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@ARTICLE{Brogi:851722,
      author       = {Brogi, C. and Huisman, J. A. and Pätzold, S. and von
                      Hebel, C. and Weihermüller, L. and Kaufmann, Manuela and
                      van der Kruk, J. and Vereecken, H.},
      title        = {{L}arge-scale soil mapping using multi-configuration {EMI}
                      and supervised image classification},
      journal      = {Geoderma},
      volume       = {335},
      issn         = {0016-7061},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2018-05254},
      pages        = {133 - 148},
      year         = {2019},
      abstract     = {Reliable and high-resolution subsurface characterization
                      beyond the field scale is of great interest for precision
                      agriculture and agro-ecological modelling because the
                      shallow soil (~1–2m depth) is responsible for the
                      storageof moisture and nutrients that are accessible to
                      crops. This can potentially be achieved with a combination
                      of direct sampling and Electromagnetic Induction (EMI)
                      measurements, which have shown great potential for
                      soilcharacterization due to their non-invasive nature and
                      high mobility. However, only a few studies have used EMI
                      beyond the field scale because of the challenges associated
                      with a consistent interpretation of EMI data frommultiple
                      fields and acquisition days. In this study, we performed a
                      detailed EMI survey of an area of 1 km2 divided in 51
                      agricultural fields where previous studies showed a clear
                      connection between crop performanceand soil properties. In
                      total, nine apparent electrical conductivity (ECa) values
                      were measured at each location with a depth of investigation
                      ranging between 0–0.2 to 0–2.7 m. Based on the
                      combination of ECa maps andavailable soil maps, an a priori
                      interpretation was performed and four sub-areas with
                      characteristic sediments and ECa were identified. Then, a
                      supervised classification methodology was used to divide the
                      ECa maps intoareas with similar soil properties. In a next
                      step, soil profile descriptions to a depth of 2m were
                      obtained at 100 sampling locations and 552 samples were
                      analyzed for textural characteristics. The combination of
                      the classifiedmap and ground truth data resulted in a 1m
                      resolution soil map with eighteen units with a typical soil
                      profile and texture information. It was found that the soil
                      profile descriptions and texture of the EMI-based soil
                      classes were significantly different when compared using a
                      two-tailed t-test. Moreover, the high-resolution soil map
                      corresponded well with patterns in crop health obtained from
                      satellite imagery. It was concluded that this novel EMI data
                      processing approach provides a reliable and cost-effective
                      tool to obtain high-resolution soil maps to support
                      precision agriculture and agro-ecological modelling.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / IRTG, Graduate School - Patterns in
                      Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling
                      and Data Assimilation (TR32) (IRTG, Graduate School)
                      (IRTG-GRADUATE-20170406)},
      pid          = {G:(DE-HGF)POF3-255 / G:(DE-Juel1)IRTG-GRADUATE-20170406},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000447095700014},
      doi          = {10.1016/j.geoderma.2018.08.001},
      url          = {https://juser.fz-juelich.de/record/851722},
}