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@INPROCEEDINGS{Chakhvashvili:910498,
      author       = {Chakhvashvili, Erekle and Bendig, Juliane and Siegmann,
                      Bastian and Muller, Onno and Verrelst, Jochem and Rascher,
                      Uwe},
      title        = {{LAI} and {L}eaf {C}hlorophyll {C}ontent {R}etrieval
                      {U}nder {C}hanging {S}patial {S}cale {U}sing a
                      {UAV}-{M}ounted {M}ultispectral {C}amera},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-03881},
      pages        = {7891-7894},
      year         = {2022},
      abstract     = {Recent advancements in unmanned aerial vehicle (UAV)
                      technologies made it possible to monitor agricultural fields
                      at higher spatial and temporal resolution than commonly
                      possible by aerial and satellite surveys. Mapping crop
                      variables such as leaf area index (LAI) and leaf chlorophyll
                      content (LCC) from low-cost UAV-based multispectral cameras
                      can deliver vital information about crop status to farmers
                      and plant breeders. Retrieval of these variables using
                      radiative transfer models (RTMs) has been widely studied in
                      the satellite remote sensing community but is still not well
                      explored in the UAV remote sensing community. This study
                      aims to investigate the advantages of high spatial
                      resolution UAV image data for retrieving LAI and LCC using
                      RTM inversion. A breeding experiment consisting of soybean
                      plots has shown that high-resolution imagery (0.015m)
                      delivers better retrieval accuracy compared to coarser
                      resampled image data. Particularly, biochemical parameters,
                      such as LCC, benefit from high spatial resolution.},
      month         = {Jul},
      date          = {2022-07-17},
      organization  = {IGARSS 2022 - 2022 IEEE International
                       Geoscience and Remote Sensing
                       Symposium, Kuala Lumpur (Malaysia), 17
                       Jul 2022 - 22 Jul 2022},
      cin          = {IBG-2},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / EXC 2070:  PhenoRob - Robotics and Phenotyping
                      for Sustainable Crop Production (390732324)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(BMBF)390732324},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:000920916607199},
      doi          = {10.1109/IGARSS46834.2022.9883446},
      url          = {https://juser.fz-juelich.de/record/910498},
}