The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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Figure 2(a) 
Measured canopy chlorophyll content (g m' 2 ) 
Figure 2(b) 
Figure 2(ab). Cross-validated prediction of grass variables in 
Majella National Park, Italy, using narrow band NDVI. Left: 
estimated LAI versus measured LAI; rights: canopy chlorophyll 
content. The optimum wavebands are those reported in Table 2. 
Figure 2 shows the relationships between the estimated and 
measured LAI and canopy chlorophyll content using narrow 
band NDVI. From the figure, it seems that saturation starts to 
occur for canopy chlorophyll content greater than 2 (g nT 2 ) and 
for LAI greater than 7(m 2 m' 2 ). 
3.2 Red edge inflection point 
The red edge inflection point (REIP) was calculated using two 
methods. As can be observed from the results reported in Table 
4, the relationships between measured and estimated grass 
variables were not reliable using any of the methods. The R 2 
and relative RMSE of the grass variables obtained from the 
three methods were relatively similar. 
Among the studied variables, estimation of canopy chlorophyll 
content again yielded the highest R 2 values and the lowest 
relative RMSE. Compared with regression models developed 
using the optimum narrow band indices, the REIP methods 
produced somewhat lower accuracies. 
confirmed previous studies by researchers who suggested a 
strong contribution by SWIR bands to the strength of 
relationships between spectral reflectance and LAI (Cohen and 
Goward, 2004; Darvishzadeh et al., 2008; Lee et al., 2004; 
Nemani et al., 1993; Schlerf et al., 2005). Compared with the 
narrow band NDVI, the narrow band SAVI2 gave somewhat 
higher R2 and lower relative RMSE values for LAI. This result 
is in agreement with that of Broge and Leblanc (2001), who 
used simulated data and found SAVI2 to be the best vegetation 
REIP method 
Rev 
RRMSE CV 
Linear 
LAI 
0.49 
0.39 
interpolation 
CCC 
0.56 
0.41 
Linear 
LAI 
0.51 
0.38 
extrapolation 
CCC 
0.57 
0.41 
Table 4. Performance of red edge inflection point calculated 
using different methods for predicting grass variables in Majella 
National Park, Italy. 
3.3 Partial least squares regression 
The relationships between grass variables and reflectance 
spectra were modeled using PLSR. Cross-validated results 
using the entire reflectance spectra as inputs are shown in 
Figure 3. The optimal number of PLSR factors preventing over 
fitting was selected in two ways: (i) through visual inspection of 
cross-validated RMSE versus the number of factors plots (not 
shown), and (ii) by setting the condition that adding an extra 
factor must reduce the RMSE (RMSE CV ) by >2%. The number 
of factors in the final model were 4 for LAI and 5 for canopy 
chlorophyll content models. Compared with other methods, 
PLSR using entire reflectance spectra increased all R 2 values 
(R 2 = 0.69, 0.74 for LAI and canopy chlorophyll content, 
respectively) and decreased the Relative RMSE values 
(RRMSE = 0.32, 0.34 for LAI and canopy chlorophyll content, 
respectively). 
4. DISCUSSION 
The field experiment led to a large number of sample subplots 
(191) with high variations in LAI. The canopy integrated 
chlorophyll content (LAI x leaf chlorophyll content) strongly 
reflects the variability of LAI and (to a lesser extent) leaf 
chlorophyll content, expressed by the high inter-correlation 
between LAI and canopy chlorophyll content (not shown). 
Among the grass characteristics studied, canopy chlorophyll 
content was most accurately estimated by nearly all of the 
applied methods. The canopy chlorophyll content contains both 
the structure and chlorophyll information of vegetation and can 
be accurately estimated by canopy spectral reflectance. 
The relationship between measured and estimated LAI was 
better explained by multivariate calibration methods (PLSR) 
than by univariate methods such as narrow band vegetation 
indices and REIP. This is because a two-wavelength index 
utilizes only a limited amount of the total spectral information 
available in hyperspectral data (Lee et al., 2004). 
The bands selected as the best combination of the vegetation 
indices for LAI were found in the NIR to SWIR regions. This 
index for LAI estimation. Moreover, the narrow band SAVI2 
performed relatively well for canopy chlorophyll content. This 
is due to the major influence of LAI in canopy chlorophyll 
content and also to the fact that SAVI2 is relatively insensitive 
to external factors such as soil background effects. 
Although red edge has proved to respond more linearly to LAI 
and chlorophyll when compared with the classical NDVI, which 
often suffers from saturation problems (Danson and Plummer,