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000061749 0247_ $$2DOI$$a10.1016/j.envsoft.2007.11.010
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000061749 084__ $$2WoS$$aComputer Science, Interdisciplinary Applications
000061749 084__ $$2WoS$$aEngineering, Environmental
000061749 084__ $$2WoS$$aEnvironmental Sciences
000061749 1001_ $$0P:(DE-Juel1)VDB51558$$aMontzka, C.$$b0$$uFZJ
000061749 245__ $$aMultispectral remotely sensed data in modelling the annual variability of nitrate concentrations in the leachate
000061749 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2008
000061749 300__ $$a1070 - 1081
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000061749 440_0 $$013262$$aEnvironmental Modelling and Software$$v23$$x1364-8152$$y8
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000061749 520__ $$aThe advantages of using multispectral remotely sensed data instead of COPINE Land Cover for the modelling of nitrate concentrations in the leachate of the Rur catchment are presented and discussed in this paper. In this context it has been shown that the identification of main crops and annual crop rotation in the Rur catchment by SPOT, LANDSAT and ASTER imagery provides the key for a spatial and thematic enhancement of the model results. The spatial resolution of the nitrogen surplus data set which denotes the linkage between RAUMIS and GROWA is enhanced from district level to field/pixel level. In parallel, the empirical water balance model GROWA is enhanced to differentiate between agricultural crops in the real evapotranspiration calculation. It is calibrated by runoff data measured at gauging stations. Results indicate, e.g., an average nitrate concentration in the leachate of 42 mg NO3/L in the relatively wet year of 2002 and almost 62 mg NO3/L in the dry year of 2003. There is a 20 mg NO3/L weather-induced difference which can be modelled in a more detailed way using self-processed remotely sensed data. The model results were compared to nitrate concentrations observed in the top parts of multi-level wells. In this way the related coefficient of determination has been improved from a value (R) of -0.50 using CORINE to 0.59 by using self-processed remotely sensed data, thus demonstrating the potential of the enhanced model system. (c) 2007 Elsevier Ltd. All rights reserved.
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000061749 65320 $$2Author$$aremote sensing
000061749 65320 $$2Author$$adisaggregation
000061749 65320 $$2Author$$anitrate concentration
000061749 65320 $$2Author$$acrop rotation
000061749 65320 $$2Author$$amodel coupling
000061749 65320 $$2Author$$adiffuse pollution
000061749 7001_ $$0P:(DE-Juel1)VDB4989$$aCanty, M. J.$$b1$$uFZJ
000061749 7001_ $$0P:(DE-HGF)0$$aKreins, P.$$b2
000061749 7001_ $$0P:(DE-Juel1)VDB4996$$aKunkel, R.$$b3$$uFZJ
000061749 7001_ $$0P:(DE-HGF)0$$aMenz, G.$$b4
000061749 7001_ $$0P:(DE-Juel1)129549$$aVereecken, H.$$b5$$uFZJ
000061749 7001_ $$0P:(DE-Juel1)VDB4997$$aWendland, F.$$b6$$uFZJ
000061749 773__ $$0PERI:(DE-600)2027304-6$$a10.1016/j.envsoft.2007.11.010$$gVol. 23, p. 1070 - 1081$$p1070 - 1081$$q23<1070 - 1081$$tEnvironmental modelling & software$$v23$$x1364-8152$$y2008
000061749 8567_ $$uhttp://dx.doi.org/10.1016/j.envsoft.2007.11.010
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