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@INPROCEEDINGS{Quirs:888900,
      author       = {Quirós, Juan and Brogi, Cosimo and Krieger, Vera and
                      Siegmann, Bastian and Celesti, Marco and Rossini, Micol and
                      Cogliati, Sergio and Weihermüller, Lutz and Rascher, Uwe},
      title        = {{S}olar {I}nduced {C}hlorophyll {F}luorescence and
                      {V}egetation {I}ndices for {H}eat {S}tress {A}ssessment in
                      {T}hree {C}rops at {D}ifferent {G}eophysics-{D}erived {S}oil
                      {U}nits},
      reportid     = {FZJ-2020-05305},
      pages        = {2},
      year         = {2020},
      abstract     = {Remotely-sensed Solar Induced chlorophyll Fluorescence
                      (SIF) is a novel promising tool to retrieve information on
                      plants’ physiological status due to its direct link with
                      the photosynthetic process. At the same time, narrow band
                      Vegetation Indices (VIs) such as the MERIS Terrestrial
                      chlorophyll index (MTCI), and the Photochemical Reflectance
                      Index (PRI), as well as broad band VIs like the Normalized
                      Difference Vegetation Index (NDVI), have been widely used
                      for crop stress assessment. A match between these remote
                      sensing products and the spatial distribution of soil units
                      is expected; nevertheless, an in-depth analysis of such
                      relationship has been rarely performed so that additional
                      studies are required.In this contribution, we aimed at the
                      comparison in the use of normalized SIF (SIF =SIF/PAR;
                      computed with the Spectral Fitting Method, SFM) and VIs
                      (MTCI, PRI and NDVI) for heat stress assessment in corn,
                      sugar beet and potato at the beginning and towards the end
                      of a heatwave occurring in Selhausen, Germany, 2018. Data
                      were acquired with the HyPlant airborne sensor, which is a
                      high performance imaging spectrometer with around 0.30 nm of
                      spectral resolution in the Oxygen absorption bands. We
                      compared different plots located in the upper (poorer soil
                      characteristics for agriculture such as water holding
                      capacity and content of coarse sediments) or lower landscape
                      terraces; we also evaluated the different remote sensing
                      products in comparison with site specific geophysics-based
                      soil maps.At the beginning of the heat wave we found that,
                      compared with VIs, SIF data showed a clearer differentiation
                      of the stress conditions at a terrace level in potato and
                      sugar beet. However, towards the end of the wave a
                      significant decrease of MTCI and NDVI contrasted with higher
                      SIF in sugar beet and corn. Nonetheless, those crops (sugar
                      beet and corn) did not show significant SIF differences
                      between terraces. A significant spatial match was found
                      between SIF and geophysics-derived soil spatial patterns (p
                      = 0.004-0.030) in fields where NDVI was more homogeneous (p
                      = 0.028-0.499, respectively). This suggests the higher
                      sensitivity of SIF to monitor heat stress compared with
                      common VIs.},
      month         = {Dec},
      date          = {2020-12-01},
      organization  = {American Geophysical Union (AGU) Fall
                       Meeting, Online (United States), 1 Dec
                       2020 - 17 Dec 2020},
      cin          = {IBG-2 / IBG-3},
      cid          = {I:(DE-Juel1)IBG-2-20101118 / I:(DE-Juel1)IBG-3-20101118},
      pnm          = {89582 - Plant Science (POF2-89582) / TRuStEE - Training on
                      Remote Sensing for Ecosystem modElling (721995)},
      pid          = {G:(DE-HGF)POF2-89582 / G:(EU-Grant)721995},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.1002/essoar.10504968.1},
      url          = {https://juser.fz-juelich.de/record/888900},
}