## 4 Summary and Conclusions  
Time series of hyper-spectral reflectance data (400-2,500 nm) for two crop species--sugar beet and spring barley--over a vegetation period clearly demonstrated a significant response of plant reflectance characteristics to variations in WC and nutrient conditions. Based on these findings, we investigated the potential of retrieving plant LAI and soil WC from spectral reflectance data. First, the full range of spectral bands (\(N=2151\)), as well as a reduced spectral data-base consisting of 28 spectral indices extracted from literature review (see Table 1; \(N=28\)), have been analyzed for their information in a system analysis sense. Using linear and non-linear techniques, the intrinsic dimensionality of both datasets suggested a number of 3-4 but not more than 8 bands/indices to fully represent the information contained in the spectral data. Results also suggest, that the full band information is well represented by the set of 28 SI's that have been divided into three functional groups: (i) greenness/biomass/vegetation structure, (ii) pigment or light use efficiency, and (iii) water related indices (see Table 1).  
Restricting the further analysis to the use of the set of SI's, we subsequently analyzed the potential for LAI and WC retrieval from the SI's using single and multiple linear regression. It was shown that including three indices in the retrieval algorithm--one index from each functional group--could significantly improve the results. In general, the retrieval of LAI and WC was significantly higher when only considering single species. R2 values range from 0.65 for WC retrieval for both sugar beet and spring barley when treated separately, to 0.67 and 0.33 for LAI retrieval. These results are very much in line with a summary statistics of LAI retrieval results reviewed by [19] given values in the range of 0.05-0.66, however we have to consider that here we have been working under an almost ideal glasshouse situation with cultivated plants under optimal and controlled irrigation, fertilization and illumination conditions and were also able to achieve constant light source sensor configuration thereby avoiding bidirectional effects.  
While the results achieved are promising, we still need to extend our analysis considering the following aspects: While the best retrieval results have been achieved considering only single crop species, this would require the application of classification schemes beforehand when applying glasshouse calibrated algorithms to \"real world\" field conditions. This is currently under investigation using field spectral measurements at selected experimental test plot of the Bad Lauchstadt long-term fertilization experiment and will be presented in near future.  
The potential of non-linear retrieval algorithms needs to be extended to a more thorough analysis considering a larger variety of methods in addition to the CART approach presented here. However, first results presented here within a cross-validation framework suggest that most of the non-linearity in the LAI/WC to spectral reflectance relationship is already covered by the non-linearity of the SI'sapplied, but needs to be further tested. Our aim in publishing the here presented data set to the remote sensing community will hopefully support this requirement.  
While our analysis has mainly built on the use of SI's, at least for the retrieval of LAI, the inversion of a leaf/canopy reflectance model such as PROSPECT/SAIL [36, 46, 59] might have the potential to improve the results, but again while being beyond the scope of this paper, this aspect needs to be investigated in more detail and is part of our current research activities that will certainly be presented in the near future.