Simulation of regional winter wheat yield by combining epic model and remotely sensed LAI based on global optimization algorithm

In recent years, combining spatial and timely remote sensing data and crop growth model is an important way to improve accuracy of crop growth simulation and crop growth monitoring. In this paper, global optimization algorithm SCE-UA (Shuffled Complex Evolution method University of Arizona) was used to integrate remotely sensed leaf area index (LAI) with EPIC crop growth model to simulate regional winter wheat yield and other field management information such as sowing date, plant density and net nitrogen fertilizer application rate in Huanghuaihai Plain in China. Final results showed that average relative error of estimated winter wheat yield was 1.81% and RMSE was 0.208 t/ha. Compared with the actual observation data, average relative error of simulated plant density and net nitrogen fertilization application rate was -7.95% and -8.88% respectively and absolute error of simulated sowing date was only 1 day. These above accuracy of simulated results could meet requirements of crop monitoring at regional scale. It was proved that integrating remotely sensed LAI with EPIC model based on SCE-UA for simulation of crop growth condition and crop yield was feasible.