Regional crop yield simulation based on crop growth model and remote sensing data

Assimilating external data into crop growth model to improve accuracy of crop growth monitoring and yield estimation is a research hotspot in recent years. In this paper, the global optimization algorithm SCE-UA (Shuffled Complex Evolution method - University of Arizona) was used to integrate remote sensing leaf area index (LAI) with crop growth model EPIC to simulate regional yield, sowing date, plant density, and net nitrogen fertilizer application amount of summer maize in Huanghuaihai Plain. The results showed that the average relative error of estimated summer maize yield was 4.37%, and RMSE was 0.44 t/hm2. By comparison of the observation data, the root mean square error (RMSE) of simulated sowing date, plant density and net nitrogen fertilization application amount was 4.16 days, 1.0 plant/m2, 40.64 kg/hm2 respectively. The absolute error of simulated sowing date was 3 days, the average relative error of simulated plant density and net nitrogen fertilization application amount was -7.78% and -10.60% respectively. The accuracy of simulated results could meet the need of crop monitoring at regional scale, and it was proved that integrating remote sensing LAI with EPIC model based on global optimization algorithm SCE-UA for simulation of crop growth condition and crop yield was feasible.