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@ARTICLE{Jakobi:875354,
      author       = {Jakobi, Jannis and Huisman, Johan A. and Schrön, Martin
                      and Fiedler, Justus and Brogi, Cosimo and Vereecken, Harry
                      and Bogena, Heye R.},
      title        = {{E}rror {E}stimation for {S}oil {M}oisture {M}easurements
                      {W}ith {C}osmic {R}ay {N}eutron {S}ensing and {I}mplications
                      for {R}over {S}urveys},
      journal      = {Frontiers in water},
      volume       = {2},
      issn         = {2624-9375},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2020-01972},
      pages        = {10},
      year         = {2020},
      abstract     = {Cosmic ray neutron (CRN) sensing allows for non-invasive
                      soil moisture measurements at the field scale and relies on
                      the inverse correlation between aboveground measured
                      epithermal neutron intensity (1eV – 100 keV) and
                      environmental water content. The measurement uncertainty
                      follows Poisson statistics and thus increases with
                      decreasing neutron intensity, which corresponds to
                      increasing soil moisture. In order to reduce measurement
                      uncertainty, the neutron count rate is usually aggregated
                      over 12 or 24 h time windows for stationary CRN probes. To
                      obtain accurate soil moisture estimates with mobile CRN
                      rover applications, the aggregation of neutron measurements
                      is also necessary and should consider soil wetness and
                      driving speed. To date, the optimization of spatial
                      aggregation of mobile CRN observations in order to balance
                      measurement accuracy and spatial resolution of soil moisture
                      patterns has not been investigated in detail. In this work,
                      we present and apply an easy-to-use method based on Gaussian
                      error propagation theory for uncertainty quantification of
                      soil moisture measurements obtained with CRN sensing. We
                      used a 3rd order Taylor expansion for estimating the soil
                      moisture uncertainty from uncertainty in neutron counts and
                      compared the results to a Monte Carlo approach with
                      excellent agreement. Furthermore, we applied our method with
                      selected aggregation times to investigate how CRN rover
                      survey design affects soil moisture estimation uncertainty.
                      We anticipate that the new approach can be used to improve
                      the strategic planning and evaluation of CRN rover surveys
                      based on uncertainty requirements.},
      cin          = {IBG-3},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / DFG project 357874777 - FOR 2694: Large-Scale
                      and High-Resolution Mapping of Soil Moisture on Field and
                      Catchment Scales - Boosted by Cosmic-Ray Neutrons},
      pid          = {G:(DE-HGF)POF3-255 / G:(GEPRIS)357874777},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000660303300001},
      doi          = {10.3389/frwa.2020.00010},
      url          = {https://juser.fz-juelich.de/record/875354},
}