1178 
2.3 Combined Use of Contemporary Optical and Radar Data 
When looking at the results in section 2.2, it is striking that the standard deviation of LAI estimation from radar 
becomes quite laige already at small LAI values. This is quite contrary to the situation in the optical domain as 
described in section 2.1. The comparison between standard deviations of LAI estimates from optical and radar 
measurements is illustrated in figure 3. This figure clearly illustrates that the accuracy of LAI estimation from 
radar measurements is much worse than from optical measurements except for very low LAI values. So, only 
little additional value is to be expected from radar measurements for LAI estimation when optical measurements 
are available and no synergy occurs in the estimation of LAI. 
The significance of radar measurements lies in the possibility of obtaining information about crop growth 
at periods that optical remote sensing is not possible from a practical point of view (mainly caused by bad weather 
conditions) and in the possibility of obtaining information about the plant structure. Therefore, in the rest of the 
study emphasis is put on monitoring the growth of crops in a dynamical way using grcwth models (non-contemporary 
approach). However, it must be noted that the above-described contemporary approach does yield synergy in 
the way that optical remote sensing measurements are used for calibrating the Cloud model, which would not 
have been possible without optical data in this study. 
L—band HH —pol. 
(b) C —band W-pol. 
Figure 3. Comparison of standard deviations of LAI estimates from optical and radar measurements, (a) L-band 
HH-polarization; (b) C-band W-polarization. 
3 - DYNAMICAL MODELLING OF CROP GROWTH 
3.1 Crop Growth Models 
Since the 19th century, agricultural researchers have used modelling as a tool to describe relationships between 
crop growth (yield) and environmental fee tors such as solar irradiation, temperature and water and nutrient availability. 
The models compute the daily growth and development rate of a crop, simulating the dry matter production from 
emergence till maturity. Finally, a simulation of yield at harvest time is obtained. The basis for the calculations 
of dry matter production is the rate of gross C0 2 assimilation of the canopy. Input data requirements concern 
mainly crop physiological characteristics, site characteristics, environmental characteristics and the initial conditions 
defined by the date at which the crop emerges. 
SUCROS (Simplified and Universal Crop Growth Simulator, Spitters et al., 1989) is a mechanistic crop growth 
model that describes the potential growth of a crop from irradiation, air temperature and crop characteristics. 
Potential grcwth means the accumulation of dry matter under ample supply of water and nutrients, in an environment 
that is free from pests and diseases. The light profile within a crop canopy is computed on the basis of the LAI 
and the extinction coefficient. At selected times during the day and at selected depths within the canopy, photosynthesis 
is calculated from the photosynthesis-light response of individual leaves. Integration over the canopy layers and 
over time within the day gives the daily assimilation rate of the crop (partly from Spitters et al., 1989). Assimilated 
matter is first used to maintain the present biomass (maintenance respiration) and for the remainder converted 
into new, structural plant matter (with loss due to growth respiration). The newly formed dry matter is partitioned