To improve winter wheat yield estimates in the Guanzhong Plain, China, the daily leaf area index (LAI) and soil moisture at depths of 0-20 cm (theta) simulated by CERES-Wheat model were assimilated from field-measured LAI and theta and from Landsat-derived LAI and theta using a particle filter algorithm. Linear regression analyses were performed to determine the relationships between assimilated LAI or theta and field-measured yields to identify highly yield-related variables for each growth stage of winter wheat, which were used to establish an optimal-assimilation yield estimation model. At the green-up and milk stages, assimilated. was highly correlated with the measured yields, and at the jointing and heading-filling stages, both assimilated LAI and theta were highly correlated with the yields. The optimal-assimilation yield estimation model was then established by combining the regression equations relating assimilated theta to the yields during the green-up and milk stages with the equations relating assimilated LAI and theta to the yields at the jointing and heading-filling stages, which resulted in better estimation accuracy than the yield estimation model established based on dualistic regression equations relating the assimilated LAI and theta to measured yields for each growth stage. Moreover, establishing different yield estimation models for irrigated and rain-fed farmlands improved the yield estimates compared with the established estimation model that did not take into account whether the farmlands were irrigated or rain-fed. Therefore, the assimilation of highly yield-related state variables at each wheat growth stage provides a reliable and promising method for improving crop yield estimates.