Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield

Abstract

This study presents a method to assimilate leaf area index retrieved from ENVISAT ASAR and MERIS data into CERES-Wheat crop growth model with the objective to improve the accuracy of the wheat yield predictions at catchment scale. The assimilation method consists in re-initialising the model with optimal input parameters allowing a better temporal agreement between the LAI simulated by the model and the LAI estimated by remote sensing data. A variational assimilation algorithm has been applied to minimise the difference between simulated and remotely-sensed LAI and to determine the optimal set of input parameters. After the re-initialisation, the wheat yield maps have been obtained and their accuracy evaluated. The method has been applied over Matera site located in Southern Italy and validated by using the dataset of an experimental campaign carried out during the 2004 wheat growing season. Results indicate that, LAI maps retrieved from MERIS and ASAR data can be effectively assimilated into CERES-Wheat model thus leading to accuracies of the yield maps ranging from 360 kg/ha to 420 kg/ha.