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@ARTICLE{Montzka:61749,
      author       = {Montzka, C. and Canty, M. J. and Kreins, P. and Kunkel, R.
                      and Menz, G. and Vereecken, H. and Wendland, F.},
      title        = {{M}ultispectral remotely sensed data in modelling the
                      annual variability of nitrate concentrations in the
                      leachate},
      journal      = {Environmental modelling $\&$ software},
      volume       = {23},
      issn         = {1364-8152},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {PreJuSER-61749},
      pages        = {1070 - 1081},
      year         = {2008},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {The advantages of using multispectral remotely sensed data
                      instead of COPINE Land Cover for the modelling of nitrate
                      concentrations in the leachate of the Rur catchment are
                      presented and discussed in this paper. In this context it
                      has been shown that the identification of main crops and
                      annual crop rotation in the Rur catchment by SPOT, LANDSAT
                      and ASTER imagery provides the key for a spatial and
                      thematic enhancement of the model results. The spatial
                      resolution of the nitrogen surplus data set which denotes
                      the linkage between RAUMIS and GROWA is enhanced from
                      district level to field/pixel level. In parallel, the
                      empirical water balance model GROWA is enhanced to
                      differentiate between agricultural crops in the real
                      evapotranspiration calculation. It is calibrated by runoff
                      data measured at gauging stations. Results indicate, e.g.,
                      an average nitrate concentration in the leachate of 42 mg
                      NO3/L in the relatively wet year of 2002 and almost 62 mg
                      NO3/L in the dry year of 2003. There is a 20 mg NO3/L
                      weather-induced difference which can be modelled in a more
                      detailed way using self-processed remotely sensed data. The
                      model results were compared to nitrate concentrations
                      observed in the top parts of multi-level wells. In this way
                      the related coefficient of determination has been improved
                      from a value (R) of -0.50 using CORINE to 0.59 by using
                      self-processed remotely sensed data, thus demonstrating the
                      potential of the enhanced model system. (c) 2007 Elsevier
                      Ltd. All rights reserved.},
      keywords     = {J (WoSType)},
      cin          = {ICG-4 / JARA-ENERGY / JARA-SIM},
      ddc          = {690},
      cid          = {I:(DE-Juel1)VDB793 / $I:(DE-82)080011_20140620$ /
                      I:(DE-Juel1)VDB1045},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Computer Science, Interdisciplinary Applications /
                      Engineering, Environmental / Environmental Sciences},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000255770300010},
      doi          = {10.1016/j.envsoft.2007.11.010},
      url          = {https://juser.fz-juelich.de/record/61749},
}