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@ARTICLE{Morata:902156,
      author       = {Morata, Miguel and Siegmann, Bastian and
                      Morcillo-Pallarés, Pablo and Rivera-Caicedo, Juan Pablo and
                      Verrelst, Jochem},
      title        = {{E}mulation of {S}un-{I}nduced {F}luorescence from
                      {R}adiance {D}ata {R}ecorded by the {H}y{P}lant {A}irborne
                      {I}maging {S}pectrometer},
      journal      = {Remote sensing},
      volume       = {13},
      number       = {21},
      issn         = {2072-4292},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2021-04065},
      pages        = {4368 -},
      year         = {2021},
      abstract     = {The retrieval of sun-induced fluorescence (SIF) from
                      hyperspectral radiance data grewto maturity with research
                      activities around the FLuorescence EXplorer satellite
                      mission FLEX, yetfull-spectrum estimation methods such as
                      the spectral fitting method (SFM) are
                      computationallyexpensive. To bypass this computational load,
                      this work aims to approximate the SFM-based SIFretrieval by
                      means of statistical learning, i.e., emulation. While
                      emulators emerged as fast surrogatemodels of simulators, the
                      accuracy-speedup trade-offs are still to be analyzed when
                      the emulationconcept is applied to experimental data. We
                      evaluated the possibility of approximating the SFM-likeSIF
                      output directly based on radiance data while minimizing the
                      loss in precision as opposed to SFMbasedSIF. To do so, we
                      implemented a double principal component analysis (PCA)
                      dimensionalityreduction, i.e., in both input and output, to
                      achieve emulation of multispectral SIF output basedon
                      hyperspectral radiance data. We then evaluated
                      systematically: (1) multiple machine learningregression
                      algorithms, (2) number of principal components, (3) number
                      of training samples, and(4) quality of training samples. The
                      best performing SIF emulator was then applied to a
                      HyPlantflight line containing at sensor radiance
                      information, and the results were compared to the SFM SIFmap
                      of the same flight line. The emulated SIF map was
                      quasi-instantaneously generated, and a goodagreement against
                      the reference SFM map was obtained with a R2 of 0.88 and
                      NRMSE of $3.77\%.The$ SIF emulator was subsequently applied
                      to 7 HyPlant flight lines to evaluate its robustness
                      andportability, leading to a R2 between 0.68 and 0.95, and a
                      NRMSE between $6.42\%$ and $4.13\%.$ EmulatedSIF maps proved
                      to be consistent while processing time was in the order of 3
                      min. In comparison, theoriginal SFM needed approximately 78
                      min to complete the SIF processing. Our results suggest
                      thatemulation can be used to efficiently reduce
                      computational loads of SIF retrieval methods.},
      cin          = {IBG-2},
      ddc          = {620},
      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},
      UT           = {WOS:000718576000001},
      doi          = {10.3390/rs13214368},
      url          = {https://juser.fz-juelich.de/record/902156},
}