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@INPROCEEDINGS{Lrm:916953,
      author       = {Lärm, Lena and Bauer, Felix and van der Kruk, Jan and
                      Vanderborght, Jan and Vereecken, Harry and Schnepf, Andrea
                      and Klotzsche, Anja},
      title        = {{E}stimating the effect of maize crops on time-lapse
                      horizontal crosshole {GPR} data},
      reportid     = {FZJ-2023-00219},
      year         = {2022},
      abstract     = {Investigating soil, roots and their interaction is
                      important to optimize agricultural practices like irrigation
                      and fertilization and therefore increase the sustainability
                      and productivity of crop production. In this study, we are
                      combining two methods to examine non-invasively,
                      characterize and monitor the soil-root zone throughout crop
                      growing seasons: crosshole ground penetrating radar (GPR)
                      and root-images within horizontal mini-rhizotrons. Over
                      three maize crop growing seasons, we acquired in-situ
                      time-lapse crosshole ground penetrating radar data and
                      time-lapse root images, at two mini-rhizotron facilities in
                      Selhausen, Germany. These facilities allow to horizontally
                      measure data at six different depths, ranging between 0.1 m
                      - 1.2 m and below three different plots with varying
                      agricultural treatments, such as irrigation, sowing density,
                      sowing date and cultivars. The GPR measurements result in
                      the dielectric permittivity slices by applying standard
                      ray-based analysis to zero-offset measurements along a pair
                      of rhizotubes. Such horizontal permittivity slices can be
                      linked to soil water content using petro physical
                      relationships. Additionally, the root images provide a root
                      fraction per image, which is derived by using a workflow
                      combining state-of-the-art software tools, deep neural
                      networks and automated feature extraction. The dielectric
                      permittivity slices suggest a permittivity variation along
                      the horizontal and vertical axes, depending on atmospheric
                      conditions, soil properties, and root architecture. To
                      quantify the influence of the roots on the spatial and
                      temporal distribution of dielectric permittivity, we used
                      statistical methods to reduce the impacting factors like
                      soil heterogeneity, tube deviations and changing atmospheric
                      conditions, which results in the spatial and temporal
                      variability. For verification these permittivity
                      variabilities are compared to the root fraction values. In
                      general, using the spatial and temporal permittivity
                      variations, we can detect the presence of roots and
                      additionally recognize a varying influence of the roots over
                      the duration of the crop growing season. Using these first
                      results, we demonstrate that GPR can be applied to improve
                      the characterization of the root-soil system related to
                      maize plants. This could be the first step towards
                      developing proxies e.g. for irrigation and fertilization
                      applications using this non-invasive method.},
      month         = {May},
      date          = {2022-05-23},
      organization  = {EGU General Assembly 2022, Vienna
                       (Austria), 23 May 2022 - 27 May 2022},
      subtyp        = {After Call},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / EXC 2070:  PhenoRob - Robotics and Phenotyping
                      for Sustainable Crop Production (390732324)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(BMBF)390732324},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/916953},
}