The crop growth monitoring at a regional scale based on the combination of remote sensing and process-based models
Moulin, Sophie | Launay, M. | Guérif, Martine
Two approaches can be distinguished for crop production forecasting : statistical methods, which are sitespecific and can hardly allow to assess exceptional events, and crop growth models which allow dynamic diagnostic and prognostic. However, the use of process models at regional scale is limited by the lack of spatialized information on model parameters. Here, we propose an approach where remote sensing information is used to calibrate a crop growth model for regional application topics. As an illustration, we present a study performed over a sugar beet factory basin where the most variable in space within crop parameters are those related to the crop implantation at the beginning of the crop life, and those related to roots behavior during water stress episodes. In this study, not only the spatial variability of the crop growth parameters, but also that of the radiative transfer model parameters, was taken into account. High spatial resolution airborne and SPOT data were used to calibrate the crop model for each field of the region. Then the crop model was used to predict the sugar production on a number of fields representative of the basin. Both parameters estimates and final sugar production estimates were consistent with validation ground measurements.
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