土地覆盖和土地覆盖变化被定义为人类干扰、气候变化所产生的地球表面生物特征波动的演化过程。它对水文建模、气候建模和地球系统建模至关重要，在生物多样性保护和碳循环中也发挥着重要作用。由于开放卫星数据的免费访问和低成本云计算平台的出现，一系列全球或区域尺度的土地覆盖遥感制图研究取得了重大进展，逐步实现中低分辨率和单年份制图向高分辨率和年度动态制图的过渡。然而，由于合适验证数据集的缺失，致使土地覆盖产品之间的精度差异未知且年度动态土地覆盖图验证滞后。因此，本研究试图通过分层等面积随机抽样和目视解译的方法收集一套2020年全球土地覆盖验证数据集；应用2020年验证样本数据集对目前可用的精细分辨率土地覆盖产品进行面积和空间一致性的比较分析以及全方位的精度评估；基于Google Earth Engine平台研发长时序验证数据集收集的解译工具进行1985-2020年长时序验证数据集的收集；以1985-2020年全球30米长时序土地覆盖动态产品（GLC_FCS30D）为研究对象，从变化监测角度完成精度评估。得到如下结论：

（1）样本布设采用柯本气候、人口密度和全球景观格局为分层依据的抽样设计方案，在一定程度上降低了分布在均质区域的纯净地类样本数量，同时提高呈零散分布的复杂地类样本数量。基于Google Earth Engine平台开发的解译工具，通过目视解译和变化监测模型完成长时序土地覆盖验证数据集的收集。该长时序验证数据集共包含79,112个样本，基于二项式分布的单个类型样本量确定公式，所有土地覆盖类型的样本大小足以支撑在全球范围内进行精度评估。此外，其在空间分布上直观反映了全球实际的土地覆盖情况。

（2）全球精细分辨率土地覆盖产品因制图策略、数据源等差异导致他们之间精度和一致性方面差异显著。欧空局研发的土地覆盖产品（ESA WorldCover）在10米产品中取得了最高的总体精度（70.54%±9%），其次是清华大学生产的Finer Resolution Observation and Monitoring-Global Land Cover10（FROM-GLC10）（68.95%±8%）和ESRI 开发的土地覆盖产品（ESRI Land Cover）（58.90%±7%））；GLC_FCS30在30米产品中总体精度最高（72.55%±9%），其次是全球土地覆盖数据集（GlobeLand30）（69.96%±9%）和FROM-GLC30（66.30%±8%）。从面积一致性角度，ESRI Land Cover除外，其余五种产品之间的面积一致性大于85%，但六种产品在草地、灌木林、湿地和裸地的面积一致性方面存在较大差异。从空间一致性角度，10米和30米产品的完全不一致像素比例分别为23.58%和14.12%，这些不一致像素主要分布在过渡带、复杂地形区域、异质景观或混合土地覆盖类型区域。

（3）全球土地覆盖在过去35年间了显著变化，其中森林损毁、耕地增加以及不透水面扩张是主要的变化趋势。耕地、灌木和草地全球净变化量分别为37.24%、27.07%和13.31%。GLC_FCS30D产品I级类总体精度范围为77.58±0.29%~79.02±0.28%；II级类总体精度范围为65.09±0.33%~66.55±0.32%。GLC_FCS30D产品对时间序列精度变化具有显著的稳定性，在美国和欧盟两个区域的平均总体精度分别为79.50%(±0.50%)和81.91%(±0.09%)。针对土地覆盖变化的二值精度评估显示，在不同的研究时段，美国和欧盟不变区域的生产者精度和用户精度（92.84±0.42%和96.28±0.32%以及96.73±0.25%和92.36±0.36%）高于变化区域的生产者精度和用户精度（72.26±2.04%和56.62±2.00%以及52.86±2.04%和73.31±2.00%）。最后，使用验证数据集对各地类面积偏差进行估计，结果表明灌木丛和草地存在明显的面积偏差。

