A novel stratified random sampling global validation dataset in 2020—SRS_Val dataset

A 2020 global validation dataset was developed using stratified random sampling and multisource auxiliary datasets. The SRS_Val dataset had obvious advantages, including 1) employing the stratification combining Köppen climate groups, population density, and landscape heterogeneity effectively increased the sample size of different rare land-cover types and heterogeneous landscapes; 2) using the standardized classification system, derived from the UN-LCCS scheme performed better compatibility and harmonization with different GLC products with independent classification system; 3) combining the multisource remote sensing data (high-resolution imagery on Google Earth Pro, phenological curves, tree height, vegetation coverage, and terrain characteristics) and duplicate quality-controlling improved the reliability and quality of each validation sample in the SRS_Val dataset. We provide a CSV file of this validation dataset and a detailed description of its classification system.