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Monitoring regional crop acreage accurately and promptly is critical for ensuring food security and promoting sustainable agricultural development in China. The Google Earth Engine (GEE) cloud platform, along with fused Sentinel-1 SAR radar and Sentinel-2 SR surface reflectance imagery were employed to classify winter wheat in 2021 within the Huang-Huai-Hai Plain. The Sentinel time series data were synthesized and smoothed across various temporal scales, and a prioritization of polarization features, spectral features, vegetation index, harmonic coefficients, and textural features were conducted to explore their impacts on the accuracy and generalization ability of winter wheat identification in the region. The results showed that the feature optimization process improved the classification accuracy of the model, and the spectral features were the most significant, followed by harmonic coefficients, polarization, and textural features. Reducing the time scale of image sequences led to higher classification accuracy, with overall accuracies of 95.4%, 95.6%, and 96.4% for 30 d, 20 d and 10 d scales, respectively. However, this also resulted in a decrease in generalization ability, with corresponding scores of 0.935, 0.919, and 0.918. Shorter time scales captured finer details of ground features, achieving higher classification accuracy but showing less adaptability to data variations. Moreover, the model’s generalization ability demonstrated a spatial pattern of ‘the closer it was, the more relevant they were’. The identification of winter wheat areas using the GEE platform and Sentinel imagery was highly accurate, with overall accuracy and F1 scores of the confusion matrix exceeding 90%, and classification results were highly consistent in spatial detail with high-resolution images. Furthermore, the estimated areas of winter wheat showed a strong correlation with official municipal statistics (coefficient of determination R2>0.9).