Application of Meteosat derived meteorological information for crop yield predictions in Europe
This paper presents the utilization of surface fluxes and relative evapotranspiration derived from satellites for crop yield prediction using a dedicated crop growth simulation algorithm, the Environmental Analysis and Remote Sensing (EARS) Crop Growth Simulation algorithm (EARS-CGS). The objective was to test the EARS-CGS algorithm independent of ground data for crop yield prediction at national level in Europe. The algorithm is based on existing crop yield models but has been modified to assimilate satellite derived global solar radiation and actual evaporation information. The algorithm simulates crop biomass. A statistical method is utilized to relate crop biomass to crop yield and to correct for regional differences in yields that are not the result of radiation or water limitation. Six years of Meteosat data were processed to predict winter wheat and spring barley yields for Spain and the UK. The predicted yields were compared to the national reported yields and to forecasts of the European Statistical Office (EUROSTAT) and the Monitoring Agriculture by Remote Sensing-Crop Growth Monitoring System (MARS-CGMS). To evaluate the timeliness of the predictions the reported yields were compared to yield predictions made at different stages of the growing season. The results presented in this paper demonstrate that crop yields predicted from meteorological satellites can be applied to provide timely and reliable crop yield forecasts.