In this paper, a practical scheme for assimilation of multi-temporal and multi-polarization ENVISAT ASAR data in rice crop model to map rice yield is presented. To achieve this, rice distribution information is obtained first by rice mapping method to retrieve rice fields from ASAR images, and then an assimilation method is applied to use the temporal single-polarized rice backscatteriug coefficients which are grouped for each rice pixel 'to re-initialize ORYZA2000. The assimilation method consists of re-initializing the model with optimal input parameters allows a better temporal agreement between the rice backscattering coefficients retrieved from ASAR data and the ones simulated by a coupled model, i. e. , the combination of ORYZA2000 and a semi-empirical rice backscatter model through LAI. The SCE-UA optimization algorithm is employed to determine the optimal set of input parameters. After the re-initialization, rice yield for each rice pixel is calculated, and the yield map over the area of interest is finally produced. The scheme is applied over Xinghua study area located in the middle of Jiangsu Province of China during the 2006 rice season. The result shows that the obtained rice yield map generally overestimates the actual rice production by 13% , with a relative error of 11.2% at validation sites, but the tendency of rice growth status and spatial variation of the rice yield are well predicted and highly consistent with the actual production variation.%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.
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