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@INPROCEEDINGS{Jakobi:875415,
      author       = {Jakobi, Jannis and Huisman, Johan Alexander and Schrön,
                      Martin and Fiedler, Justus and Brogi, Cosimo and Vereecken,
                      Harry and Bogena, Heye},
      title        = {{E}rror estimation for soil moisture measurements with
                      cosmic-ray neutron sensing and implications for rover
                      surveys},
      reportid     = {FZJ-2020-02021},
      year         = {2020},
      abstract     = {<p>The cosmic ray neutron (CRN) probe is a non-invasive
                      device to measure soil moisture at the field scale. This
                      instrument relies on the inverse correlation between
                      aboveground epithermal neutron intensity (1eV $\&#8211;$ 100
                      keV) and environmental water content. The measurement
                      uncertainty of the neutron detector follows Poisson
                      statistics and thus decreases with decreasing neutron
                      intensity, which corresponds to increasing soil moisture. In
                      order to reduce measurement uncertainty (e.g. < 0.03
                      m<sup>3</sup>/m<sup>3</sup>), the neutron count rate is
                      often aggregated over large time windows (e.g. 12h or 24h).
                      To enable shorter aggregation intervals, the measurement
                      uncertainty can be reduced either by using more efficient
                      detectors or by using arrays of detectors, as in the case of
                      CRN rover applications. Depending on soil moisture and
                      driving speed, aggregation of neutron counts may also be
                      necessary to obtain sufficiently accurate soil moisture
                      estimates in rover applications. To date, signal aggregation
                      has not been investigated sufficiently with respect to the
                      optimisation of temporal (stationary probes) and spatial
                      (roving applications) resolution. In this work, we present
                      an easy-to-use method for uncertainty quantification of soil
                      moisture observations from CRN sensors based on Gaussian
                      error propagation theory. We have estimated the uncertainty
                      using a third order Taylor expansion and compared the result
                      with a more computationally intensive Monte Carlo approach
                      and found excellent agreement. Furthermore, we used our
                      method to quantify the dependence of soil moisture
                      uncertainty on CRN rover survey design and on selected
                      aggregation time. We anticipate that the new approach helps
                      to quantify cosmic ray neutron measurement uncertainty. In
                      particular, it is anticipated that the strategic planning
                      and evaluation of CRN rover surveys based on uncertainty
                      requirements can be improved considerably.</p>},
      month         = {May},
      date          = {2020-05-04},
      organization  = {EGU General Assembly 2020, Wien
                       (Austria), 4 May 2020 - 8 May 2020},
      subtyp        = {After Call},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / TERENO - Terrestrial Environmental
                      Observatories (TERENO-2008)},
      pid          = {G:(DE-HGF)POF3-255 / G:(DE-HGF)TERENO-2008},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.5194/egusphere-egu2020-8488},
      url          = {https://juser.fz-juelich.de/record/875415},
}