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@ARTICLE{OLeary:1021940,
      author       = {O'Leary, Dave and Brogi, Cosimo and Brown, Colin and Tuohy,
                      Pat and Daly, Eve},
      title        = {{L}inking electromagnetic induction data to soil properties
                      at field scales aided by neural network clustering},
      journal      = {Frontiers in soil science},
      volume       = {4},
      issn         = {2673-8619},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2024-01080},
      pages        = {1346028},
      year         = {2024},
      abstract     = {The mapping of soil properties, such as soil texture, at
                      the field scale is important in the context of national
                      agricultural planning/policy and precision agriculture.
                      Electromagnetic Induction (EMI) surveys are commonly used to
                      measure soil apparent electrical conductivity and can
                      provide valuable insights into such subsurface properties.
                      Multi-receiver or multi-frequency instruments provide a
                      vertical distribution of apparent conductivity beneath the
                      instrument, while the mobility of such instruments allows
                      for spatial coverage. Clustering is the grouping together of
                      similar multidimensional data, such as the processed EMI
                      data over a field. A neural network clustering process,
                      where the number of clusters can be objectively determined,
                      results in a set of one-dimensional apparent electrical
                      conductivity cluster centers, which are representative of
                      the entire threedimensional dataset. These cluster centers
                      are used to guide inversions of apparent conductivity data
                      to give an estimate of the true electrical conductivity
                      distribution at a site. The method is applied to two sites
                      and the results demonstrate a correlation between (true)
                      electrical conductivity with sampled soil texture (sampled
                      prior to the EMI surveys) which is superior to correlations
                      where no clustering is included. The method has the
                      potential to be developed further, with the aim of improving
                      the prediction of soil properties at cluster scale, such as
                      texture, from EMI data. A particularly important conclusion
                      from this initial study is that EMI data should be acquired
                      prior to a focused soil sampling campaign to calibrate the
                      electrical conductivitysoil property correlations.},
      cin          = {IBG-3},
      ddc          = {630},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / DFG project 357874777 - FOR 2694: Large-Scale
                      and High-Resolution Mapping of Soil Moisture on Field and
                      Catchment Scales - Boosted by Cosmic-Ray Neutrons
                      (357874777)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(GEPRIS)357874777},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001173661400001},
      doi          = {10.3389/fsoil.2024.1346028},
      url          = {https://juser.fz-juelich.de/record/1021940},
}