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In remote sensing crop identification, the quality of sample data significantly influences the accuracy of identification. However, collecting sample data for multiple years in large regions is a laborious task. To reduce the workload of annual sample collection and improve crop identification efficiency, a sample transfer strategy was proposed based on multi-phenological feature indices. Utilizing the winter wheat distribution map of Jiaozuo City in 2019, high-quality sample data for 2020 and 2021 were generated by the correlation of multi-phenological feature index time series curves. Then the random forest machine learning method was employed to achieve automatic and efficient identification of winter wheat in Jiaozuo City for 2020 and 2021. The results indicated that by employing the proposed sample migration strategy to obtain the sample data, the overall accuracy of winter wheat identification in both years exceeded 94% when the correlation reached 0.001 at the significance level. Additionally, the Kappa coefficient was above 0.91. The coefficient of determination (R2) between the identified area and the statistical area for each county (city) reached 0.957, with root mean square error (RMSE) of 20.16km2. This demonstrated the effectiveness and precision of the proposed method in winter wheat identification. Compared with the method of transferring correlation based on single vegetation index time series curves, the overall accuracy of the method in 2020 and 2021 was improved by 1.32 percentage points and 2.27 percentage points, respectively. The Kappa coefficients were increased by 0.022 and 0.037, respectively, and the R2 between the identified areas and the statistical areas of each county was increased by 0.026 over the two years, the RMSE was decreased by 20.1%. Furthermore, when applying this transfer strategy to Xinxiang City and Hebi City, the overall accuracy of winter wheat identification exceeded 92%, and the R2 between the identified areas and the statistical areas was 0.92. The research result demonstrated that the proposed sample transfer strategy performed well across both time and space, providing insights and technical support for rapidly and accurately obtaining longterm crop distribution information in large regions.