Revealing remote sensing blind spots in human-driven land transitions: a systematic evaluation of state-of-the-art land use and land cover datasets on the Tibetan Plateau

For a long time, human-induced land transformation in sparsely populated areas has been a blind spot for satellite monitoring. The Tibetan Plateau (TP), with scattered human-managed land and vast wilderness, poses challenges for fine-scale monitoring due to frequent cloud cover and complex terrain. While some studies have evaluated the accuracy of land use and land cover (LULC) datasets on the TP, few have focused on a spatial scale-based, multi-category evaluation of human-managed land. Here, we used 16,800 samples within a stratified sampling design to evaluate 13 mainstream LULC datasets, focusing on their ability to capture human-managed land across spatial scales. Results show that the overall accuracy of these datasets ranges from 71.6% to 89.0%, with ESA WorldCover performing best. For built-up land, ESRI LC exhibits the least omission and less commission. For cropland, CCropland and GlobeLand30 perform well. While most datasets depict ecological land well, they tend to underestimate human activity at county and village scales. Specifically, CLCD and GISA barely reflect built-up land, and CACD captures around 50% of cropland. Our study underscores the need to enhance LULC datasets to more accurately reflect human impacts, providing a stronger foundation for evidence-based conservation policy and long-term sustainability.