Abstract: Grain safety has been attracting much more attention as the population increasing and cultivated land decreasing continually. Taking Guangrao County, Shandong Province as the study area, winter wheat yield estimation techniques were explored based on high and moderate resolution remote sensing data at county level. Selecting four medium and high resolution images of Landsat and China-Brazil Earth Resources Satellite(CBERS), which hare the similar temporal characteristics with obvious winter wheat information, the area of winter wheat was acquired by a decision tree classification approach through image pre-processing, including geometric accurate correction, masking and relative radiometric calibration, based on the analysis of spectrum characteristics of typical objects in the study area. Vegetation indexes were calibrated according to their changing trends, and winter wheat yield estimation models were established based on the sum of vegetation index with pixel ratio (∑RVI) and normalized difference vegetation index (NDVI) in different growing areas, respectively, according to their relations to wheat yields. The results showed high precision for winter wheat area extraction of more than 96%, and precision for yield estimation of two models were better than 96% and 94.74%, respectively. The study provides an effective method for winter wheat yield prediction, which is favorable for winter wheat production and grain policy-making and has significance for regional agriculture sustainable development and grain safety.