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VEGETATION - 2000

Lake Maggiore - Italy, 3-6 April 2000


Use of medium-resolution imagery
in the Belgian Crop Growth Monitoring System (B-CGMS)

K. Wouters*, H. Eerens*, D. Dehem**, B. Tychon**, D. Buffet*** & B. Oger***
*Centre of Expertise on Remote Sensing and Atmospheric Processes (Vito-TAP)
Boeretang 200, B-2400 Mol, Belgium.
E-mail: jan.vanrensbergen@vito.be
**Fondation Universitaire Luxembourgoise (FUL)
Avenue de Longwy 185, B-6700 Arlon, Belgium.
E-mail: tychon@ful.ac.be
***Centre de Recherches Agronomiques (CRA)
Rue de Liroux 9, B-5030 Gembloux, Belgium.
E-mail: oger@cragx.fgov.be

Paper (pdf file, 191 k)

The yield forecasting system CGMS (Crop Growth Monitoring System) was one of the major achievements of the European MARS-programme (Monitoring Agriculture with Remote Sensing). Since a few years, CGMS is used on a continuous base to predict harvests of the main crops at the level of the EU member states. In July 1998, a 2-year pilot project was started up in order to implement a specific version of the CGMS in Belgium, the so-called "B-CGMS". In this way the Belgian Ministry of Agriculture will be able to generate its own harvest predictions in a timely way. This communication presents intermediate results after 1.5 year of project work.

The main task of the project consisted in the adaptation of the CGMS to the Belgian conditions. First, the spatial scale of the system was enhanced, such that the agrometeorological model now runs on 5km x 5km cells (instead of 50km x 50km before). Second, at the level of pedological and meteorological input, more detailed information was collected and introduced into the model. Crop parameters were also chosen typically for the Belgian territory. And finally, the model output is now specified per agricultural region and "circumscription". Currently, the adapted forecasting system is being validated on a 15-years series of yield data, obtained from the National Institute for Statistics.

Another important project task aimed at the incorporation of 1kmĀ²-resolution remote sensing imagery in order to improve the yield forecasts. The strategy developed so far first estimates the reflectances of the pure classes (crops) by application of a spectral unmixing procedure on the imagery. The required land use data per parcel were obtained from the (yearly updated) "Integrated Administration and Control System" of the Ministry of Agriculture. When this procedure is repeated on the multitemporal image set, one obtains pure and crop-specific time series, which can be converted into fAPAR-profiles (fraction of absorbed PAR). Finally, biomass accumulation is assessed by means of a Monteith-based algorithm which combines the satellite-derived fAPAR-values with meteorological data (solar irradiance, air temperature). The additional value of the obtained crop-specific estimates is currently being evaluated. Subsequently, they can be used in an integrated approach together with the B-CGMS output to come to a refined yield prediction. The methodology was developed with historical images of NOAA-AVHRR, but in the near future it will be applied as well on the data of the SPOT-VEGETATION sensor system.