1176 
with a as a combination of extinction and scattering coefficients describing the rate with which the function of 
equation (1) runs to its asymptotic value, and WDVI,, as the asymptotic limiting value for the WDVI. 
The exponential relationship between WDVI and LAI means that LAI estimations will be less accurate when 
approximating the asymptotic value of WDVI (WDVI„). In other words: the accuracy of LAI estimation will 
decrease with increasing LAI value. The standard deviation of LAI estimation can be described as: 
<r[LAI] = exp[a*LAI - ln(a*WDVI„)] * afWDVI] (2) 
The validation of the CLAIR model for sugar beet was performed by Bouman et al. (1992). They found for 
sugar beet empirically for a an estimate of 0.485 and for WDVI„ an estimate of 48.4, whereby the WDVI was 
based on green reflectance instead of red reflectance. The residual mean square for the calibration set was 4.1. 
This value may be used as an estimate of the variance of the individual WDVI measurements. The resulting estimate 
for the WDVI standard deviation (ofWDVI] in equation 2) is 2.0. Figure 1 plots the estimated LAI using the 
CLAIR model against the measured LAI (ground measurements) for the calibration set used by Bouman et al. 
(1992). In addition, the lines exhibiting deviations + /- two standard deviations from the measured LAI are shown. 
Figure 1. Relationship between estimated LAL using the CLAIR model and measured LAI for sugar beet. Flevoland 
test site, AGRLSCATT campaigns 1987 and 1988. 
2.2 LAI Estimation with the Cloud Model 
In radar modelling it appeared that the more complicated interaction models with respect to agricultural crops 
could be used as a basis for deriving simplified semi-empirical models. These models are valid for the most sensitive 
parameters and their (bio)physical plausible ranges. In former studies (Hoekman et al., 1982; van Leeuwen, 1992) 
it was shown that the Cloud model could be used as such a simple description of the radar backscatter of agricultural 
crops. However, in general this model is only valid during the beginning of the growing season, because after 
closure of the crop a constant backscatter level is reached. Another limitation is the calibration and validation 
process itself. A high temporal resolution is needed for calibrating the radar model wi thin this short period (e.g. 
sugar beet take 3 to 4 weeks from bare soil to closure). However, this is not practically feasible, as radar data 
are not provided for with this high temporal resolution. This could be solved by making use of the spatial variation, 
due to different farm management, at the time of one or two radar measurements. At the moment the Cloud model 
has not been calibrated accurately yet for most crops. 
For sugar beet a constant relationship (factor A) between the amount of crop moisture (W.h) and the LAI 
was found: 
LAI = A . W . h (3) 
For one date in the growing season we may consider the soil moisture content (mj and the soil roughness for 
all sugar beet fields in Flevoland constant. If we put: 
K = C - G*exp(B.mJ and D' = D/A 
where C, G and D are regression constants, each with their physical meaning. The parameter C represents backscatter