Assimilation Technique of Remote Sensing Information and Rice Growth Model Based On Particle Swarm Optimization.

Abstract: The choice of optimization method is very important in the assimilation process of crop growth model and remote sensing data, and it concerns the running efficiency and result accuracy of assimilation. In this study, a new optimization--Particle Swarm Optimization (PSO) technique was used for assimilating remote sensing data and RiceGrow model in minimizing difference between inverted and simulated values by remote sensing and RiceGrow model. We compared PSO with another optimization--Simulated Annealing (SA) and explored the assimilation result when LAI and LNA were used as external assimilation parameters respectively. The results showed that PSO performed better than SA in both running efficiency and assimilation result, which indicated that PSO is a reliable optimization method for assimilating remote sensing information and model. LAI and LNA each had advantage as external assimilation parameters, sowing date and seeding rate could be well inverted when LAI was selected as external assimilation parameter, while nitrogen rate was better predicted using LNA. However, generally speaking, the inverted result is better when LAI is employed as external assimilation parameter. Experiment data was used to test the assimilation technique and result showed that the relative errors for initial parameters of growth model and yield were less than 2.5% and 5%, respectively. RMSE values were between 0.7 and 2.2, which indicated that the assimilation technique based on PSO was reliable and applicable and that this new assimilation technique could lay the foundation for crop model application from spot to region scale. Key Words: Particle Swarm Optimization; Remote sensing information; RiceGrow model; Assimilation technique; Parameter initialization