
                      context of the retrieval of Sun-induced fluorescence (SIF)
                      in the O2-A oxygen absorption band for the ISS- based DESIS
                      spectrometer [1] and the airborne HyPlant instrument
                      [2].Recently, SIF has gained much interest in the wake of
                      the selection of the FLEX satellite mission by the European
                      Space Agency to be the first dedicated Earth Explorer
                      satellite mission for global SIF retrieval. The value of SIF
                      maps at multiple spatial scales to environmental and
                      agricultural use- cases are due to the close causal link
                      between SIF and plant photosynthesis. The present work aims
                      at show-casing the possibility to tightly integrate a neural
                      network with the domain knowledge of radiative transfer
                      codes. While DESIS has not been designed a-priori for SIF
                      retrieval, HyPlant is a testing instrument for the FLORIS
                      spectrometer onboard FLEX. The present work can thus make
                      use of the insights gained on high-resolution HyPlant data
                      for the more challenging SIF prediction on DESIS
                      acquisitions.We have developed a two-module simulation tool
                      to simulate DESIS and HyPlant at-sensor radiance spectra in
                      the O2-A band, which is particularly sensitive to SIF
                      changes. The first module runs MODTRAN6 on a set of
                      atmosphere and geometry parameters from which atmospheric
                      functions can be derived. The second module models the SIF
                      emission as well as surface and sensor properties. A
                      detailed analysis of appropriate input parameter ranges and
                      dense sampling allowed to generate spectra covering the most
                      common acquisition conditions of HyPlant and DESIS.We
                      tightly integrate the physical domain knowledge intrinsic in
                      the simulation data base and the network architecture by
                      using an emulator of the simulations as non-trainable output
                      layer. Simple emulator models have been tested in order to
                      reduce the complexity of the regression problem due to its
                      high dimensionality (11d -> 13d for DESIS, 13d -> 349d for
                      HyPlant). The training of our encoder-decoder network on
                      simulated and observed at-sensor spectra is conducted in a
                      semi- supervised fashion, where we use the network to
                      estimate appropriate emulator input parameters. The
                      label-free loss evaluates the residuals between the
                      corresponding emulator outputs and the data. This
                      semi-supervised training set-up allows a direct comparison
                      of the performance on simulated and observed data, where no
                      SIF labels are available. Moreover, it has the advantage of
                      allowing emulator transformations to reduce domain gaps
                      between simulations and observations. A combined training on
                      simulated and observed data with label regularization is
                      left for future work.[1] K. Alonso et al, “Data Products,
                      Quality and Validation of the DLR Earth Sensing Imaging
                      Spectrometer (DESIS)”, Sensors, 19(20), 4471, 2019.[2] B.
                      Siegmann et al, “The high-performance airborne imaging
                      spectrometer HyPlant – From raw images to top-of-canopy
                      reflectance and fluorescence products: Introduction of an
                      automatized processing chain.”, Remote Sensing, 11, 2760,
                      2019.s},
      month         = {Jun},
      date          = {2023-06-12},
      organization  = {Helmholtz AI Conference, Hamburg
                       (Germany), 12 Jun 2023 - 14 Jun 2023},
      subtyp        = {After Call},
      cin          = {IBG-2 / IAS-8},
      cid          = {I:(DE-Juel1)IBG-2-20101118 / I:(DE-Juel1)IAS-8-20210421},
      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-2023-04567},
      url          = {https://juser.fz-juelich.de/record/1018123},
}