土地覆盖数据是气候变化研究、生态环境建模、陆表过程模拟、地理国情监测等不可或缺的重要基础数据。近年来，随着遥感技术和计算机存储及计算能力的不断提升，全球土地覆盖制图取得了突破性的进展，正逐步从中低分辨率向30米中高分辨率过渡。然而，考虑到地球系统本身的复杂性和各产品制图策略间的差异性，用户如何从多种全球30米土地覆盖产品中挑选最合适的数据集依然存在较大的不确定性。因此，本研究聚焦于全球30米土地覆盖产品的一致性分析和定量精度评估，分别从全球和区域尺度分析各产品的精度状况，进而为土地覆盖相关用户提供科学的认知和准确的数据支撑。

研究以国内外共享的全球30米土地覆盖产品（包括全要素和专题要素）为研究对象，分别开展了全要素土地覆盖产品分类体系统一性处理、全要素和专题要素产品一致性分析以及基于区域/全球验证样本数据集的精度定量评估三方面的研究，得到如下结论：

（1）基于语义相似度的分类体系转换是解决不同全球30米土地覆盖产品不兼容的重要手段。通过欧洲土地监测环境信息和观察网行动小组（Environmental information and observation network Action Group on Land monitoring in Europe, EAGLE）提出的EAGLE概念计算类别间的语义相似度，并基于此构建严格的分类体系转换关系，可以最大程度减小因为类别转换差异对不同产品集间对比分析结果带来的影响。

（2）全球30米土地覆盖产品因制图策略、数据源等差异导致其空间差异性较为显著。对于全要素数据集，研究结果表明在全球范围内，三种全要素产品在空间上完全一致的像元数占总像元数的35.40%，完全不一致像元所占百分比为29.14%。对于各专题要素（不透水面、林地、耕地以及水体）不同数据集，结果表明不同水体专题要素产品的一致性较其他要素而言更高，而不同耕地专题要素产品间的一致性最低，其空间一致性最高的产品对的R²仅为0.67。

（3）基于区域/全球验证数据集的定量精度评估为用户选择合适数据产品提供了科学的认知和定量数据指标。研究基于混淆矩阵的精度检验结果表明在美国区域以及全球范围内，GLC_FCS30-2015产品的总体精度最高，其次为GlobeLand30-2010，FROM_GLC30-2015产品的总体精度最低；同时，针对用户的不同需求加权后的总体精度明显高于传统基于混淆矩阵得到的总体精度，且针对不同的用户需求，产品集的精度状况表现出了明显差异。

本文的主要创新点包括：

（1）构建了一种基于语义相似度度量的分类体系转换规则来解决全球土地覆盖产品的分类体系不兼容的问题，减少了传统分类体系转换中人为引入的误差，能够更为准确地反映产品数据集本身之间的一致性与差异状况；

（2）通过引入产品中各地类的面积占比，将传统混淆矩阵中对精度指标的有偏估计转为无偏估计，减少了精度定量评估的不确定性；同时提出一种加权的精度评估策略以更好分析不同用户需求下的精度差异。

Land cover data is a fundamentally important and indispensable data for research on climate change, ecological environment modeling, land surface process simulation, and national conditions monitoring. In recent years, with the improvement of remote sensing technology and computer storage and computing capabilities, breakthroughs have been made in global land cover mapping, which is gradually transitioning from low-medium resolution to mudium-high resolution of 30 m. However, considering the complexity of the earth system itself and the differences between the mapping strategies of various products, there is still a big uncertainty in how users choose the most suitable product from the multisource global 30 m land cover products. Therefore, this study focuses on the consistency analysis and quantitative accuracy assessment of global 30 m land cover products, and analyzes the accuracy of each product at global/regional scales, and then provides scientific knowledge and accurate data support for land cover related users.  

Taking the global 30 m land cover products (including all-element and thematic elements) currently available internationally as the research object, this study carried out several specific studies, including the unification of the different classification system of all-element land cover products, the consistency analysis of the all-element and thematic element products, and the quantitative assesement of the accuracy based on the regional/global validation sample data sets. The main results of this study are as follows:

(1) Taking into account the impact of different classification systems on the comparative analysis of different products, when evaluating and comparing the accuracy of land cover products, it is necessary to minimize the impact of classification differences on the results.  To solve this problem, the EAGLE (Environmental information and observation network Action Group on Land monitoring in Europe) concept was adapted to calculate the semantic similarity between the classification systems, and a strict classification system conversion relationship based on the semantic similarity was constructed.

(2) The global 30 m land cover products have significant spatial differences due to differences in mapping strategies and data sources. For all-element datasets, the results showed that the number of spatially consistent pixels of the three GLC products accounted for 35.40% of the total number of pixels, and the percentage of completely inconsistent pixels was 29.14% on a global scale. For the consistency analysis of different datasets of each thematic element (including the impervious surface, forest, cropland and water land cover types), the results showed that among the four thematic elements, the consistency of the water products had higher consistency than other elements, while the consistency of the products of cropland were the lowest, and the product pair with the highest spatial consistency has an R² of only 0.67.

(3) Quantitative accuracy assessment based on regional/global validation data sets provides users with scientific congnition and quantitative data indicators when selecting appropriate data products. In order to minimize the uncertainty of the quantitative assessment of accuracy, and fully understand the accuracy difference under different user needs, the accuracy assessment indexes were calculated based on the traditional confusion matrix, the adjusted confusion matrix and the confusion matrix weighted for specific user needs, respectively. The results showed that: Firstly, the GLC_FCS30-2015 product has the highest overall accuracy in the global and US regions. Meanwhile, the accuracy index value weighted for the different needs of users was higher than the traditional accuracy index based on the confusion matrix. And for different user needs, the accuracy of the products showed obvious differences.

The main innovations of this study contain:

(1) A rigorous classification system conversion rule based on the semantic similarity measurement was constructed to solve the problem of incompatibility of the different classification system of global land cover products. This rule can reduce the errors introduced by humans in the tranditional classification system conversion, and reflect the consistency and differences between land cover priducts more accurately.

(2) An adjusted confusion matrix was proposed by introducing the area proportion of each land cover type in the product to reduce the uncertainty of quantitative accuracy assessment, and a weighted accuracy assessment strategy is proposed to analyze the difference in accuracy under different user needs. 

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