The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
Far-red line: D= miA+C] (3) 
N1R line'. D= m 2 .X,+c 2 (4) 
where m and c represent the slope and intercept of the straight 
lines, respectively. At the intersection, the two lines have equal 
wavelengths and D values. Therefore, the REIP, which is the 
wavelength at the intersection, is given by: 
R1EP 
LEM 
- (c, - c 2 ) 
Oi - m 2 ) 
(5) 
Partial least squares regression (PLSR) is a technique that 
reduces the large number of measured collinear spectral 
variables to a few non-correlated latent variables or factors 
while maximizing co-variability to the variable(s) of interest 
(Atzberger et al., 2003; Cho et al., 2007; Geladi and Kowalski, 
1986; Hansen and Schjoerring, 2003). The latent variables 
represent the relevant information present in the measured 
reflectance spectra and are used to predict the dependent 
variables (here, biophysical and biochemical grass 
characteristics). As with other linear calibration methods, the 
aim is to build a linear model: 
content. The best performing indices and the band positions are 
tabulated in Table 2. 
It can be observed from Table 2 that narrow band SAVI2 had 
somewhat higher correlations than narrow band NDVI with the 
studied variables. However, the coefficients of determination 
between the grass characteristics and the indices were relatively 
low. Studying regions where R 2 >0.6 for LAI and canopy 
chlorophyll content (CCC) revealed that LAI had a strong 
influence on the selection of suitable bands for estimating 
canopy chlorophyll content. The similarity in the observed 
patterns is obviously due to the high correlation between the 
two variables (not shown). 
600 800 1000 1200 1400 1600 1800 2000 2200 2400 
Wavelength nm 
Figure 1. 2-D correlation plots illustrating the coefficient of 
determination (R 2 ) between narrow band SAVI2 and LAI. 
Y=X|3+e (6) 
where Y is the mean-centred vector of the response variable 
(grass characteristics), X is the mean-centred matrix of the 
predictor (spectral reflectance), P is the matrix of coefficients, 
and e is the matrix of residuals. 
The optimum number of factors was estimated by leave-one-out 
cross-validation. A common way of using cross-validation for 
this estimation is to select the number of factors that minimizes 
the RMSE (Geladi and Kowalski, 1986). To prevent collinearity 
and to preserve model parsimony, the condition for adding an 
extra factor to the model was that it had to reduce the root mean 
square error of cross-validation (RMSE C v) by >2% (Cho et al., 
2007; Kooistra et al., 2004). In addition, coefficients of 
determination (R 2 ) between measured and predicted values in 
the cross-validation were used to evaluate the relationships 
found. The PLSR analysis was performed using the TOMCAT 
toolbox 1.01 within MATLAB (Daszykowski et al., 2007). 
3. RESULTS 
Variables 
Narrow 
band VI 
X,[nm] 
R 2 
LAI 
NDVI 
1105/1229 
0.61 
SAVI2 
1998/1402 
0.64 
CCC 
NDVI 
1141/1150 
0.68 
SAVI2 
1211/1086 
0.69 
Table 2. Band positions and R 2 values between the best narrow 
band NDVI and SAVI2 (derived from 2-D correlation plots of 
different data sets) and grass variables. 
For the best performing narrow band index, cross-validated R 2 
and relative RMSE (RRMSE = RMSE/mean) were computed 
from linear regression models (Table 3). As can be observed 
from this table, compared with narrow band NDVI, narrow 
band SAVI2 gave slightly higher R 2 and lower RMSE values 
for LAI and canopy chlorophyll content. The better 
performance of SAVI2 compared with NDVI is probably due to 
the fact that SAVI2 is less sensitive to external factors such as 
soil background effects. 
3.1 Hyperspectral vegetation indices 
NDVI and SAVI2 narrow band vegetation indices were 
calculated from the measured canopy reflectance spectra, using 
all possible two-band combinations. The coefficients of 
determination (R 2 ) between these narrow band vegetation 
indices and the grass canopy characteristics were computed. An 
illustration of these results is shown for LAI in the 2-D 
correlation plot in Figure 1. The meeting point of each pair of 
wavelengths in a 2-D plot corresponds to the R 2 value of LAI 
and the vegetation index calculated from the reflectance values 
in those two wavelengths. Based on the R 2 values in the 2-D 
correlation plots, band combinations that formed the best 
indices were determined for LAI and canopy chlorophyll 
Variables 
Narrow 
band VI 
R 2 cv 
RRMSE CV 
LAI 
NDVI 
0.60 
0.34 
SAVI2 
0.63 
0.33 
CCC 
NDVI 
0.67 
0.36 
SAVI2 
0.68 
0.35 
Table 3. Performance of narrow band vegetation indices for 
predicting grass variables in Majella National Park, Italy.