中文摘要：

为进一步提高冬小麦单产的估测精度和验证粒子滤波算法在同化研究中的适用性,以陕西省关中平原为研究区域,以叶面积指数（LAI）和条件植被温度指数（VTCI）为同化系统的状态变量,采用重采样粒子滤波算法同化CERES-Wheat模型模拟的与遥感数据反演的LAI和VTCI,并依据在不同类型样点应用最优同化LAI和VTCI构建的单产组合估测模型对2008—2014年冬小麦单产进行估测。结果表明,同化LAI具有良好的时间和空间连续性,可减缓CERES-Wheat模型模拟LAI的剧烈变化,其峰值出现时间与遥感LAI变化趋势基本同步,更加符合关中平原冬小麦实际变化情况;同化VTCI能同时表达模型模拟值和遥感观测值的变化趋势,且更能反映冬小麦对水分胁迫的敏感性。比较不同类型样点基于不同同化变量建立的估产模型,发现在旱作样点,同时同化VTCI和LAI的单产估测结果（R^2=0.531）优于单独同化VTCI（R^2=0.475）或LAI（R^2=0.428）的估测结果,且同时同化VTCI和LAI与实测产量间相关性达极显著水平（P〈0.001）;而在灌溉样点单独同化LAI的估测结果精度最高（R^2=0.539）,同时同化VTCI和LAI的估测结果次之（R^2=0.457）,单独同化VTCI的估测结果较差（R^2=0.243）。表明在旱作样点,冬小麦叶面积指数和水分胁迫是影响其产量形成的主要因子,而在灌溉样点,叶面积指数是影响冬小麦产量形成的主要因子。

英文摘要：

Data assimilation（ DA） provides a way for effective combination of model simulation and observation,and improves accuracy of winter wheat yield estimation. Among various DA methods,the particle filter（ PF） is not constrained by the conditions of linear models and Gaussian error distribution,and receives more attention and application of DA. Currently,most researchers adopt single remotely sensed data source and single variable assimilation strategy, which cannot accurately reflect the interactive process among radiation, temperature and water, and limit the performance of data assimilation systems. To improve accuracy of winter wheat yield estimation,a particle filter algorithm was proposed,which was based on a sequential important sampling procedure of assimilating leaf area index（ LAI） and vegetation temperature condition index（ VTCI） retrieved from MODIS data into the CERESWheat model（ Crop environment resource synthesis for wheat） to estimate winter wheat yield from 2008 to 2014 in Guanzhong Plain,Shaanxi,China. In order to determine effects of the assimilated variables on winter wheat yield estimation under different management practices,eight typical rainfed farming sites and four irrigation sites were selected,and the assimilated LAI or VTCI or both of them were used toestablish winter wheat yield estimation models. The results showed that the assimilated LAI had good temporal and spatial continuity,and the sharp changing points of seasonal LAI were decreased after applying the particle filter assimilation algorithm. The peak and seasonal trend of the assimilated LAI were basically in agreements with those of the remotely sensed LAI,and the problem of low values of MODIS-LAI was solved to a certain degree after assimilation. The seasonal change of assimilated VTCI was in good agreement with those of both the remotely sensed VTCI and the simulated VTCI,and the assimilated VTCI was a good index for indicating crop water stress of winter wheat. These results suggested that the assimilation