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@ARTICLE{Morata:916745,
      author       = {Morata, Miguel and Siegmann, Bastian and Perez-Suay, Adrian
                      and Garcia-Soria, Jose Luis and Rivera-Caicedo, Juan Pablo
                      and Verrelst, Jochem},
      title        = {{N}eural {N}etwork {E}mulation of {S}ynthetic
                      {H}yperspectral {S}entinel-2-like {I}magery with
                      {U}ncertainty},
      journal      = {IEEE journal of selected topics in applied earth
                      observations and remote sensing},
      volume       = {16},
      issn         = {1939-1404},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2023-00073},
      pages        = {762-772},
      year         = {2023},
      abstract     = {Hyperspectral satellite imagery provides highly-resolved
                      spectral information for large areas and can provide vital
                      information. However, only a few imaging spectrometer
                      missions are currently in operation. Aiming to generate
                      synthetic satellite-based hyperspectral imagery potentially
                      covering any region, we explored the possibility of applying
                      statistical learning, i.e. emulation. Based on the
                      relationship of a Sentinel-2 (S2) scene and a hyperspectral
                      HyPlant airborne image, this work demonstrates the
                      possibility to emulate a hyperspectral S2-like image. We
                      tested the role of different machine learning regression
                      algorithms (MLRA) and varied the image-extracted training
                      dataset size. We found superior performance of Neural
                      Network (NN) as opposed to the other algorithms when trained
                      with large datasets (up to 100'000 samples). The developed
                      emulator was then applied to the L2A (bottom-of-atmosphere
                      reflectance) S2 subset, and the obtained S2-like
                      hyperspectral reflectance scene was evaluated. The
                      validation of emulated against reference spectra
                      demonstrated the potential of the technique. R2 values
                      between 0.75-0.9 and NRMSE between $2-5\%$ across the full
                      402-2356 nm range were obtained. Moreover, epistemic
                      uncertainty is obtained using the dropout technique,
                      revealing spatial fidelity of the emulated scene. We
                      obtained highest SD values of 0.05 (CV of $8\%)$ in clouds
                      and values below 0.01 (CV of $7\%)$ in vegetation land
                      covers. Finally, the emulator was applied to an entire S2
                      tile (5490x5490 pixels) to generate a hyperspectral
                      reflectance datacube with the texture of S2 (60Gb, at a
                      speed of 0.14sec/10000pixels). As the emulator can convert
                      any S2 tile into a hyperspectral image, such scenes give
                      perspectives how future satellite imaging spectroscopy will
                      look like.},
      cin          = {IBG-2},
      ddc          = {520},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36644656},
      UT           = {WOS:000981757000002},
      doi          = {10.1109/JSTARS.2022.3231380},
      url          = {https://juser.fz-juelich.de/record/916745},
}