% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Buffat:1018121,
      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 Scharr, Hanno},
      title        = {{A} {N}ovel {S}elf-{S}upervised {S}un-{I}nduced
                      {F}luorescence {R}etrieval {U}sing {S}imulated {H}y{P}lant
                      and {DESIS} {D}ata},
      reportid     = {FZJ-2023-04565},
      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     = {Operationally efficient retrieval of sun-induced
                      fluorescence (SIF) from remote sensing data requires exact
                      atmospheric correction at low computational cost to
                      compensate for atmospheric effects at play in the radiative
                      transfer of light through the atmosphere. Incomplete
                      knowledge not only of the atmospheric state at acquisition
                      time, but also of surface conditions imply the formulation
                      of SIF retrieval as a simultaneous search of the
                      atmospheric, viewing geometry and surface related variables
                      describing best the observed scene.Such a problem can be
                      addressed by the construction of large look-up tables (LUT)
                      covering the space of possible input parameters describing
                      the hyperspectral observations. The SIF retrieval reduces to
                      spectrally fitting this LUT to the observations. In the
                      present contribution we investigate the use of a neural
                      network to perform this optimization step. We show-case the
                      possibility to tightly integrate a neural network with the
                      domain knowledge of radiative transfer codes simulating
                      observations of the airborne HyPlant instrument and the
                      ISS-based DESIS spectrometer in a spectral window around the
                      O2-A oxygen absorption band (740-780 nm). While DESIS has
                      not been designed 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 generic
                      simulation tool based on MODTRAN6 to simulate at-sensor
                      radiance in the O2-A oxygen absorption band. The tool
                      consists of two modules. A primary module simulates
                      distributions of atmospheric functions under varying
                      atmospheric state variables. The high spectral resolution of
                      DESIS (full width at half maximum 3.5 nm) and HyPlant (0.25
                      nm) required us to run MODTRAN6 at the finest available
                      resolution. A secondary module modeling the sensor and
                      surface characteristics factors in expert knowledge on DESIS
                      and HyPlant as well as constraints on the SIF signal. We
                      sampled an exhaustive range of observable atmospheric and
                      surface conditions based on an extensive sensitivity
                      analysis of key input parameters.Due to its computational
                      complexity and cost the simulation tool can not be included
                      in the network predictor directly. In order to tightly
                      integrate the physical domain knowledge with a neural
                      network predictor we tested a set of models to emulate
                      at-sensor radiance using the simulated database. The
                      high-dimensional nature of the regression problem (11d ->
                      13d for DESIS, 13d -> 349 for HyPlant) required us to test
                      relatively simple models. We found fourth-degree polynomials
                      to perform best with at-sensor radiance residuals of less
                      than 0.01 mW/cm2/sr/μm on average across our simulated test
                      set for both DESIS and HyPlant. Importantly, the per sample
                      prediction time is of the order 10 μs, i.e. 107 times
                      faster than the original simulation. We integrated this
                      emulator as a prediction head into an encoder-decoder type
                      neural network and trained the network in a first step on
                      observed HyPlant imagery. Application of this approach to
                      DESIS data is left for future work. A self- supervised loss
                      evaluating the radiance residual of the emulated
                      reconstruction and a physiological constraint was
                      implemented to this end. Furthermore, the inclusion of
                      topographic variation in the simulation data base as well as
                      spatial constraints in the network architecture allow for
                      SIF prediction even in topographically complex
                      terrain.Validation of our self-supervised method’s
                      predictions with ground based SIF measurements and
                      comparison with the state-of-the-art Spectral Fitting Method
                      (SFM) and Improved Fraunhofer Line Detection (iFLD) baseline
                      methods revealed that the presented approached yielded
                      comparable correlation scores. Linear calibration is,
                      however, still needed to reach state-of-the-art absolute
                      prediction accuracy as the SIF predictions suffer from
                      systematic biases. Further plausibility studies highlight
                      that the network yields physiologically plausible diurnal
                      SIF dynamics and compensates for changes in the atmospheric
                      path in topographically complex terrain.In summary, we have
                      developed a SIF prediction approach integrating a
                      computationally efficient and precise emulator in a neural
                      network architecture derived from extensive sampling and
                      high precision simulations of observational conditions.
                      Validation and plausibility studies could show- case its
                      successful application to HyPlant imagery.},
      month         = {Sep},
      date          = {2023-09-19},
      organization  = {Flex Fluorescence Workshop 2023,
                       Frascati (Italy), 19 Sep 2023 - 21 Sep
                       2023},
      subtyp        = {Invited},
      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)24},
      doi          = {10.34734/FZJ-2023-04565},
      url          = {https://juser.fz-juelich.de/record/1018121},
}