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@INPROCEEDINGS{Dogar:1037664,
      author       = {Dogar, Sardar Salar Saeed and Brogi, Cosimo and Donat,
                      Marco and Vereecken, Harry and Huisman, Johan Alexander},
      title        = {{U}se of electromagnetic induction and remote sensing
                      datasets to characterize spatial variability in soil
                      properties for sustainable farming},
      reportid     = {FZJ-2025-00829},
      year         = {2024},
      abstract     = {Within-field soil variability significantly influences
                      water and nutrients availability, which in turn affects crop
                      growth and yield. A comprehensive understanding of soil
                      characteristics is thus necessary in sustainable
                      agriculture, which demands both above and below-surface soil
                      sensing. Commonly used sensing methods include
                      electromagnetic induction (EMI) mapping and remote sensing
                      of the normalized difference vegetation index (NDVI).
                      Previous studies have harnessed EMI data to characterize the
                      impact of soil heterogeneity on crop production, utilizing
                      classification techniques in combination with soil maps and
                      remote sensing data. However, there is further potential in
                      combining proximal sensing, remote sensing, and yield maps
                      in a fully integrated manner. This combination may result in
                      the delineation of agricultural management zones that can
                      account for a more holistic range of factors that affect
                      crop development. This study focuses on a 70-hectare field
                      of the PatchCrop living lab in Tempelberg, Brandenburg. EMI
                      measurements were performed with two systems recording nine
                      different coil separations that provide information on
                      different subsurface depth ranges. Three field campaigns
                      between August 2022 and 2023 have been conducted. The
                      analysis presented here is focused on the 2019 growing
                      season, where 19 NDVI images obtained from high-resolution
                      PlanetScope satellite were available. In addition,
                      historical yield maps from 2011 to 2019 are available. In
                      this study, we used unsupervised classification approaches
                      to derive more holistic management zones using a combination
                      of NDVI maps and normalized EMI maps. The results of
                      clustering are compared with yield maps to assess the
                      efficacy of the derived management zones.},
      month         = {Feb},
      date          = {2024-02-01},
      organization  = {Agriculture and geophysics:
                       Illuminating the subsurface, Zürich
                       (Switzerland), 1 Feb 2024 - 2 Feb 2024},
      subtyp        = {After Call},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / DFG project G:(GEPRIS)390732324 - EXC 2070:
                      PhenoRob - Robotik und Phänotypisierung für Nachhaltige
                      Nutzpflanzenproduktion (390732324)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(GEPRIS)390732324},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1037664},
}