Vegetation temperature condition index (VTCI) combines normalized difference vegetation index (NDVI) and land surface temperature (LST), and is applicable to a more accurate monitoring of droughts in Guanzhong Plain, Shaanxi Province, China. Quantile regression is a tool for comprehensively reflecting the conditional distribution characters under different quantiles, and its regression results are steady and reliable. In order to achieve a better correlation between winter wheat yield and the weighted VTCI as well as a higher yield estimation accuracy, linear regression models between the weighted VTCI and yields in the cities of Guanzhong Plain in the years from 2008 to 2014 were analyzed by using the quantile regression whose quantiles were set to be 0.1, 0.3, 0.5, 0.7 and 0.9, respectively. These quantile regression results roundly reflected the distribution of the yields under different drought conditions and were beneficial supplement of the linear regression from which the single fitted line and impressionable results from outliers were obtained. The wheat yield estimation model based on the median regression (quantile equalled to 0.5) was used to monitor the wheat yields in the cities of Guanzhong Plain from 2008 to 2014, the average and minimum values of the relative errors and the root mean square errors (RMSE) between the estimated yields and the actual yields were all lower than those derived from the ordinary least square method. Additionally, the characteristics of inter-annual evolution and spatial distribution of the estimated yields using the median regression model were in good agreement with the actual situation, which indicated that the quantile regression was feasible and reliable in the research of winter wheat yield estimation and the relationship between yield and drought.