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@ARTICLE{Altdorff:134439,
      author       = {Altdorff, Daniel and Dietrich, Peter},
      title        = {{C}ombination of electromagnetic induction and gamma
                      spectrometry using {K}-means clustering: {A} study for
                      evaluation of site partitioning},
      journal      = {Journal of plant nutrition and soil science},
      volume       = {175},
      number       = {3},
      issn         = {1436-8730},
      address      = {Weinheim},
      publisher    = {Wiley-VCH},
      reportid     = {FZJ-2013-02638},
      pages        = {345 - 354},
      year         = {2012},
      abstract     = {Today rapid survey methods of proximal soil sensing (PSS)
                      provide an increasing number of different and highly
                      resolved data. These multidimensional data sets can lead to
                      multilayered and complex maps of parameters which are only
                      indirectly related to soil properties and soil functions.
                      However, in applications usually just one clear elementary
                      map is required. It is of increasing importance to tackle
                      this problem utilizing a cluster algorithm for the synthesis
                      and reduction of multidimensional input variables. The
                      cluster algorithm provides a partitioning of the
                      investigated site whereby the units are characterized by the
                      statistics of the PSS data. Therefore, the question that
                      arises is how suitable is the suggested partitioning in
                      terms of the delineation of different soil units. In this
                      study, we investigate the suitability of cluster
                      partitioning through a case study at a medium-scale test
                      site (≈ 50 000 m2). Two common PSS methods:
                      electromagnetic induction (EMI) and gamma spectrometry (GS)
                      will be employed to create a data set for partitioning by a
                      K-means cluster. The result of the cluster analysis is a
                      delineation of three different parts. In contrast to
                      previous studies, we evaluate the generated partitions by
                      independent soil properties such as grain size, horizon
                      thickness, and color of stratified randomly taken soil
                      samples. The soil analyses show that one of three clusters
                      significantly differs from the others in terms of grain-size
                      distribution and horizon thickness. The partitioning of the
                      other two clusters could not be confirmed by the considered
                      soil parameters. Nevertheless, the case study demonstrates
                      the combination of different PSS data by K-means clustering
                      as a potential approach for site partitioning. An evaluation
                      of the results of the cluster analysis through the
                      collection and analysis of soil samples is highly
                      recommended.},
      cin          = {IBG-3},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {246 - Modelling and Monitoring Terrestrial Systems: Methods
                      and Technologies (POF2-246)},
      pid          = {G:(DE-HGF)POF2-246},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000304598900003},
      doi          = {10.1002/jpln.201100262},
      url          = {https://juser.fz-juelich.de/record/134439},
}