In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
173 
Figure 1. Measured and simulated canopy reflectance spectra of 
two sample plots. 
Generally the simulated reflectances were in relatively good 
agreement with the measured reflectances for canopies with 
different LAI values. A more concise analysis reveals that most 
spectral bands were modelled with average absolute error 
(AAE) lower than 0.02 reflectance units. As Figure 2 shows the 
AAE in some regions is relatively high (greater than 0.02), 
especially close to the water vapour absorption regions (1135 
nm to 1400 nm, and 1820 nm to 1940 nm). We considered the 
bands with an AAE greater or equal to 0.02 as wavelengths 
being either poorly modelled or poorly measured (Darvishzadeh 
et al., 2008). 
Figure 2. The average absolute error between best-fit and the 
measured HyMap reflectance. 
The relation between the measured and estimated grass canopy 
chlorophyll content based on the smallest RMSE criterion is 
demonstrated in Figure 3. 
Measured canopy chlorophyll content (g m" 2 ) 
Figure 3. Estimated versus measured canopy chlorophyll using 
the PROSAIL model and the minimum RMSE criterion in the 
LUT search. 
We also evaluated the retrieval accuracy if multiple solutions 
are used. Table 3 compares the “multiple solutions” with the 
“best-fit” LUT solutions. This demonstrates how different 
solutions affect the accuracy of the estimated variables. 
No. of 
Stat. 
LCC 
ccc 
Solu. 
Param 
(UR cm' 2 
) 
(g m' 2 ) 
Best 
spectra 
/ 
R 2 
RMSE 
nRMS 
R 2 
RMSE 
0.24 
nRMS 
0.12 
0.35 
3.8 
0.17 
0.84 
First 10 
Median 
0.36 
3.8 
0.17 
0.84 
0.24 
0.12 
Mean 
0.36 
3.7 
0.17 
0.85 
0.23 
0.11 
First 
Median 
0.39 
3.1 
0.14 
0.81 
0.25 
0.12 
100 
Mean 
0.40 
3.1 
0.14 
0.82 
0.24 
0.11 
Table 3. R\ RMSE and normalized RMSE between measured 
and estimated leaf and canopy chlorophyll content from 
PROSAIL inversion. 
An appropriate band selection is known to improve radiative 
transfer model inversion and prevents bias in the estimation of 
the variables of interest (Schieri and Atzberger, 2006). 
Therefore, to account for band selection the inversion of the 
model was also tested with wavelengths that had an AAE 
smaller than 0.02. We considered bands with an AAE greater or 
equal to 0.02 as wavelengths with high errors (Figure 2). These 
bands were systematically excluded in the inversion process, 
and each time the AAE between the measured and best-fit 
reflectance spectra was re-calculated until all remaining 
wavelengths had an AAE smaller than 0.02. The elimination of 
wavelengths stopped after 19 iterations. The remaining 
wavebands (n=107) are called subset II and was used in the 
inversion procedure. 
The assignment of the spectral subset II in the estimation of 
grass chlorophyll was again evaluated on the basis of the R 2 and 
the normalized RMSE between the measured and estimated 
variables. The results showed that, after removing the 
wavelengths with high AAE (AAE>0.02), the relationships 
between measured and estimated leaf and canopy chlorophyll 
content were considerably improved (Table 4). 
Spectral 
Stat. 
LCC 
CCC 
sampling set 
Param 
(pg cm' 2 
) 
(gm' 2 
Using all 
R 2 
RMSE 
nRMS 
R 2 
RMSE 
nRMS 
bands 
(n=126) 
Best fit 
0.35 
3.8 
0.17 
0.84 
0.24 
0.12 
median 
0.36 
3.8 
0.17 
0.84 
0.24 
0.12 
mean 
0.36 
3.7 
0.17 
0.85 
0.23 
0.11 
Best fit 
0.37 
3.7 
0.17 
0.84 
0.25 
0.12 
Subset II 
median 
0.38 
3.4 
0.15 
0.87 
0.23 
0.11 
(n=107) 
mean 
0.39 
3.2 
0.14 
0.87 
0.22 
0.10 
Table 4. R 2 , RMSE and normalized RMSE between measured 
and estimated leaf and canopy chlorophyll content from 
PROSAIL inversion using subset II. 
Overall, the estimation accuracies between measured and 
estimated leaf and canopy chlorophyll content improved using 
the spectral subset (Table 4). This reflects the danger with