Assimilation technique of remote sensing information and rice growth model based on particle swarm optimization
应用粒子群算法的遥感信息与水稻生长模型同化技术

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 is used for assimilating remote sensing data and RiceGrow model in minimizing difference between inverted and simulated values by remote sensing and RiceGrow model. We compare PSO with another optimization--Simulated Annealing (SA) and explored the assimilation result when LAI and LNA are used as external assimilation parameters respectively. The results show that PSO performed better than SA in both running efficiency and assimilation result, which indicates that PSO is a reliable optimization method for assimilating remote sensing information and model. LAI and LNA each have advantage as external assimilation parameters, sowing date and seeding rate can be well inverted when LAI is selected as external assimilation parameter, while nitrogen rate is better predicted using LNA. However, the inverted result is better when LAI is employed as external assimilation parameter. Experiment data is used to test the assimilation technique and result shows that the relative errors for initial parameters of growth model and yield are less than 2.5% and 5%, respectively. RMSE values are between 0.7 and 2.2, which indicates that the assimilation technique based on PSO is reliable and applicable and that this new assimilation technique can lay the foundation for crop model application from spot to region scale