首页 >  2009, Vol. 13, Issue (2) : 282-290

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10.11834/jrs.20090250

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ASAR数据与水稻作物模型同化制作水稻产量分布图
1.南京信息工程大学应用气象学院,江苏 南京 210044;2.江苏省农业科学院农业资源与环境研究所,江苏 南京 10014;3.中国林业科学院资源信息研究所,北京 100091
摘要:

提出了利用雷达数据进行水稻估产的技术方法,并以ASAR数据为例,探讨了雷达数据在水稻估产中的可行性.首先利用ASAR数据进行水稻制图,从各时相ASAR数据中提取水稻后向散射系数.随后,基于像元尺度,采用同化方法,以LAI为结合点,将水稻作物模型ORYZA2000与半经验水稻后向散射模型结合,建立嵌套模型模拟水稻后向散射系数.选择水稻出苗期和播种密度为参数优化对象,利用全局优化算法SCE-UA对0RYZA2000模型重新初始化,使模拟的水稻后向散射系数值与实测值误差最小,并由优化后的ORYZA2000模型计算每个像元的水稻产量,生成水稻产量分布图.结果表明,水稻产量分布图能够描绘研究区水稻实际产量的分布趋势,但由于采用潜在生长条件模拟,模拟的水稻平均产量比实测平均值高约13%,验证点的水稻产量模拟值与实测值相对误差为11.2%.由于半经验水稻后向散射模型存在对LAI变化不够敏感和对水层的简化处理,增加了水稻估产的误差.但从总体上看,利用该方法进行区域水稻估产是可行的,并为多云多雨地区的水稻遥感监测提供了重要参考.

Mapping rice yield based on assimilation of ASAR data with rice growth model
Abstract:

In this paper,a practical scheme forassimilation ofmulti-temporalandmulti-polarization ENVISATASAR data in rice cropmodel tomap rice yield is presented. To achieve this, rice distribution information is obtained firstby ricemappingmethod to retrieve rice fields from ASAR images, and then an assimilationmethod is applied to use the temporal single-polarized rice backscattering coefficients which are grouped for each rice pixel to re-initialize ORYZA 2000.The assimilationmethod consists of re-initializing themodelwith optimal inputparameters allows a better temporalagree ment between the rice backscattering coefficients retrieved from ASAR data and the ones simulated by a coupledmode,l .i e., the combination of ORYZA 2000 and a semi-empirical rice back scatter model through LAI. The SCE-UA optimization algorithm is employed to determine the optimal setof inputparameters. After the re-initialization,rice yield for each rice pixel is calculated, and the yieldmap over the area of interest is finally produced. The scheme is applied overXinghua study area located in themiddle of Jiangsu Province of China during the2006rice season. The result shows that the obtained rice yieldmap generally overestimates the actual rice production by13%, with a relative errorof11.2% atvalidation sites, but the tendency of rice growth status and spatial variation of the rice yield arewell predicted and highly consistentwith the actualproduction variation.

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