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4. DISCUSSION 
The agronomic parameters and the spectral reflectance are 
influenced by the farmer's management during the growth 
stages. It has to be considered, that, this study was made under 
normal conditions on fields managed by usual famers, so that 
fields, cultivars, plant date, N-management, harvest date and 
irrigation have not been modified for the study. Schellberg 
(1990) and Biicker (1992) analyse fields in specially adapted 
case studies with similar results. Oppelt and Mauser (2004) 
show, that the models are influenced by winter wheat cultivar 
and growth stage. Here the OSAVI correlates for many winter 
wheat cultivars. LAI measurements were not taken frequently 
and only in 2006. Therefore it is not possible to evaluate the 
data and take it into consideration here as well. 
Additionally, the spectral and agronomic data can be stored in a 
Web-based spectral database. That ensures easy management 
of a very voluminous data (Laudien, 2006). 
The research in Huimin County in 2006 and 2007 shows, that 
some promising models can be developed for hyperspectral Vis. 
The experimental data of these two years result in more 
similarities as differences in their results. By means of the 
spectral and agronomic library the influence of N-fertilisation 
and cultivars can be analysed for every feekes GS and across all 
stages in a time series, assuming that data has been collected for 
that stage. 
5. CONCLUSION 
The collected and post processed spectral and agronomic data 
of winter wheat in combination with GIS and RS analysis help 
to identify over-fertilised and undersupplied managements for 
different phenological stages from shooting to heading. Some 
Vis like OSAVI, HNDVI and MCARI2 show significant 
correlation between biomass and N-uptake. In the early 
development stage (shooting), the different N-applications for 
the treatments could be detected in the spectra as well as in 
agronomic parameters such as chlorophyll content and biomass. 
Consequently, the vitality of the crop can be detected on a local 
scale. The extrapolation of the derived experimental plots on a 
regional scale is realised by analysing Hyperion imagery in a 
comparable manner (Koppe et al., 2008). Here, it can be stated 
that some of the Vis like HNDVI, which performs well on plot 
scale, cannot be used for Hyperion imagery. Others, such as 
MCARI, come up with reliable results, so that the chosen VI 
has to be rated very carefully while being adapted to the 
analysis. 
The method of knowledge extrapolation, as presented in this 
contribution, offers the possibility to facilitate the development 
of a decision support tool for winter wheat production and to 
secure an adequate nutrition management in such densely 
populated areas as the NCR These steps of precision agriculture, 
as described by Rdsch et al. (2007) are very important for a 
sustainable agricultural production. 
REFERENCES 
Boning-Zilkens, M.I., 2004. Comparative appraisal of different 
agronomic strategies in a winter wheat -summer maize double 
cropping system in the North China Plain with regard to their 
contribution to sustainability. PhD. Thesis - Hohenheim. 
Shaker Verlag, Aachen. 
Büker, C., 1992. Fernerkundung von Winterweizen - 
Zustandsbeschreibung und Ertragsschätzung durch bodennahe 
Messungen. PhD. Thesis - Bonn. 
Chen, X., Zhang, F., Römheld, V., Horlacher, D., Schulz, R., 
Böning-Zilkens, M., Wang, P., Claupein, W., 2006. 
Synchronizing N Supply from Soil and Fertilizer and N 
Demand of Winter Wheat by an improved N min Method. - In. 
Nutrient Cycling in Agroecosystems, 74, pp. 91-98. 
Daughtry, C.S.T., Walthall, C.L., Kim, M.S., Brown de 
Colstoun, E., McMurtrey, J.E., 2000. Estimating Com Leaf 
Chlorophyll Concentration from Leaf and Canopy Reflectance. 
- In. Remote Sensing of Environment, 74, pp. 229-239. 
FAO, 2004. FAO Statistical Yearbook 2004. 
http://www.fao.org/statistics/yearbook/vol_l_l/ 
(accessed 9 Nov. 2006) 
Flowers, M., Weisz, R., Heiniger, R., 2001. Remote Sensing of 
Winter Wheat Tiller Density for early Nitrogen Application 
Decisions. -In. Agron. J., 93, pp. 783-789. 
Gitelson, A., Merzyak, M.N., Lichtenthaler, K., 1996. 
Detection of Red Egde Position and Chlorophyll Content by 
Reflectance. - In. Plant Physiol., 148, pp. 501-508. 
Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., 
Strachan, I. B., 2004. Hyperspectral Vegetation Indices and 
Novel Algorithms for Predicting Green LAI of CropCanopies. 
Modeling and Validation in the Context of Precision 
Agriculture. - In. Remote Sensing of Environment, 90, pp. 
337-352. 
Jia, L., Biirkert, A., Chen, X., Roemheld,V., Zhang, F., 2004. 
Low-altitude aerial Photography for Optimum N Fertilization of 
Winter Weat on the North China Plain. - In. Field Crops 
Research, 89, pp. 389-395. 
Jia, L., Yu, Z., Li, F., Gnyp, M.L., Koppe, W., Bareth, G., Miao, 
Y., Chen, X., Zhang, F., 2008. Nitrogen status estimation of 
winter wheat by using a IKONOS satellite image in the North 
China Plain, (in review) 
Koppe, K., Li, F., Gnyp, M.L., Yu, Z., Laudien, R., Miao, Y., 
Jia, L., Chen, X., Bareth, G., 2008. Deriving Winter Wheat 
Characteristics from Multitemporal EO-1 Hyperion and ALI 
Data on a regional level in the North China Plain (in review) 
Large, E.C., 1954. Growth stage in cereals. Plant Pathol. 3, pp. 
128-129. 
Laudien, R., 2005. Entwicklung eines GIS-gestützten 
schlagbezogenen Führungsinformationssystems für die 
Zuckerwirtschaft. PhD. Thesis - Hohenheim. 
Laudien, R., Barky, K., Doloschütz, R, Barth, G., 2006. 
Establishment of a Web-based spectral database for the 
analysis of hyperspectral data from Rhizoctania salami- 
inoculated sugarbeets. - In. Sugar Industry, 131(6), pp. 164- 
170. 
Li, X.X., Hu, C.S., Delgado, J.A., Zhang, Y.M., Ouyang, Z.Y., 
2007. Increased nitrogen use efficiencies as a key mitigation 
alternative to reduce nitrate leaching in north china plain. Agr. 
Water Manage, 8(9), pp. 137-147