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@ARTICLE{Wang:864349,
      author       = {Wang, Hui and Wellmann, Florian and Zhang, Tianqi and
                      Schaaf, Alexander and Kanig, Robin Maximilian and Verweij,
                      Elizabeth and Hebel, Christian and Kruk, Jan},
      title        = {{P}attern extraction of top‐ and subsoil heterogeneity
                      and soil‐crop interaction using unsupervised {B}ayesian
                      machine learning: an application to satellite‐derived
                      {NDVI} time series and electromagnetic induction
                      measurements},
      journal      = {Journal of geophysical research / Biogeosciences
                      Biogeosciences [...]},
      volume       = {124},
      number       = {6},
      issn         = {2169-8961},
      address      = {[Washington, DC]},
      reportid     = {FZJ-2019-04145},
      pages        = {1524-1544},
      year         = {2019},
      abstract     = {The link between remotely sensed surface vegetation
                      performances with the heterogeneity of subsurface physical
                      properties is investigated by means of a Bayesian
                      unsupervised learning approach. This question has
                      considerable relevance and practical implications for
                      precision agriculture as visible spatial differences in crop
                      development and yield are often directly related to
                      horizontal and vertical variations in soil texture caused
                      by, for example, complex deposition/erosion processes. In
                      addition, active and relict geomorphological settings, such
                      as floodplains and buried paleochannels, can cast
                      significant complexity into surface hydrology and crop
                      modeling. This also requires a better approach to detect,
                      quantify, and analyze topsoil and subsoil heterogeneity and
                      soil‐crop interaction. In this work, we introduce a novel
                      unsupervised Bayesian pattern recognition framework to
                      address the extraction of these complex patterns. The
                      proposed approach is first validated using two synthetic
                      data sets and then applied to real‐world data sets of
                      three test fields, which consists of satellite‐derived
                      normalized difference vegetation index (NDVI) time series
                      and proximal soil measurement data acquired by a
                      multireceiver electromagnetic induction geophysical system.
                      We show, for the first time, how the similarity and joint
                      spatial patterns between crop NDVI time series and soil
                      electromagnetic induction information can be extracted in a
                      statistically rigorous means, and the associated
                      heterogeneity and correlation can be analyzed in a
                      quantitative manner. Some preliminary results from this
                      study improve our understanding the link of above surface
                      crop performance with the heterogeneous subsurface.
                      Additional investigations have been planned for further
                      testing the validity and generalization of these findings.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000477719400009},
      doi          = {10.1029/2019JG005046},
      url          = {https://juser.fz-juelich.de/record/864349},
}