Remote sensing as a tool enabling the spatial use of crop models for crop diagnosis and yield prediction

Crop models are useful tools for estimating crop growth and yield. Their spatial application, either on a regional scale for purposes of yield prediction, either on a with-in-field scale for monitoring crop management, is hampered by the difficulty of obtaining information about local conditions or crop characteristics. Particularly, weather and soil conditions may vary a lot, as well as the characteristics depending on farmers cultural operations. Information on weather and soil on large scale may be obtained with generally low spatial resolution by classical means of measurements (soil maps, weather stations), but higher spatial resolution needed for dealing within field variability may be hardly available. Remote sensing, which gives extensive spatial information on the real crop growth status, is a practical way of estimating this spatially distributed information and make the spatial application of the model possible. One of the methods consists of assimilating remote sensing data into the crop model, providing a local adjustment of the crop model. The authors show how such a method can be used on a sugar factory area to estimate sugarbeet yields using a crop model and remote sensing data (optical domain). They first illustrate the variability of emergence and early growth conditions at this scale and its consequences on yield; then they describe the methodology of model adjustment developed on a local scale, and then evaluate the possibilities of the method on a larger scale for estimating crop parameters and yield in virtual regional conditions where neither the initial condition (sowing date) nor some important crop parameters (crop establishment characteristics) are known.