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@ARTICLE{Buffat:1050415,
      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}mulation-based self-supervised {SIF} retrieval in the
                      {O}$_2$-{A} absorption band with {H}y{P}lant},
      journal      = {Remote sensing of environment},
      volume       = {334},
      issn         = {0034-4257},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2026-00185},
      pages        = {115203 -},
      year         = {2026},
      abstract     = {The retrieval of sun-induced fluorescence (SIF) from
                      hyperspectral imagery requires accurate atmospheric
                      compensation to correctly disentangle its small contribution
                      to the at-sensor radiance from other confounding factors. In
                      spectral fitting SIF retrieval approaches this compensation
                      is estimated in a joint optimization of free variables when
                      fitting the measured at-sensor signal. Due to the
                      computational complexity of Radiative Transfer Models (RTMs)
                      that satisfy the level of precision required for accurate
                      SIF retrieval, fully joint estimations are practically
                      unachievable with exact physical simulation. We present in
                      this contribution an emulator-based spectral fitting method
                      neural network (EmSFMNN) approach integrating RTM emulation
                      and self-supervised training for computationally efficient
                      and accurate SIF retrieval in the O$_2$-A absorption band of
                      HyPlant imagery. In a validation study with in-situ
                      top-of-canopy SIF measurements we find improved performance
                      over traditional retrieval methods. Furthermore, we show
                      that the model predicts plausible SIF emission in
                      topographically variable terrain without scene-specific
                      adaptations. Since EmSFMNN can be adapted to hyperspectral
                      imaging sensors in a straightforward fashion, it may prove
                      to be an interesting SIF retrieval method for other sensors
                      on airborne and spaceborne platforms.},
      cin          = {IAS-8 / IBG-2},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IAS-8-20210421 / I:(DE-Juel1)IBG-2-20101118},
      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)16},
      doi          = {10.1016/j.rse.2025.115203},
      url          = {https://juser.fz-juelich.de/record/1050415},
}