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@INPROCEEDINGS{Dogar:1050589,
      author       = {Dogar, Sardar Salar Saeed and Brogi, Cosimo and O'Leary,
                      Dave and Donat, Marco and Vereecken, Harry and Huisman,
                      Johan Alexander},
      title        = {{D}elineating {A}gricultural {M}anagement {Z}ones using
                      {U}nsupervised {C}lassification of {E}lectromagnetic
                      {I}nduction and {R}emote {S}ensing {D}ata},
      reportid     = {FZJ-2026-00341},
      year         = {2025},
      abstract     = {An accurate delineation of management zones that reliably
                      characterizes within-field heterogeneity is essential to
                      optimize resources and improve yields in precision
                      agriculture. Non-invasive hydro-geophysical methods, such as
                      electromagnetic induction (EMI), offer a rapid approach to
                      delineating agricultural management zones that are based on
                      subsurface soil characteristics that influence crop growth.
                      Integrating additional data sources, such as remote sensing
                      imagery and yield maps, can further enhance the quality and
                      applicability of these management zones. However,
                      integrating above-ground and subsurface information from
                      multiple datasets for large agricultural fields poses
                      challenges in data harmonization, analysis, and
                      methodological consistency. Additionally, the impact of
                      different dataset combinations on management zone
                      delineation remains underexplored.In this study, we propose
                      a robust processing workflow that combines unsupervised
                      classification and statistical validation to delineate
                      management zones using proximal and remote sensing. This
                      method was applied to a 70-ha field of the patchCROP
                      experiment in Tempelberg (Germany). Part of this field
                      consists of 30 small patches (0.5 ha each) that are managed
                      separately since 2020. EMI data were collected in four
                      campaigns between 2022 and 2024 by using a CMD Mini-Explorer
                      and a CMD Mini-Explorer Special-Edition (featuring 3 and 6
                      coil separations, respectively). Maps of measured ECa were
                      standardized using z-score normalization (ECaz) to reduce
                      the effect of measuring in different environmental
                      conditions. Additionally, seven satellite images of the 2019
                      growing season with 3 m resolution (PlanetScope) were used
                      to obtain maps of NDVI development. Three dataset
                      combinations were investigated: 1) ECaz maps, 2) NDVI maps,
                      and 3) a combination of the EMI and NDVI maps. The
                      Self-Organizing Maps (SOM) machine learning technique was
                      used to cluster these three datasets. The optimal number of
                      clusters was determined using the Multi-Cluster Average
                      Standard Deviation (MCASD) method. Nine years (2011-2019) of
                      yield data and detailed soil information up to 100 cm depth
                      were used to refine the cluster numbers by using Tukey's
                      post-hoc analysis and to assess the accuracy of the
                      clustered maps with two-tailed t-tests in a subsequent
                      step.The EMI-based clustering resulted in 4 management
                      zones. A comparison of adjacent zones showed that 15 out of
                      21 soil properties and 23 out of 27 yield combinations were
                      statistically separated. The average p of all these
                      combinations was 0.113 and 0.045, respectively. The
                      NDVI-based clustering resulted in 3 zones with 10 out of 14
                      soil properties and 18 out of 18 yield combinations showing
                      significant separation (average p of 0.166 and 0.001,
                      respectively). Overall, the EMI-based zones better captured
                      the patterns in soil heterogeneity, whereas the NDVI-based
                      zones better matched yield maps. The combined EMI-NDVI
                      clustering resulted in 3 zones, and all the combinations of
                      soil properties and yield showed significant separation.
                      This EMI-NDVI derived 3 m resolution map better represented
                      soil properties and yield maps, highlighting the potential
                      of integrating multi-source datasets for field management
                      and, ultimately, agricultural productivity. It represents
                      the base for actionable insights not only for precision
                      agriculture applications such as fertilization and
                      irrigation, but also for environmental modelling or to guide
                      future sampling campaigns.},
      month         = {Apr},
      date          = {2025-04-27},
      organization  = {European Geosciences Union (EGU) 2025,
                       Vienna (Austria), 27 Apr 2025 - 2 May
                       2025},
      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          = {10.5194/egusphere-egu25-9440},
      url          = {https://juser.fz-juelich.de/record/1050589},
}