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@ARTICLE{Rodionov:186445,
      author       = {Rodionov, Andrei and Pätzold, Stefan and Welp, Gerhard and
                      Pallares, Ramon Cañada and Damerow, Lutz and Amelung, Wulf},
      title        = {{S}ensing of {S}oil {O}rganic {C}arbon {U}sing {V}isible
                      and {N}ear-{I}nfrared {S}pectroscopy at {V}ariable
                      {M}oisture and {S}urface {R}oughness},
      journal      = {Soil Science Society of America journal},
      volume       = {78},
      number       = {3},
      issn         = {0361-5995},
      address      = {Madison, Wis.},
      publisher    = {SSSA},
      reportid     = {FZJ-2015-00520},
      pages        = {949 - 957},
      year         = {2014},
      abstract     = {Variations in soil moisture and surface roughness are major
                      obstacles for the proximal sensing of soil organic C (SOC)
                      using visible and near-infrared spectroscopy (VIS-NIRS). We
                      gained a significant improvement of SOC prediction under
                      field conditions with a stepwise approach. This comprised of
                      (i) the estimation of these disturbing factors and (ii) the
                      subsequent use of this information in multivariate SOC
                      prediction. We took 120 surface soil samples (SOC contents
                      6.55–13.40 g kg−1) from a long-term trial near Bonn,
                      Germany. To assess soil moisture, we recorded VIS-NIR
                      spectra on <2-mm sieved disturbed samples at seven different
                      moisture levels (air-dried to $30\%$ w/w). The impact of
                      roughness on VIS-NIRS performance was studied with
                      undisturbed samples (air-dried and at different moisture
                      levels), which were scanned with a laser profiler after
                      fractionation into six aggregate size classes. The results
                      confirmed that it was possible to include VIS-NIRS based
                      assessments of soil moisture [R2adj = 0.96; root mean square
                      error of cross validation (RMSEcv) = $1.99\%$ w/w] into the
                      prediction of SOC contents for sieved samples <2 mm (R2adj =
                      0.81–0.94; RMSEp = 0.41–0.72 g SOC kg−1). However, for
                      rough soil surfaces, SOC contents were overestimated, and
                      the prediction of roughness indices using VIS-NIRS failed.
                      Fortunately, surface roughness did not impair the VIS-NIRS
                      assessment of soil moisture. Hence, we could directly
                      estimate moisture via VIS-NIRS in undisturbed field samples
                      and then incorporate this information into a
                      moisture-dependent prediction of SOC contents. This provided
                      accurate SOC estimates for field-moist, undisturbed samples
                      (R2adj = 0.91). Deviations from the reference method
                      (elemental analysis) were below 2 g SOC kg−1.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {246 - Modelling and Monitoring Terrestrial Systems: Methods
                      and Technologies (POF2-246) / 255 - Terrestrial Systems:
                      From Observation to Prediction (POF3-255)},
      pid          = {G:(DE-HGF)POF2-246 / G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000341564100027},
      doi          = {10.2136/sssaj2013.07.0264},
      url          = {https://juser.fz-juelich.de/record/186445},
}