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@ARTICLE{Hebel:890646,
      author       = {Hebel, Christian and Reynaert, Sophie and Pauly, Klaas and
                      Janssens, Pieter and Piccard, Isabelle and Vanderborght, Jan
                      and Kruk, Jan and Vereecken, Harry and Garré, Sarah},
      title        = {{T}oward high‐resolution agronomic soil information and
                      management zones delineated by ground‐based
                      electromagnetic induction and aerial drone data},
      journal      = {Vadose zone journal},
      volume       = {20},
      number       = {4},
      issn         = {1539-1663},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {FZJ-2021-01096},
      pages        = {e20099},
      year         = {2021},
      abstract     = {Detailed knowledge of the intra-field variability of soil
                      properties and crop characteristics is indispensable for the
                      establishment of sustainable precision agriculture. We
                      present an approach that combines ground-based
                      agrogeophysical soil and aerial crop data to delineate
                      field-specific management zones that we interpret with soil
                      attribute measurements of texture, bulk density, and soil
                      moisture, as well as yield and nitrate residue in the soil
                      after potato (Solanum tuberosum L.) cultivation. To
                      delineate the management zones, we use aerial drone-based
                      normalized difference vegetation index (NDVI), spatial
                      electromagnetic induction (EMI) soil scanning, and the
                      EMI–NDVI data combination as input in a machine learning
                      clustering technique. We tested this approach in three
                      successive years on six agricultural fields (two per year).
                      The field-scale EMI data included spatial soil information
                      of the upper 0–50 cm, to approximately match the soil
                      depth sampled for attribute measurements. The NDVI
                      measurements over the growing season provide information on
                      crop development. The management zones delineated from EMI
                      data outperformed the management zones derived from NDVI in
                      terms of spatial coherence and showed differences in
                      properties relevant for agricultural management: texture,
                      soil moisture deficit, yield, and nitrate residue. The
                      combined EMI–NDVI analysis provided no extra benefit. This
                      underpins the importance of including spatially distributed
                      soil information in crop data interpretation, while
                      emphasizing that high-resolution soil information is
                      essential for variable rate applications and agronomic
                      modeling.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {217 - Für eine nachhaltige Bio-Ökonomie – von
                      Ressourcen zu Produkten (POF4-217) / 2173 -
                      Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-217 / G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000674053400011},
      doi          = {10.1002/vzj2.20099},
      url          = {https://juser.fz-juelich.de/record/890646},
}