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@ARTICLE{Wieneke:819466,
      author       = {Wieneke, S. and Ahrends, H. and Damm, A. and Pinto, F. and
                      Stadler, A. and Rossini, M. and Rascher, U.},
      title        = {{A}irborne based spectroscopy of red and far-red
                      sun-induced chlorophyll fluorescence: {I}mplications for
                      improved estimates of gross primary productivity},
      journal      = {Remote sensing of environment},
      volume       = {184},
      issn         = {0034-4257},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2016-05130},
      pages        = {654 - 667},
      year         = {2016},
      abstract     = {Remote sensing (RS) approaches commonly applied to
                      constrain estimates of gross primary production (GPP) employ
                      greenness-based vegetation indices derived from surface
                      reflectance data. Such approaches cannot capture dynamic
                      changes of photosynthesis rates as caused by environmental
                      stress. Further, applied vegetation indices are often
                      affected by background reflectance or saturation effects.
                      Sun. induced chlorophyll fluorescence (F) provides the most
                      direct measure of photosynthesis and has been recently
                      proposed as a new RS approach to improve estimates of GPP
                      and tracing plant stress reactions. This work aims to
                      provide further evidence on the complementary information
                      content of F and its relation to changes in photosynthetic
                      activity compared to traditional RS approaches. We use the
                      airborne imaging spectrometer HyPlant to obtain several F
                      products including red fluorescence (F687), far-red
                      fluorescence (F760), F760 yield (F760yield) and the ration
                      between F687 and F760 (Fratio). We calculate several
                      vegetation indices indicative for vegetation greenness. We
                      apply a recently proposed F-based semi-mechanistic approach
                      to improve the forward modeling of GPP using F760 and
                      compare this approach with a traditional one based on
                      vegetation greenness and ground measurements of GPP derived
                      from chamber measurements. In addition, we assess the
                      sensitivity of F760yield and Fratio for environmental
                      stress. Our results show an improved predictive capability
                      of GPP when using F760 compared to greenness-based
                      vegetation indices. F760yield and Fratio show a strong
                      variability in time and between different crop types
                      suffering from different levels of water shortage,
                      indicating a strong sensitivity of F products for plant
                      stress reactions. We conclude that the new RS approach of F
                      provides complements to the set of commonly applies RS: The
                      use of F760 improves constraining estimates of GPP while the
                      ratio of red and far-red F shows large potential for
                      tracking spatio-temporal plant adaptation in response to
                      environmental stress conditions.},
      cin          = {IBG-2},
      ddc          = {050},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582)},
      pid          = {G:(DE-HGF)POF3-582},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000383827800049},
      doi          = {10.1016/j.rse.2016.07.025},
      url          = {https://juser.fz-juelich.de/record/819466},
}