The spatiotemporal distribution information of crops is essential. However, remote sensing mapping still faces challenges, such as fragmented land plots, cloud cover, and changes in crop morphology across the phenological stages. To address these issues, we constructed a time series multi-feature dataset using the flexible spatio-temporal data fusion method, which fused Sentinel-2 and MODIS data, with the addition of Sentinel-1 SAR data. We employed the time-weighted dynamic time warping (TWDTW) algorithm and compared its performance with the random forest (RF) algorithm. By comparing the crop classification results of different lengths of time-series multi-feature datasets, we investigate the earliest identifiable time for crops. The results demonstrate that the incorporation of multi-source time-series remote sensing data in the construction of crop phenological features effectively improves crop classification accuracy. The TWDTW algorithm attained an overall accuracy of 92.28% and a Kappa coefficient of 0.895, outperforming the RF method, which had an accuracy of 90.44% and a Kappa coefficient of 0.869. Early-stage time-series data was enough to identify wheat, whereas complete time-series data for the entire growing season was required for oilseed rape and fallow fields. In the early stages of identification, using spatial-temporal fusion can improve crop recognition accuracy. This study found that the spatio-temporal fusion of multi-source remote sensing data and TWDTW offers the potential for accurate crop classification, especially in areas with complex fragmented cropping systems.