应用粒子群算法的遥感信息与水稻生长模型同化技术.pdf

1007-4619 (2010) 06-1226-15 Journal of Remote Sensing 遥感学报 Assimilation technique of remote sensing information and rice growth model based on particle swarm optimization ZHU Yuanli, ZHU Yan, HUANG Yan, YAO Xia, LIU Leilei, CAO Weixing, TIAN Yongchao Jiangsu Key Laboratory for Information Agriculture , Nanjing Agricultural University, Jiangsu Nanjing 210095, China 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 optimiza- tion--Particle Swarm Optimization (PSO) technique is used for assimilating remote sensing data and RiceGrow model in mini- mizing difference between inverted and simulated values by remote sensing and RiceGrow model. We compare PSO with an- other optimization--Simulated Annealing (SA) and explored the assimilation result when LAI and LNA are used as external as- similation parameters respectively. The results show that PSO performed better than SA in both running efficiency and assimila- tion 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 re- sult 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 le