m3. 
4 - RESULTS OF MAC EUROPE CAMPAIGN 1991 FOR SUGAR BEET 
to the various plant organs, through partitioning factors introduced as a function of the phenological development 
stage of the crop. An important variable that is similated is the LAI, since the increase in leaf area contributes 
to next day’s light interception and hence to next day’s rate of assimilation. 
When applied to operational uses such as yield estimation, models such as SUCROS often appear to fail when 
growing conditions are non-optimal (e.g. 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). Remote sensing can provide such information. 
3.2 Combination Method Using Inverse Models 
In the inverse modelling method the SUCROS crop growth model is initialized and calibrated to fit simulated 
LAI values to estimated LAI values obtained from remote sensing measurements. Thus, first the CLAIR and/or 
inverted Cloud model are applied for obtaining LAI estimates from the remote sensing measurements. Subsequently, 
the SUCROS model is calibrated on these LAI estimates. Since we have seen that the accuracy of the LAI estimates 
depends on the absolute value of the LAI, the reciproke of the standard deviation of LAI estimation is used as 
a weighting factor for each individual LAI estimate used in the optimization procedure. For LAI estimates from 
optical measurements equation (1) is used and for LAI estimates from radar measurements equation (4) is used. 
In addition, parameter estimates obtained during the calibration of CLAIR and Cloud model, respectively, are 
used in these equations. This approach yields at the same time a proper mutual weighting between optical and 
radar data when data from both windows are used together in the optimization procedure. Moreover, it is obvious 
that for the methodology it is not relevant whether one has optical and radar data at the same date or not. 
4.1 Optical Remote Sensing 
As a reference, first, SUCROS was calibrated so that simulated LAI matched LAI-values estimated from WDVI 
measurements performed with a field radiometer ("CROPSCAN") for ten sugar beet fields (Flevoland test site). 
The radiometer measurements consisted of time-series of about 10 measurement dates spread all over the growing 
season. Results are given in'table 2. 
The measurements obtained from three CAESAR recordings (July 4th, July 23rd and August 29th) during 
the MAC Europe campaign in 1991 over the Flevoland test area were used for testing the calibration procedure 
for sugar beet using optical data only. For each date the LAI values for ten sugar beet fields were estimated from 
the CAESAR recordings. Subsequently, SUCROS was calibrated on these three LAI estimates. Results are given 
in table 2. The comparison between estimated and actual yield is given in figure 4. Results using only three dates 
during the growing season in the calibration procedure seem to offer quite satisfactory results, that are not much 
worse than using a time-series of optical data. On the average, the simulation error of (fresh) beet yield was 4.2 
t/ha (5.5% error) with SUCROS calibrated on three CAESAR dates (see table 2). 
(a) 
estimated beet yield 
(b) 
estimated beet yield 
actual beet yield (tons/ha) 
actual beet yield (tons/ha) 
Figure 4. Comparison between estimated yield and actual yield for 10 optical CROPSCAN measurements (a) and 
3 optical (CAESAR) recording dates (b). 
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