The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
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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.
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