Generally, there is a problem of inconsistency between the total area of regional crops obtained from remote sensing technology and the statistical data of crop area, which affects the application of remote sensing-based crop spatial distribution information to a certain extent. To obtain high accuracy crop spatial distribution information consistent with the statistical data of crop area, a method for extracting and mapping winter wheat spatial distribution was proposed in this study based on threshold optimization of NDVI time series similarity under regional total planting area control, and the accuracy was verified. This study took Wuyi County, Hengshui City, Hebei Province as the study area, based on the Sentinel-2 NDVI data covering the whole growth period of winter wheat, the reference and actual cross-correlation curves were obtained by the Cross Correlogram Spectral Matching (CCSM) algorithm. On this basis, the root mean square error between the two curves was calculated, and a winter wheat extraction model was constructed. Then, using the Shuffled Complex Evolution-University of Arizona (SCE-UA) global optimization algorithm, the visual interpretation data of the regional winter wheat planting area was regarded as the reference for the winter wheat planting area extracted by remote sensing, and the optimal threshold in the winter wheat extraction model was obtained. Finally, according to the optimal threshold, the winter wheat was extracted by using the winter wheat extraction model. On this basis, a comparative analysis on the accuracy of winter wheat mapping results was carried out, which were extracted from the similarity of NDVI time series in the whole growth period and the similarity and similarity combinations of NDVI time series at different growth stages, respectively. The results showed that the regional crop mapping results using the similarity of NDVI time series throughout the whole growth period were excellent, and the total area accuracy was more than 99.99%, the overall accuracy and Kappa coefficient were 98.08% and 0.96, respectively. It was proved that the method could ensure the result consistency between the total area of regional crops obtained by remote sensing and the total amount of control reference data, and a higher recognition accuracy could be obtained. Seen from the crop distribution extraction results based on the similarity and similarity combinations of NDVI time series at different growth stages, the conclusions could be drawn that the NDVI time series from the seedling stage to the tillering stage before winter and from the reviving stage to the jointing stage could be used to obtain high accuracy crop distribution extraction results, while the NDVI time series from the heading stage to the maturity stage were used to extract winter wheat, the accuracy was low. Moreover, the comprehensive application of the similarity of NDVI time series at different growth stages was beneficial to the improvement of crop extraction and mapping accuracy to a certain extent. This study could provide a certain reference for the study on regional high-precision winter wheat mapping, as well as could provide a thought thread for obtaining large-scale, long-term, remote sensing-based regional crop spatial distribution information which was highly consistent with the statistical data of crop area.