Timely and accurate information on winter wheat distribution and planting area is of great significance to food security, policy-making, and ecological function evaluation. However, several problems exist in the traditional winter wheat mapping approaches using remote sensing data, such as the limited spatial resolution of the remote sensing image data, the utilization of full-season remote sensing data, and the heavy dependence on training data. In this context, we propose a method based on the Sentinel-2 time series data with a 10-m spatial resolution to map winter wheat in Shandong, China. This is a novel, easy-to-operate, and effective mapping method, which is called the automated early-season method to map winter wheat using the Sentinel-2 data (AEMMS). The model is based on the assumption that the biomass accumulated by winter crops (mainly consisting of winter wheat and garlic in Shandong province) is gradually increasing and other vegetation is gradually decreasing in the early-season phenological window phase. In addition, winter wheat is the crop that accumulates the most biomass among all the winter crops in Shandong province, and the normalized difference vegetation index (NDVI) value of winter wheat is generally higher than that of garlic. We designe five phenological metrics and a series of classification rules for winter wheat discrimination. The AEMMS method has the following advantages: (1) it achieves high spatial resolution winter wheat mapping with a 10-m spatial resolution; (2) it is an early-season mapping method, which provides winter wheat maps nearly 5 months before harvest; and (3) it is automatic and needs no training sample data. The AEMMS method was applied in Shandong, China, to discriminate winter wheat for the 2017–2018 season. Winter wheat areas were derived in all 17 of the municipal administrative regions of Shandong province, and a strong correlation was observed between the derived winter wheat areas and the official statistics, with the coefficient of determination reaching 0.8973. A high mapping accuracy was also achieved in Jiaxiang County using the AEMMS method, with an overall accuracy of 97.80% and a kappa coefficient of 0.9368.