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@INPROCEEDINGS{Dogar:1037659,
      author       = {Dogar, Sardar Salar Saeed and Brogi, Cosimo and Donat,
                      Marco and Vereecken, Harry and Huisman, Johan Alexander},
      title        = {{D}ata fusion and classification of electromagnetic
                      induction and remote sensing data for management zone
                      delineation in sustainable agriculture},
      reportid     = {FZJ-2025-00824},
      year         = {2024},
      abstract     = {A precise and reliable characterization of intra-field
                      heterogeneity of soil properties and water content is vital
                      in precision agriculture as these significantly impact crop
                      growth and yield. Non-invasive hydrogeophysical methods such
                      as electromagnetic induction (EMI) can be used to delineate
                      intra-field agricultural management zones that represent
                      areas where field characteristics tend to be homogeneous and
                      have similar impact on crops. The combination with
                      additional data sources, for example, remote sensing or
                      yield maps, has the potential to improve the quality of the
                      management zones. However, extracting subsurface information
                      from multiple datasets and for large agricultural fields
                      poses several challenges in data harmonization and analysis.
                      The selection of optimal dataset combinations and the
                      influence of different data products on the creation of
                      management zones have also not been sufficiently
                      investigated. In this study, we present an approach to
                      produce intra-field management zones that combines a)
                      electromagnetic induction (EMI) measurements performed with
                      a CMD Mini-Explorer and a CMD Mini-Explorer Special-Edition
                      (with 3 and 6 coil separation, respectively) and b)
                      normalized difference vegetation index (NDVI) from
                      PlanetScope satellite imagery. The method was tested on a
                      70-ha field of the PatchCrop experiment in Tempelberg,
                      Brandenburg (Germany). This field is challenging to
                      investigate as it contains 30 small patches of 0.5 ha (72 x
                      72m) that are managed separately. EMI measurements were
                      collected in three different campaigns in 2022 and 2023
                      depending on the availability of these small patches. The
                      EMI data were automatically filtered, temperature corrected,
                      and interpolated onto a 1x1 meter resolution grid.
                      Furthermore, EMI measurements were normalized by testing
                      different methodologies (min-max, log, and z-transformation)
                      to reduce the influence of measuring in different periods.
                      Satellite NDVI maps with 3 m resolution for selected years
                      within the period 2019-2023 were obtained from PlanetScope
                      and provided information on crop development over the
                      growing season. For validation, yield maps with 10 m
                      resolution for the period 2011-2019 were available. Both the
                      EMI and the NDVI maps revealed the presence of sub-surface
                      heterogeneities that clearly impact plant productivity, but
                      their patterns did not fully match. To delineate
                      agricultural management zones, ISODATA and K-means
                      clustering algorithms were employed by using a) EMI data, b)
                      NDVI maps, and c) a combination of these datasets.
                      Silhouette and elbow methods were used to identify the
                      optimal number of clusters. The adequacy of the resulting
                      management zones was assessed by comparing them to the
                      available yield maps. The results revealed that a
                      combination of EMI and NDVI datasets could often improve the
                      spatial representation of yield patterns, which confirms the
                      relevance of this method for precision agriculture.
                      Nonetheless, further research is needed to assess the
                      relevance of each dataset and to evaluate the applicability
                      in different regions and contexts.},
      month         = {Apr},
      date          = {2024-04-14},
      organization  = {European Geosciences Union (EGU) 2024,
                       Vienna (Austria), 14 Apr 2024 - 19 Apr
                       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},
      doi          = {10.34734/FZJ-2025-00824},
      url          = {https://juser.fz-juelich.de/record/1037659},
}