The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
1995), in our study wavelengths within the red edge region 
were almost absent. 
The PLSR model appears to be a powerful alternative to 
univariate statistical methods (Darvishzadeh et al., 2008). 
Compared to the other investigated methods, it achieved 
relatively better results. It seems that important information will 
be lost by selecting only two wavelengths for narrow band 
vegetation indices. 
LAI 
CCC 
Figure 3. Cross-validated prediction of grass variables in 
Majella National Park, Italy, using the entire reflectance spectra 
in partial least squares regression models. Left: estimated LAI 
versus measured LAI; right: for canopy chlorophyll content. 
Estimation of biochemical and biophysical characteristics of 
heterogonous grassland with mixtures of different grass species 
is challenging in remote sensing (Roder et al., 2007), as the 
measured signal correspond to different grass species. In our 
study, an indicator of this was the observed high variations in 
the SPAD readings within a given subplot (not shown). 
Nevertheless, by using hyperspectral remote sensing with a 
large number of narrow spectral bands and powerful 
multivariate regression techniques, the biophysical grass 
characteristics could be retrieved with acceptable accuracy. 
5. CONCLUSION 
The most important conclusions that can be drawn from this 
study are as follows: 
- Compared with LAI, canopy chlorophyll content was 
estimated with higher accuracy in all models. 
- LAI was best estimated by partial least square regression 
which utilize more than two wavelengths from the entire 
spectral region (400 nm to 2500 nm) to estimate the 
variable of interest. 
- SAVI2 is a potentially useful vegetation index for 
extracting canopy variables such as LAI. However, the 
selection of appropriate wavelengths and bandwidths is 
important. 
Partial least squares regression provided the most useful 
explorative tool for unraveling the relationship between canopy 
spectral reflectance and grass characteristics at canopy scale. 
In summary, multivariate calibration methods, which until now 
have only been used in a few cases concerning the remote 
sensing of grasslands, can enhance estimates of different grass 
variables, and thus present new prospects for mapping and 
monitoring heterogeneous grass canopies from air- and space- 
borne platforms. 
ACKNOWLEDGEMENTS 
We would like to acknowledge the assistance of the park 
management of Majella National Park, Italy, and in particular 
of Dr. Teodoro Andrisano. We extend our gratitude to Dr. Istiak 
Sobhan for his assistance during the field campaign. Special 
thanks go to Dr. Michal Daszykowski for his assistance in 
applying the TOMCAT toolbox and for his valuable comments. 
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