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@PHDTHESIS{Salattna:1048364,
      author       = {Salattna, Saja},
      title        = {{UAV}-based {I}maging of {M}ultispectral {R}eflectance and
                      {S}olar-{I}nduced {C}hlorophyll {F}luorescence for {C}rop
                      {M}onitoring},
      school       = {Rheinische Friedrich-Wilhelms-Universität Bonn},
      type         = {Dissertation},
      reportid     = {FZJ-2025-04584},
      pages        = {129},
      year         = {2025},
      note         = {Dissertation, Rheinische Friedrich-Wilhelms-Universität
                      Bonn, 2025},
      abstract     = {The agri-food sector is facing significant challenges due
                      to climate change, unpredictable weather, and rapid
                      population growth. The growing demand to embrace advanced
                      agricultural systems that boost productivity while
                      mitigating climate impacts requires accurate and reliable
                      crop monitoring techniques. In this context, site-specific
                      farm management and remote sensing have become
                      indispensable. Remote sensing offers real-time information
                      about crop growth and health throughout the growing season
                      at different scales. UAV-based remote sensing, in
                      particular, offers a cost-effective tool for monitoring crop
                      growth and health with high spatiotemporal resolution that
                      enables response to field-scale issues by driving informed
                      decision-making. Our study contributes to this evolving
                      landscape by exploring the potential of UAV-based
                      multispectral imaging in crop monitoring on the field
                      scale.In the first study, high-resolution imagery from the
                      DJI Phantom 4 multispectral UAV system was employed to
                      monitor the seasonal development of spelt in a
                      biochar-enriched experiment. A straightforward data
                      processing workflow was developed based on an empirical line
                      method to convert raw UAV data to normalized and comparable
                      reflectance maps and calculate various vegetation indices.
                      Results indicated that EVI was the most effective index in
                      relation to the actual yield, indicating better spelt
                      development over the biochar-enriched plots with a full
                      conventional fertilization amount compared to controls that
                      received the same conventional fertilization.The second
                      study addressed the retrieval of sun-induced fluorescence,
                      F760, from SIFcam, a dual-camera system prototype mounted on
                      a UAV. A comprehensive overview and advancements of the
                      developed methodology for SIFcam imagery, in addition to a
                      second innovative workflow, were presented. The F760
                      retrieved from the two workflows showed strong correlations
                      with ground-based measurements (R² = 0.92) and moderate
                      correlations with airborne imaging spectrometer HyPlant (R²
                      = 0.56, 0.52 for workflows 1 and 2b, respectively). The
                      SIFcam has shown its capability to effectively disentangle
                      the fluorescence signal from canopy reflectance with a
                      moderate level of accuracy and adequate stability in data
                      collection at the field scale, with less than one-pixel
                      variation between spectral channels in both horizontal and
                      vertical directions.The third study investigated the
                      potential of integrating SIFcam F760 alongside UAV-based
                      multispectral VIs to characterize and delineate diverse new
                      and old winter wheat cultivars. SIFcam demonstrated a
                      notable potential in capturing the variability of F760
                      between wheat cultivars with structural and pigment
                      differences. New wheat cultivars generally revealed higher
                      F760, consistent with their higher chlorophyll content, yet
                      old cultivar Banco indicated that canopy architecture could
                      significantly modulate TOC F760, with F760 values comparable
                      to or even exceeding those of certain new cultivars. VIs
                      sensitive to chlorophyll content, particularly TCARI/OSAVI
                      (Cohen's d >= 0.5), outperformed structure-related VIs and
                      F760 for distinguishing the cultivars. SIFcam proved to be a
                      valuable tool for field plant phenotyping and potentially
                      guiding breeding programs.},
      cin          = {IBG-2},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582) / 2171 - Biological and
                      environmental resources for sustainable use (POF4-217)},
      pid          = {G:(DE-HGF)POF3-582 / G:(DE-HGF)POF4-2171},
      typ          = {PUB:(DE-HGF)11},
      urn          = {https://nbn-resolving.org/urn:nbn:de:hbz:5-86026},
      doi          = {10.34734/FZJ-2025-04584},
      url          = {https://juser.fz-juelich.de/record/1048364},
}