【摘要】 青藏高原农牧地是保护天然草地、维持该区域生态安全屏障的基础，其空间分布格局亟需进行精确刻画。卫星遥感技术广泛应用于快速准确获取地表覆盖的空间分布制图，为农牧地识别提供了技术途径。本研究利用Google Earth Engine(GEE)云平台，结合物候知识和机器学习算法，以实地调研获取的样点数据、Sentinel-1 Synthetic Aperture Radar(SAR)雷达影像和Sentinel-2光学遥感影像为数据源，通过雷达极化特征、光学植被指数特征、物候特征和地形特征，对青藏高原典型的混合农牧地青稞和油菜种植分布、种植频率等进行识别。研究结果表明：2019～2023年间，日喀则市青稞和油菜的总种植面积呈现稳步增长的趋势，种植结构相对稳定，且分布格局表现出明显的东多西少特征，整体分布较为分散。在分类过程中，将Sentinel-2光学遥感数据与Sentinel-1 SAR雷达数据相结合，相比仅使用单一数据源特征，显著提升了分类的总体精度、Kappa系数和F1得分。进一步融入地形特征后，精度再次提升，且遥感估算的种植面积与统计公报中的实际面积更加接近。鉴于此，整合植被指数、地形以及后向散射特征，研究实现了对青稞和油菜种植地的精准识别，期间的总体分类精度均超过92%,Kappa系数最低值为0.841,F1得分均高于0.917。本研究为进一步开展青藏高原人工草地种植分布制图，科学制定草地畜牧业发展及生态保护政策，提供了重要的方法基础。更多还原

【Abstract】 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.更多还原