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In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
2. MATERIALS 
2.1 Study area and sampling 
The study site is located in a National Park in Italy (latitude 
41°52' to 42°14 , N, longitude 13°50' to 13°14'E). The park 
covers an area of 74.095 ha and extends into the southern part 
of Abruzzo, at a distance of 40 km from the Adriatic Sea. The 
region is situated in the massifs of the Apennines. The flora of 
the park includes more than 1800 plant species, which 
approximately constitute one third of the entire flora in Italy. A 
total of 45 plots (30 m by 30 m) were selected. For each plot, 
the relevant biophysical and biochemical parameters were 
measured within few randomly selected subplots. In each plot 
the species varied in terms of leaf shape, size and the amount of 
leaves. 
2.2 Vegetation parameter measurements 
A SPAD-502 Leaf Chlorophyll Meter was used to assess leaf 
chlorophyll content. A total of 150 leaves were randomly 
selected in each plot representing the dominant species and their 
SPAD readings were recorded. From the 150 individual SPAD 
measurements, the average was calculated. These averaged 
SPAD readings were converted into leaf chlorophyll contents 
[pg cm' 2 ] by means of an empirical calibration function 
provided by (Markwell et al. 1995). The total canopy 
chlorophyll content (CCC) [g m' 2 ] for each plot then have been 
obtained by multiplying the leaf chlorophyll content with the 
corresponding leaf area index 
In each plot, leaf area index was measured using the Plant 
Canopy Analyzer LAI-2000 (LICOR Inc., Lincoln, NE, USA). 
To prevent direct sunlight on the sensor, samples of below- and 
above-canopy radiation were made with the sun behind the 
operator and using a view restrictor of 45°. Table 1 reports 
summery statistics for some of the measured variables of the 
plots. 
Measured variables 
STDV 
Min 
Mean 
Max 
SPAD (unit-less) 
3.7 
24.2 
32.7 
41.0 
Leaf chlorophyll (pg cm' 2 ) 
4.7 
18.9 
28.7 
40.9 
Canopy chlorophyll (g m' 2 ) 
0.56 
0.21 
0.86 
2.3 
LAI (m 2 m' 2 ) 
1.59 
0.72 
2.87 
7.54 
Table 1. Summary statistics for some measured variables of 
sample plots. 
2.3 Hyperspectral images 
HyMap images of the study area were acquired by DLR, 
Germany’s Aerospace Research Centre and Space Agency. The 
sensor contained 126 wavelengths, operating over the spectral 
range of 436 nm to 2485 nm. The spatial resolution of the data 
was 4 m. The data were collected in four image strips, each 
covering an area of about 40 km by 2.3 km. The image 
acquisition was close to solar noon. The image strips were 
atmospherically and geometrically corrected by DLR. A 7 by 7 
pixel window centred around the central position of a plot was 
used for collection of grass spectra from each sample plot and 
its average spectrum was calculated. 
3. METHODS 
3.1 PROSAIL & Inversion 
The commonly used PROSAIL radiative transfer model which 
is a combination of the SAILH canopy reflectance model 
(Verhoef, 1984; Verhoef, 1985) and the PROSPECT leaf 
optical properties model (Fourty et al., 1996; Jacquemoud and 
Baret, 1990; Jacquemoud et al., 1996) was selected for canopy 
parameter retrieval. The PROSPECT model calculates the leaf 
hemispherical transmittance and reflectance as a function of 
four input parameters: the leaf structural parameter N (unitless); 
the leaf chlorophyll a + b concentration LCC (pg cm' 2 ); the dry 
matter content Cm (g cm' 2 ); and the equivalent water thickness 
Cw (g cm" 2 ). The SAILH model, apart from the leaf reflectance 
and transmittance, requires eight input parameters to simulate 
the top-of-canopy bidirectional reflectance. These are sun zenith 
angle, ts (deg); sensor viewing angle, to (deg); relative azimuth 
angle between sensor and sun, phi (deg); fraction of diffuse 
incoming solar radiation, skyl; background reflectance (soil 
reflectance) for each wavelength, rsl; LAI (m 2 m' 2 ); average leaf 
inclination angle, ALA (deg); and the hot spot size parameter, 
hot (m m" 1 ). To account for the changes induced by moisture 
and roughness in soil brightness, we used a soil brightness 
parameter, scale (Atzberger et al., 2003). Sensor viewing angle, 
azimuth angle, sun zenith angle and fraction of diffuse 
incoming solar radiation were fixed. 
The inversion of PROSAIL radiative transfer model was 
considered by using a look-up table (LUT). To build the LUT, 
100,000 parameter combinations were randomly generated and 
used in the forward calculation of the PROSAIL model. The 
ranges (minimum and maximum) for each of the eight “free” 
model parameters are reported in Table 2. The maximum and 
minimum values of LAI, LCC and ALA were fixed based on 
prior knowledge from the field data collection (Combal et al., 
2003; Darvishzadeh et al., 2008). To find the solution to the 
inverse problem for a given canopy spectra, for each modelled 
reflectance spectra of the LUT the root mean square error 
between measured and modelled spectra (RMSEr) was 
calculated. 
Parameter 
Min 
Max 
Leaf area index 
0 
8 
Mean leaf inclination angle 
40 
70 
Leaf chlorophyll content 
15 
45 
Leaf structural parameter 
1.5 
1.9 
Dry matter content 
0.005 
0.010 
Equivalent water thickness 
0.01 
0.02 
Hot spot size 
0.05 
0.10 
Soil brightness 
0.5 
1.5 
Table 2. Specific ranges for eight input parameters used for 
generating the LUT. 
4. RESULTS 
To find the solution to the inverse problem, the LUT is sorted 
according to the cost function and the set of variables providing 
the minimum RMSE is considered as the solution. Figure 1 
illustrates measured and simulated canopy reflectance spectra 
found in this way for two plots with contrasting LAI values.