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024 7 _ |2 DOI
|a 10.1016/j.envsoft.2007.11.010
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|a WOS:000255770300010
037 _ _ |a PreJuSER-61749
041 _ _ |a eng
082 _ _ |a 690
084 _ _ |2 WoS
|a Computer Science, Interdisciplinary Applications
084 _ _ |2 WoS
|a Engineering, Environmental
084 _ _ |2 WoS
|a Environmental Sciences
100 1 _ |a Montzka, C.
|b 0
|u FZJ
|0 P:(DE-Juel1)VDB51558
245 _ _ |a Multispectral remotely sensed data in modelling the annual variability of nitrate concentrations in the leachate
260 _ _ |a Amsterdam [u.a.]
|b Elsevier Science
|c 2008
300 _ _ |a 1070 - 1081
336 7 _ |a Journal Article
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336 7 _ |a article
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440 _ 0 |a Environmental Modelling and Software
|x 1364-8152
|0 13262
|y 8
|v 23
500 _ _ |a Record converted from VDB: 12.11.2012
520 _ _ |a 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.
536 _ _ |a Terrestrische Umwelt
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588 _ _ |a Dataset connected to Web of Science
650 _ 7 |a J
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653 2 0 |2 Author
|a remote sensing
653 2 0 |2 Author
|a disaggregation
653 2 0 |2 Author
|a nitrate concentration
653 2 0 |2 Author
|a crop rotation
653 2 0 |2 Author
|a model coupling
653 2 0 |2 Author
|a diffuse pollution
700 1 _ |a Canty, M. J.
|b 1
|u FZJ
|0 P:(DE-Juel1)VDB4989
700 1 _ |a Kreins, P.
|b 2
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700 1 _ |a Kunkel, R.
|b 3
|u FZJ
|0 P:(DE-Juel1)VDB4996
700 1 _ |a Menz, G.
|b 4
|0 P:(DE-HGF)0
700 1 _ |a Vereecken, H.
|b 5
|u FZJ
|0 P:(DE-Juel1)129549
700 1 _ |a Wendland, F.
|b 6
|u FZJ
|0 P:(DE-Juel1)VDB4997
773 _ _ |a 10.1016/j.envsoft.2007.11.010
|g Vol. 23, p. 1070 - 1081
|p 1070 - 1081
|q 23<1070 - 1081
|0 PERI:(DE-600)2027304-6
|t Environmental modelling & software
|v 23
|y 2008
|x 1364-8152
856 7 _ |u http://dx.doi.org/10.1016/j.envsoft.2007.11.010
909 C O |o oai:juser.fz-juelich.de:61749
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914 1 _ |y 2008
915 _ _ |0 StatID:(DE-HGF)0010
|a JCR/ISI refereed
920 1 _ |d 31.10.2010
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