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Vegetation temperature condition index (VTCI) combines the main parameters of normalized difference vegetation index (NDVI) and land surface temperature (LST), and is applicable to a more accurate monitoring of droughts in the Guanzhong Plain, Shaanxi, China. VTCI also provides a scientific basis for drought relief and crop yield estimation by using remotely sensed data. This study chose Guanzhong Plain as the study area, and was to combine the remote sensed VTCI and simulated soil surface moisture by the CERES-Wheat (Crop environment resource synthesis for wheat) model to get a high regional yield estimation accuracy by using the fourdimensional variational (4D-VAR) data assimilation approach. The improved analytic hierarchy process, the entropy method and the joint the two weighting methods were used to establish winter wheat yield estimation models by using the monitored VTCI and the assimilated ones respectively. The optimal model for estimating winter wheat yields in the study area from 2008 to 2014 was selected, and the measured wheat yield of the year 2011 was used to validate the accuracies of the optimal model. The results showed that no matter at the sampling sites or at the regional scale, the assimilated VTCIs were all better able to respond the monitored VTCIs and the surface moisture data, and the texture of assimilated VTCI images was better and more consistent with the regional drought distribution. Compared the yield estimation models with the monitored VTCIs, the accuracies of the yield estimation models with the assimilated VTCIs were improved, and the correlation coefficients of the optimal yield estimation model with the weighted VTCIs of 0.784（P<0.001). The optimal yield estimation model was applied to estimate wheat yields in 29 counties of the Guanzhong Plain, and the results showed that except for the Pucheng County, the estimated yields’ relative errors of other 28 counties in Guanzhong Plain were less than 15%, and the errors were less than 10% in 16 counties of Guanzhong Plain. In general, the average relative error of the estimated yields was 8.68%, and the root mean square error was 4219kg/hm2, indicating the optimal yield estimation model had a better performance. The yearly estimated yields from 2008 to 2014 were in an increasing trend with fluctuation in Guanzhong Plain. For the spatial distribution of the yields, the yields were the highest in the central of Guanzhong Plain, and the yields in the west were higher than those in the east.