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@ARTICLE{PachecoLabrador:1032256,
      author       = {Pacheco-Labrador, Javier and Cendrero-Mateo, M. Pilar and
                      Van Wittenberghe, Shari and Hernandez-Sequeira, Itza and
                      Koren, Gerbrand and Prikaziuk, Egor and Fóti, Szilvia and
                      Tomelleri, Enrico and Maseyk, Kadmiel and Čereković,
                      Nataša and Gonzalez-Cascon, Rosario and Malenovský,
                      Zbyněk and Albert-Saiz, Mar and Antala, Michal and Balogh,
                      János and Buddenbaum, Henning and Dehghan-Shoar, Mohammad
                      Hossain and Fennell, Joseph T. and Féret, Jean-Baptiste and
                      Balde, Hamadou and Machwitz, Miriam and Mészáros, Ádám
                      and Miao, Guofang and Morata, Miguel and Naethe, Paul and
                      Nagy, Zoltán and Pintér, Krisztina and Pullanagari, R.
                      Reddy and Rastogi, Anshu and Siegmann, Bastian and Wang,
                      Sheng and Zhang, Chenhui and Kopkáně, Daniel},
      title        = {{E}cophysiological variables retrieval and early stress
                      detection: insights from a synthetic spatial scaling
                      exercise},
      journal      = {International journal of remote sensing},
      volume       = {46},
      number       = {1},
      issn         = {0143-1161},
      address      = {London},
      publisher    = {Taylor $\&$ Francis},
      reportid     = {FZJ-2024-06100},
      pages        = {443-468},
      year         = {2025},
      abstract     = {The ability to access physiologically driven signals, such
                      as surfacetemperature, photochemical reflectance index
                      (PRI), and suninducedchlorophyll fluorescence (SIF), through
                      remote sensing(RS) are exciting developments for vegetation
                      studies. Accessingthis ecophysiological information requires
                      considering processesoperating at scales from the
                      top-of-the-canopy to the photosystems,adding complexity
                      compared to reflectance index-basedapproaches. To
                      investigate the maturity and knowledge of thegrowing RS
                      community in this area, COST Action CA17134SENSECO organized
                      a Spatial Scaling Challenge (SSC). Challengeparticipants
                      were asked to retrieve four key ecophysiological
                      variablesfor a field each of maize and wheat from a
                      simulated fieldcampaign: leaf area index (LAI), leaf
                      chlorophyll content (Cab), maximumcarboxylation rate
                      (Vcmax,25), and non-photochemicalquenching (NPQ). The
                      simulated campaign data included hyperspectraloptical,
                      thermal and SIF imagery, together with groundsampling of the
                      four variables. Non-parametric methods that combinedmultiple
                      spectral domains and field measurements were usedmost often,
                      thereby indirectly performing the top-of-the-canopy
                      tophotosystem scaling. LAI and Cab were reliably retrieved
                      in mostcases, whereas Vcmax,25 and NPQ were less accurately
                      estimated anddemanded information ancillary to RS imagery.
                      The factors consideredleast by participants were the
                      biophysical and physiologicalcanopy vertical profiles, the
                      spatial mismatch between RS sensors,the temporal mismatch
                      between field sampling and RS acquisition,and measurement
                      uncertainty. Furthermore, few participantsdeveloped NPQ maps
                      into stress maps or provided a deeper analysisof their
                      parameter retrievals. The SSC shows that, despiteadvances in
                      statistical and physically based models, the vegetationRS
                      community should improve how field and RS data are
                      integratedand scaled in space and time. We expect this work
                      will guide newcomersand support robust advances in this
                      research field.},
      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:001343869000001},
      doi          = {10.1080/01431161.2024.2414435},
      url          = {https://juser.fz-juelich.de/record/1032256},
}