Land cover and land cover changes are defined as the dynamic process involving alterations in the biological characteristics of the Earth's surface due to human activities and climate variations. It is critical for hydrological modelling, climate modeling and earth system modeling, and also plays a vital role in biodiversity conservation and the global carbon cycle. Due to free access to open satellite data and the emergence of low-cost cloud computing platforms, there has been significant progress in global or regional-scale land cover mapping research. This progress has seen a shift from low-resolution and single-year mapping to high-resolution and annual dynamic mapping. However, the lack of suitable validation datasets has led to uncertainties in the accuracy of land cover products and a delay in validating annual dynamic land cover maps. Therefore, this study attempts to collect a comprehensive global dataset for global land cover validation dataset in 2020 through stratified equal-area random sampling design and visual interpretation. We conducted area and spatial consistency analyses on currently available fine-resolution land cover products, as well as a comparative accuracy assessment using the 2020 validation dataset. Then, we developed an interpretation tool for collecting a long-term validation dataset from 1985 to 2020 based on the Google Earth Engine platform. We also performed accuracy assessments from a change monitoring perspective taking the global 30-m long time series land cover dynamic product (GLC_FCS30D) from 1985 to 2020 as the research object. The following conclusions are obtained:

(1) The sample allocation adopts a stratified sampling design based on Köppen climate, population density and global landscape pattern. This approach effectively reduces the number of pure land samples distributed in a homogeneous area, while enhancing the samples number in complex regions. The interpretation tool developed based on the Google Earth Engine platform successfully gathered long-term land cover validation datasets through visual interpretation and the change monitoring model. The validation dataset contains a total of 79,112 samples. Based on the single-type sample size determination formula of the binomial distribution, the sample size of all land cover types is sufficient to support accuracy assessment on a global scale. And its spatial distribution intuitively reflects the distribution of actual global land cover.

(2) Global fine-resolution land cover products exhibit considerable variability in accuracy and consistency, due to the disparities in mapping strategies, data sources, and other factors. ESA WorldCover achieved the highest overall accuracy (of 70.54% ± 9%) among the global 10 m land cover products, followed by FROM-GLC10 (68.95% ± 8%) and ESRI Land Cover (58.90% ± 7%), and that GLC_FCS30 had the best overall accuracy (of 72.55% ± 9%) among the global 30 m land cover datasets, followed by GlobeLand30 (69.96% ± 9%) and FROM-GLC30 (66.30% ± 8%). From the perspective of area consistency, it can be found that the area consistencies between the five GLC products (except ESRI Land Cover) were greater than 85%, and that all six GLC products showed large discrepancies in area consistency for grassland, shrubland, wetland and bare land. From the perspective of spatial consistency, the totally inconsistent pixel proportions of the 10 m and 30 m GLC products were 23.58% and 14.12%, respectively, these inconsistent pixels were mainly distributed in transition zones and regions with complex terrains, heterogeneous landscapes, or mixed land-cover types.

(3) Global land cover has changed significantly in the past 35 years, with forest destruction, increase in cropland, and expansion of impervious surfaces being the main trends. The net changes in global land cover mainly transformed into net changes of 37.24%, 27.07% and 13.31% in cropland, shrubland and grassland, respectively. The overall accuracy range of GLC_FCS30D product in level I classification system is 77.58±0.29%~79.02±0.28% and the overall accuracy range in level II classification system is 65.09±0.33%~ 66.55±0.32%. The GLC_FCS30D product has significant stability to changes in time series accuracy, with average overall accuracies of 79.50% (±0.50%) and 81.91% (±0.09%) in the United States and the European Union, respectively. The binary accuracy assessment for land cover changes showed that producer’s accuracy and user’s accuracy in unchanged area (92.84±0.42% and 96.28±0.32% and 96.73±0.25% and 92.36±0.36% for the US and EU) higher than those in changed area (72.26±2.04% and 56.62±2.00% and 52.86±2.04% and 73.31±2.00%) during different study periods. Finally, the validation dataset was used to estimate the area bias of each land-cover types, and the results showed a distinct area bias between shrublands and grasslands.

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