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Two specific study sites were selected to represent the extreme conditions of the climatic gradient in the study area: 
Avisur site representing the northern and humid zone and Lehavim site representing the more southern and arid zone. 
The remote sensing data used in this study include three ERS-2.SAR.PRI C-band images, acquired at February, April 
and May 1997. These dates represent the vegetation status in the study area at the beginning, peak, and the end of the 
winter season and thus provide a variety of phenological phases of annual herbaceous vegetation. In addition to the 
remote sensing data, a detailed field campaign was carried out simultaneously with the satellite overpass. 
Measurements of gravimetric soil moisture (at the top 5 cm) using the double weight technique were taken and 
herbaceous vegetation green biomass, was measured randomly along 100 meter transects using the harvest and 
assessment method (Tadmor et al. 1975). 
3 METHODS 
The remote sensing methodology proposed here includes a calibration of the ERS-2.SAR.PRI image DN to sigma 
naught (decibels and power units) considering both viewing geometry and local topography. In addition, correction of 
the image’s file coordinates to Real World coordinates (Israel New Grid) was carried out based on 250 ground control 
points and a first order algorithm with a root mean square error of less than one pixel. The derivation of backscatter 
(6^) from the DN value of ERS-2 SAR PRI image (equation 1) was executed based on the method of Laur et al. 
(1997): 
c 9 = x DN i [ee sin c (1) 
N j-i k SIN OL er 
where N is the number of pixels within an area of interest; DN is the ERS-2 SAR image digital number and the 
average in the square parenthesis is calculated following the application of a mean filter with a 3x3 pixel window size 
to reduce speckle effects; K is the processing center specific calibration constant; C accounts for updating the gain due 
to the elevation antenna pattern implemented in the processing of the ERS SAR PRI data products and o, and os are 
the mid-range and reference incidence angle respectively. Since the study area is hilly, backscatter was adjusted for 
variations in the local angle of incidence, derived from a digital elevation model (Shoshany et al. 1998). 
The image preprocessing enabled to derive ERS-2 SAR backscattering coefficient values from surface covered by the 
four dominant Mediterranean vegetation formations. The image sampling procedure was executed at 50, relatively 
homogenous, plots per vegetation formation per date. The understanding of the backscattering mechanism from the 
Mediterranean vegetation formations has lead us to apply the semi-empirical water cloud model (Attema and Ulaby 
1978). This model was applied earlier for vegetated surfaces based on ERS-2 SAR data but was mainly used for 
agricultural crops (Xu et al. 1996). The general form of the water cloud model is described in equation 2: 
c°= Af(L)cos 6(1-exp(-2BL/cos 0))+(C+ Dm,)exp(-2BL/cos 0) (2) 
where 6° is the radar backscatter [m°/m°]; cos 6 is the cosine of the incidence angle; L is a canopy descriptor such as 
LAI or biomass; and (C+Dm,) is the soil contribution which can be derived from the linear soil moisture model and 
converted to power units [m"/m^]; A and B are the canopy coefficients and should be adapted empirically to each 
vegetation layer. The determination of the canopy coefficients was executed by a non-linear least square regression 
code based on the E04FDF procedure of the NAG libraries. The initial points were set to 0.005 for both coefficients 
based on previous works that determined the canopy coefficients for agricultural crops such as wheat (Prevot et al. 
1993) and sugar beet (Xu et al. 1996). 
This form of the water cloud model could be used for homogenous areas but is not suitable for the heterogeneous 
environment that characterize the Mediterranean region. In order to adapt the water-cloud model for Mediterranean 
environments we propose to include in the model the unmixing method of Shoshany and Svoray (2000) which is 
based on a multi-temporal multi-spectral approach. This adaptation has enabled us to receive the contribution of each 
of the vegetation formations to each pixel. Our suggestion for the application of the water-cloud model in 
heterogeneous areas is to incorporate the cover fraction assessments applied on Landsat TM images with the general 
form of the water cloud model. Such a an incorporated model can use the form described in equation 3: 
0° = Aficos 0(1-exp(-2B,GVDy/cos 0)) + Agf;cos O( l-exp(-2B4GVDycos 0)) + (3) 
Anfncos 60(1-exp(-2B,GVDy/cos 6)) +f,(C+ Dm,)exp(-2B,GVDy/cos 6) 
where different A and B canopy coefficients are related to the different three vegetation formations — shrubs, dwarf 
shrubs and herbaceous vegetation; different f are the cover fractions of the three vegetation formations and different 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part Bl. Amsterdam 2000. 323