Accurate global land cover (GLC), as a key input for scientific communities, is important for a wide variety of applications. In order to understand the current suitability and limitation of GLC products, the discrepancy and pixel-level uncertainty in major GLC products in three epochs are assessed in this study by using an integrated uncertainty index (IUI) that combines the thematic uncertainty and local classification accuracy uncertainty. The results show that the overall spatial agreements (A_o values) between GLC products are lower than 58%, and the total areas of forests are very consistent in major GLC products, but significant differences are found in different forest classes. The misclassification among different forest classes and mosaic types can account for about 20% of the total disagreements. The mean IUI almost reaches 0.5, and high uncertainty mostly occurs in transition zones and heterogeneous areas across the world. Further efforts are needed to make in the land cover classifications in areas with high uncertainty. Designing a classification scheme for climate models, with explicit definitions of land cover classes in the threshold of common attributes, is urgently needed. Information of the pixel-level uncertainty in major GLC products not only give important implications for the specific application, but also provide a quite important basis for land cover fusion.

中文翻译：

---

识别和量化主要卫星衍生的全球陆地覆盖产品之间的像素级不确定性

作为科学界的重要投入，准确的全球土地覆盖（GLC）对于广泛的应用至关重要。为了了解GLC产品当前的适用性和局限性，本研究通过结合主题不确定性和局部分类精度的综合不确定性指数（IUI）评估了三个时期主要GLC产品的差异和像素级不确定性不确定。结果表明，总体空间一致性（A _oGLC产品之间的总价值低于58％，并且主要GLC产品的森林总面积非常一致，但不同森林类别之间存在显着差异。不同森林类别和镶嵌类型之间的错误分类可占总分歧的约20％。IUI的平均值几乎达到0.5，高度不确定性主要发生在世界各地的过渡带和异质地区。在不确定性较高的地区，需要进一步努力进行土地覆被分类。迫切需要设计一种气候模型分类方案，并在共同属性的阈值中明确定义土地覆盖类别。主要GLC产品中像素级不确定性的信息不仅会对特定应用产生重要影响，