The agricultural and pastoral land on the Qinghai-Tibet Plateau forms the basis for safeguarding natural grasslands and maintaining the ecological security barrier in the region. There is an urgent need for precise characterization of the spatial distribution pattern of this land. Satellite remote sensing technology is extensively used to rapidly and accurately generate spatial distribution maps of land cover. This approach offers a technical means for identifying agricultural and pastoral land. In this study， the Google Earth Engine （GEE） cloud platform is utilized， incorporating phenological knowledge and machine learning algorithms. Ground-truth data， Sentinel-1 Synthetic Aperture Radar （SAR） imagery， and Sentinel-2 optical remote sensing imagery serve as data sources. The study identifies the distribution of typical mixed agricultural and pastoral land with barley and oilseed rape cultivation on the Qinghai-Tibet Plateau by analyzing radar polarization features， optical vegetation index characteristics， and topographical features. The results of the study show that the total planting area of barley and oilseed rape in Shigatse City during 2019~2023 shows a trend of steady growth， the planting structure is relatively stable， and the distribution pattern shows obvious characteristics of more in the east and less in the west， and the overall distribution is more dispersed. Combining Sentinel-2 optical remote sensing data with Sentinel-1 SAR radar data in the classification process significantly improved the overall accuracy， Kappa coefficient and F1 score of the classification compared to using only a single data source feature. Further combination of topographic features resulted in another improvement in accuracy and a closer match between the remotely sensed estimated planted area and the actual area in the statistical bulletin. In view of this， by integrating the vegetation index， topography and backscattering features， the study achieved accurate identification of barley and oilseed rape plantations， with overall classification accuracies exceeding 92% during the period， with the lowest Kappa coefficients of 0.841 and F1 scores higher than 0.917.This study establishes a crucial methodological foundation for mapping the distribution of artificial grassland cultivation on the Qinghai-Tibet Plateau and scientifically formulating policies for grassland livestock development and ecological conservation.