Estimation of crop yield using CERES-Wheat model based on particle filter data assimilation method

In order to validate the feasibility of particle filter algorithm for crop yield estimation using crop growth model as well as accuracy, the data assimilation system of CERES-Wheat (crop environment resource synthesis for wheat) model was integrated. The ground observation was employed to perform an experiment to test the capability of data assimilation system for crop yield estimation. The effect of particle dimension and perturbed variance on accuracy and efficiency was also discussed. The results showed that CERES-Wheat data assimilation system based on particle filter could correctly adjust the trajectory of model states and significantly improve the precision of forecasted crop yield. The coefficient of determination R2 between simulated and observed winter wheat yields before and after performing PF assimilation scheme increased from 0.68 to 0.83, normalized root mean square error (NRMSE) from 4.93% to 3.40%, and relative error (RE) from 4.15% to 2.93%. For the trials of perturbed particle size and variance, there were no significant improvement when particle size increased from 50 to 250, but the computation time increased by 5 times. As perturbed variance of particles increased, NRMSE and RE increased by 0.32% and 0.26%, respectively. Therefore, the disturbed dimensions and variance of particles should be determined based on a compromised consideration of both the simulated precision and computed cost. This study provides a valuable reference for monitoring crop growth and yield estimation on regional scale using multi-sources remote sensing data.