Winter wheat is one of the major food crops in China. It is of great importance to timely and accurately monitor winter wheat cultivation for the formulation of agricultural policies. Therefore, to accurately and effectively calculate the planting information of winter wheat, we proposed a method for extracting winter wheat from short-time series synthetic aperture radar (SAR) data. It combines three new SAR indices, SAR backscatter features, and coherence features for extracting the winter wheat based on Sentinel-1 images from October 2021 to June 2022. First, the SAR backscatter coefficients were counted, and the newly constructed SAR indices and coherence features were introduced to increase the distinction between the winter wheat and other ground objects. Subsequently, the artificial neural network (ANN), support vector machine (SVM), and random forest (RF) classifiers were used to identify the main ground objects. The spatial distribution of winter wheat was obtained, and the accuracy was verified. Finally, the planting area of different growth stages for winter wheat was compared, and the relative errors between the extraction area of winter wheat and official statistics data also were calculated. The results showed that: (1) The accuracy of the RF classifier is better than SVM and ANN in extracting winter wheat, the overall accuracy is 95.653%, the Kappa coefficient is 0.933, and producer accuracy and user accuracy are 97.68% and 98.19%, respectively. (2) A more accurate thematic map of winter wheat can be obtained by combining the SAR backscatter features, new SAR indices, and coherence features. (3) By comparing the extraction results of different growth stages, the accuracy of the tassel stage is the highest. The short-time series SAR data of the tassel stage are constructed to replace the time series SAR data of the complete growth stage. It provides basic data for carrying out acreage and yield estimation of winter wheat before maturity.