光学遥感数据与作物模型同化是实现区域化作物长势监测和产量评估的重要途径。然而，同化过程复杂且受制于一些不确定因素，导致区域应用精度较低。其中，遥感对地观测空间分辨能力不足是限制同化模拟精度的重要原因之一。近年来，国内外连续发射了多颗兼顾高时空分辨率的光学遥感卫星，为降低观测误差提供了数据支持。本项目拟应用多源高分多光谱遥感数据，结合严格定标的冬小麦模型，研究高分遥感观测、时空尺度和同化方法的不确定性及其对同化的影响。通过在像元尺度上搭建两种同化法的同化模拟平台，以高分遥感反演的作物生长参数为同化变量，比较高分辨率数据与中、低分辨率数据的同化差异，并从观测步长、所处生育阶段的组合等方面共同分析时空尺度上的不确定性。再调整两种同化法各自的系统参数，分析和比较不同算法下同化的不确定性和运行效率。最后，提出并验证适用于精细化作物监测和产量评估的最优同化方案，进一步探讨减少同化不确定性的途径。

Assimilation of optical remote sensing data into crop model is an important way to implement the regional crop growth monitoring and yield estimation. However, assimilation is complex and always restrained by several uncertainty factors, leading to unreliable results in regional application. Among these factors, insufficient spatial resolution in the observation is the most evident issue. In recent years, China and other countries have launched several optical remote sensing satellites with high-spatial-resolution sensors, which provides the possibility for reducing assimilation uncertainties. The high spatial resolution multi-spectral remote sensing data will be used in this program. The uncertainties in high resolution remote sensing observation, temporal and spatial scales in the remote sensing data and assimilation algorithms, and their effects on assimilation results will be explored by using a well-tuned winter wheat crop model. Observation errors will be analyzed through canopy LAI which is an assimilation variable retrieved from two different assimilation algorithms using remote sensing data. The differences in assimilation results from high, middle, and low spatial resolution data will be investigated to understand uncertainties from different spatial scales of assimilation data. The uncertainty from temporal scales caused by investigation step, wheat growth stages and their combination will be also analyzed. By adjusting the systematic parameters of the two algorithms, uncertainties and work efficiency will be analyzed and compared. Finally, based on the above-mentioned analysis, we put forward an optimal assimilation scheme for regional precision crop monitoring and yield estimation and validate it with observations, through which a way to reduce uncertainty in the assimilation process will be discussed.