Abstract. The Landsat series constitutes an unparalleled repository of multi-decadal Earth observations, serving as a cornerstone in global environmental monitoring. However, the inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses significant challenges to land monitoring over large geographical extents. In this study, we developed a full-chain processing framework for the multi-sensor data fusion of Landsat-5, 7, 8, 9 and MODIS Terra surface reflectance products. Based on this framework, a global, 30-m resolution, and daily Seamless Data Cube (SDC) of land surface reflectance was generated, spanning from 2000 to 2022. A thorough evaluation of the SDC was undertaken using a leave-one-out approach and a cross-comparison with NASA’s Harmonized Landsat and Sentinel-2 (HLS) products. The leave-one-out validation at 425 global test sites assessed the agreement between the SDC with actual Landsat surface reflectance values (not used as input), revealing an overall Mean Absolute Error (MAE) of 0.014 (the valid range of surface reflectance values is 0–1). The cross-comparison with the HLS products at 22 Military Grid Reference System (MGRS) tiles revealed an overall Mean Absolute Deviation (MAD) of 0.017 with L30 (Landsat-8-based 30-m HLS product) and a MAD of 0.021 with S30 (Sentinel-2-based 30-m HLS product). Moreover, experimental results underscore the advantages of employing the SDC for global land cover classification, achieving a sizable improvement in overall accuracy (2.4 %~11.3 %) over that obtained using Landsat composite and interpolated datasets. A web-based interface has been developed for researchers to freely access the SDC dataset, which is available at https://doi.org/10.12436/SDC30.26.20240506 (Chen et al., 2024).

中文翻译：

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由 Landsat-5、7、8、9 和 MODIS Terra 星座生成的全球 30 米无缝数据立方体（2000-2022 年）陆地表面反射率


摘要。 Landsat 系列构成了无与伦比的数十年地球观测资料库，是全球环境监测的基石。然而，Landsat数据由于重访间隔长、云层覆盖频繁而覆盖范围不一致，给大地理范围的土地监测带来了巨大挑战。在本研究中，我们开发了Landsat-5、7、8、9和MODIS Terra表面反射率产品的多传感器数据融合的全链处理框架。基于该框架，生成了 2000 年至 2022 年全球、30 米分辨率、每日的地表反射率无缝数据立方体 (SDC)。使用留一法对 SDC 进行了全面评估以及与 NASA 的协调 Landsat 和 Sentinel-2 (HLS) 产品的交叉比较。在 425 个全球测试站点进行的留一验证评估了 SDC 与实际 Landsat 表面反射率值（不用作输入）之间的一致性，显示总体平均绝对误差 (MAE) 为 0.014（表面反射率值的有效范围）为 0–1)。与 22 个军事网格参考系统 (MGRS) 块上的 HLS 产品的交叉比较显示，L30（基于 Landsat-8 的 30 米 HLS 产品）的整体平均绝对偏差 (MAD) 为 0.017，S30 的 MAD 为 0.021 （基于 Sentinel-2 的 30 米 HLS 产品）。此外，实验结果强调了采用 SDC 进行全球土地覆盖分类的优势，与使用 Landsat 合成和插值数据集获得的总体精度相比，总体精度有了相当大的提高（2.4%~11.3%）。已经开发了一个基于网络的界面，供研究人员自由访问 SDC 数据集，该数据集可在 https://doi.org/10.12436/SDC30.26.20240506 上获得（Chen 等人，2024）。, 2024).