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Winter wheat is one of the main food crops in China. Timely and accurate localization of the planting areas of this crop is highly crucial for making adequate agricultural policies. The feasibility of a remote-sensing system for mapping winter wheat in the Fugou County was explored, combining Sentinel-1A and Sentinel-2A/B remote-sensing images. Firstly, remote-sensing images were collected to reflect the different phenological patterns of winter wheat. In particular, these images were sampled across five typical growth stages, namely, jointing, heading, flowering, milk mature, and mature stages. Then, spectral, vegetation, and polarization features were extracted from the collected images, and the characteristic curves of various ground objects were analyzed. Last, random forest classifiers were trained to accurately classify the remote-sensing images associated with four possible winter wheat models: a single-growth-stage single-sensor model, a single-growth-stage multi-sensor model, a multi-growth stage single-sensor model, and a multi-growth-stage multi-sensor model. The results showed that the single-growth-stage models cannot meet the crop mapping requirements, where the highest attained accuracy reached only 62.9% for the jointing stage. Additionally, these requirements were met by the multi-growth-stage models whose highest classification accuracy reached 81.9%. The optical and fusion images associated with the single-growth-stage models achieved the highest accuracy for the mature stage, with overall accuracies of 93.4% and 95.1%, and Kappa coefficients of 92.4% and 94.8%, respectively. These results could lead to more accurate mapping of the spatial distribution of the winter wheat crop. Also, the spatial distribution map of winter wheat in Fugou County in 2019 drawn by multi-growth-stage model has the overall accuracy of 96.8%, and the result is the best. Thus the proposed multi-growth-stage fusion model can be effectively employed in the localization and mapping of winter wheat planting areas.