A simplified approach for yield prediction of sugar beet based on optical remote sensing data

Crop yield can be predicted already at an early stage of growth using various kinds of crop growth models with various levels c f complexity. However, estimates of crop growth and thus yield predictions in practice are inaccurate for nonoptimal growing conditions. Optical remote sensing data can provide information on plant variables which also play an important role in the process of crop growth. Leaf area index (LAI) and amount of absorbed photosynthetically active radiation (APAR) are the most important crop variables that can be estimated, for example, by using a simple reflectance model or vegetation index. Through LAI and APAR, optical remote sensing can provide information on. the actual status of agricultural crops during the growing season, thus offering the possibility of calibrating the growth modeling. In this article, a simple approach based on the fraction of absorbed photosynthetically active radiation (FPAR), estimated from optical remote sensing measurements, is derived for crop growth monitoring and yield prediction in practice. A case study for sugar beet, being one of the economically important crops in Europe, showed that FPAR can be estimated from the weighted difference vegetation index by applying a multiplication factor. Subsequently, a linear relationship between FPAR around crop closure (one date) and final beet yield already showed very good results. Parameters of this linear relationship were` not estimated empirically, but a complex physiological crop growth model was used to derive these parameters. Results were similar to the accuracy of sugar beet yield predictions obtained from directly calibrating a complex crop growth model using a time-series of optical reflectance data.