Accurate identification of the spatial distribution of winter wheat is important for agricultural resource allocation and production management. However, the applicability, complementary effects, and optimal observation windows of different remote sensing data sources across key phenological stages of winter wheat remain unclear. Taking Haiyang City and Wenshang County in Shandong Province of China as the study areas, this study used multi-source remote sensing data from Sentinel-1, Sentinel-2, and Landsat-8/9 to extract spectral and backscattering features at different phenological stages. Winter wheat classification was conducted using the Random Forest algorithm, and the classification performance of different data combinations at each growth stage was compared. The results showed that winter wheat classification accuracy varied clearly with phenological development, with peak performance occurring at the heading stage in Haiyang and the grain-filling stage in Wenshang. Optical data played a dominant role in winter wheat classification. The tillering stage was identified as the earliest candidate observation window in both regions, although independent evaluation confirmed reliable early classification only in Wenshang. The incorporation of synthetic aperture radar (SAR) data did not consistently improve accuracy at all stages but provided complementary information under specific phenological and regional conditions. Overall, Sentinel-2 showed the most stable performance, and the optimal observation stage and data combination depended on regional landscape conditions.

With global population growth, accelerated urbanization, and sustained economic development in arid and semi-arid regions, oases, as core ecological carriers in desert environments, have reached un...