| 应用粒子群算法的遥感信息与水稻生长模型同化技术 |
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| 引用本文： | 朱元励,朱艳,黄彦,姚霞,刘蕾蕾,曹卫星,田永超.应用粒子群算法的遥感信息与水稻生长模型同化技术[J].遥感学报,2010,14(6):1233-1249. |
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| 作者姓名： | 朱元励 朱艳 黄彦 姚霞 刘蕾蕾 曹卫星 田永超 |
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| 作者单位： | 南京农业大学江苏省信息农业高技术研究重点实验室,江苏,南京,210095 |
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| 基金项目： | 国家自然科学基金(编号: 30900868, 30871448); 教育部新世纪优秀人才支持计划(编号: NCET-08-0797)。 |
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| 摘 要： | 在研究遥感信息和水稻生长模型的同化过程中, 最小化遥感反演与生长模型(RiceGrow)输出的水稻生长
信息差值绝对值时引入了一种新的优化算法-粒子群算法(PSO), 并对比了其与模拟退火算法(SA)的优缺点; 探讨
了叶面积指数(LAI)和叶片氮积累量(LNA)分别作为同化参数时的同化效果。结果表明, PSO 无论是从同化效率还是
反演精度上都要好于SA, 粒子群优化算法是一种可靠的遥感与模型同化算法; LAI 和LNA 作为外部同化参数时各
有优势, LAI 作为同化参数可获得较准确的播期及播种量, 而LNA 作为同化参数可获得更为准确的施氮量信息。但
是LAI 作为外部同化参数时的反演结果总体要优于利用LNA 作为同化参数时的反演结果。利用试验资料对该技术
进行了测试和检验, 结果显示反演的模型初始参数的平均值与真实值的相对误差(RE)均小于2.5%, 均方根误差
(RMSE)为0.7—2.2, 产量模拟值与实测值之间的相对误差为5%左右, 模拟与实测相关指标值吻合度较高, 该同化
技术具有较好的适用性。从而为生长模型从单点扩展到区域尺度应用奠定了基础。
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| 关 键 词： | 粒子群算法 RiceGrow 模型 同化技术 模型参数初始化 |
| 收稿时间： | 2009/10/19 0:00:00 |
| 修稿时间： | 3/1/2010 12:00:00 AM |
Assimilation technique of remote sensing information and rice\ngrowth model based on particle swarm optimization |
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| ZHU Yuanli,ZHU Yan,HUANG Yan,YAO Xi,LIU Leilei,CAO Weixing and TIAN Yongchao.Assimilation technique of remote sensing information and rice\ngrowth model based on particle swarm optimization[J].Journal of Remote Sensing,2010,14(6):1233-1249. |
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| Authors: | ZHU Yuanli ZHU Yan HUANG Yan YAO Xi LIU Leilei CAO Weixing and TIAN Yongchao |
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| Institution: | Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Jiangsu Nanjing 210095, China;Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Jiangsu Nanjing 210095, China;Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Jiangsu Nanjing 210095, China;Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Jiangsu Nanjing 210095, China;Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Jiangsu Nanjing 210095, China;Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Jiangsu Nanjing 210095, China;Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Jiangsu Nanjing 210095, China |
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| 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 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 |
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| Keywords: | particle swarm optimization RiceGrow model assimilation technique parameter initialization |
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