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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, and 9 and MODIS Terra surface reflectance products. Based on this framework a global 30 m resolution 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 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).
Chen et al. (Thu,) studied this question.