大尺度土地覆盖数据是全球陆地表层过程研究、生态系统评估、环境建模等科学研究的重要基础，研究现有数据集的特点对数据使用者及生产新的数据集都具有指导意义。本研究以中国及周边区域为研究区，根据不同分类体系对地物的定义，研究不同分类体系中对应地物的相关系数，并将所有分类体系转换为IGBP分类体系；然后，从定性和定量两方面分析现有5种土地覆盖数据集（IGBP DISCover、UMD、GLC2000、MOD12Q1和GlobCover 2005）的空间一致性；并利用Google Earth高分影像选取两期验证样本评价5种土地覆盖数据集的精度。结果表明：同种地物在不同土地覆盖数据集之间的空间分布格局差异较大，且不同土地覆盖数据集之间的总体一致性系数较低；5种土地覆盖数据集中，GLC2000的总体精度和Kappa系数均最高，GlobCover 2005的总体精度和Kappa系数均最低。

Large-scale land cover datasets comprise an important foundation of the research on land surface processes, ecosystem assessment, and environmental modeling. The evaluation of existing land cover datasets provides a guide to dataset use and new dataset production. Five kinds of global land cover datasets (IGBP DISCover, UMD, GLC2000, MOD12Q1, and GlobCover 2005) over China and adjacent regions are evaluated in this paper. First, the categories of five land cover datasets are translated into the International Geosphere-Biosphere Programme-IGBP scheme based on the correlation coefficients of the corresponding classes, which is computed according to the class definition in each land cover dataset. Second, the spatial agreements of the five land cover datasets are analyzed using visual comparison and per-pixel comparison. Finally, the classification accuracy of the five land cover datasets is evaluated based on validation samples collected through Google Earth high-resolution satellite images. The results show large areas of disagreement among the five land cover datasets, and the overall consistency among them is low. GLC2000 has the highest overall accuracy and Kappa coefficient, whereas GlobCover 2005 has the lowest overall accuracy and Kappa coefficient.