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1.2 Optical Remote Sensing 
Crop growth models as described above were developed to formalize and synthesize knowledge on the processes 
that govern crop growth. When applied to operational uses such as yield estimation, these models often appear 
to fail when growing conditions are non-optimal (caused by stresses, e.g., fertilizer deficiency, pest and disease 
incidence, severe drought, frost damage). Therefore, for yield estimation, it is necessary to ’check’ modelling 
results with some sort of information on the actual status of the crop throughout the growing season (Bouman, 
1991). Optical remote sensing can provide such information. There are three ’key-factors’ useful in crop growth 
models that may be derived from optical remote sensing data: (a) LAI, (b) LAD and (c) leaf optical properties 
(leaf colour) in the PAR region. This is illustrated in figure 1. 
Figure 1. Possible links between optical remote sensing information and a crop growth model. 
1.2.1 Leaf Area Index (LAI). The LAI during the growing season is an important state variable in crop growth 
modelling. Moreover, the LAI is a major factor determining crop reflectance and is often used in crop reflectance 
modelling. The estimation of LAI from remote sensing measurements has received much attention. Much research 
has been aimed at determining combinations of reflectances, so-called vegetation indices, to correct for the effect 
of disturbing factors on the relationship between crop reflectance and crop characteristics such as LAI. A sensitivity 
analysis revealed that the main parameter influencing the relationship between many vegetation indices and green 
LAI is the leaf angle distribution (e.g., Clevers & Verhoef, 1993). 
1.2.2 Leaf Angle Distribution (LAD). LAD affects the process of crop growth because it has an effect on the 
interception of APAR by the canopy (e.g. Clevers et al., 1994). Moreover, it is the main parameter influencing 
the relationship between vegetation index and LAI. With optical remote sensing it is more difficult to obtain quantitative 
information on LAD than on LAI. A solution may be found by performing measurements at different viewing 
angles. Goel & Dee ring (1985) have shown that measurements at two viewing angles for fixed solar zenith and 
view azimuth angles are enough to allow estimation of LAI and the LAD by the near-infrared (NIR) reflectance. 
1.2.3 Leaf Optical Properties in the PAR Region. Leaf optical properties (leaf colour) are important in the process 
of crop growth because: (1) they influence the fraction of absorbed PAR, and (2) they can be indicative for the 
nitrogen status (or chlorophyll content) of leaves which affects the maximum rate of photosynthesis. Leaf optical 
properties in the PAR region are determined by the leaf chlorophyll content. A measure of chlorophyll content 
may be offered by the so-called red edge index (Horler et al., 1983). The position of the red edge is defined 
as the position of the main inflexion point of the red infrared slope. A decrease in leaf chlorophyll content results 
into a shift of the red edge towards the blue. 
2 - LINKING OPTICAL REMOTE SENSING WITH GROWTH MODELS 
2.1 Framework 
Two methods can be distinguished to link optical remote sensing data with crop growth models. In the first method,