GLC_FCS10: a global 10-m land-cover dataset with a fine classification system from Sentinel-1 and Sentinel-2 time-series data in Google Earth Engine

Abstract. The continuous development of remote sensing techniques provides ample opportunities for high-resolution land-cover mapping. Although global 10-m land-cover products have made considerable progress over past few years, their simple classification system makes it difficult to meet the needs of diverse applications. In this work, we propose a hierarchical land-cover mapping framework to produce a novel global 10-m land-cover dataset with a fine classification system (called GLC_FCS10) using Sentinel-1 and Sentinel-2 time-series observations from 2023. First, the globally distributed training samples are hierarchically obtained from multisource prior products after applying a series of refinements. Then, a combination of hierarchical land-cover mapping, local adaptive modeling, and multisource features is used to produce land-cover maps for each 5 × 5 geographical tile. Next, using 56121 globally distributed validation samples and a third-party validation dataset (LCMAP_Val), the GLC_FCS10 is assessed. The GLC_FCS10 achieves an overall accuracy of 83.16 % and a kappa coefficient of 0.789 globally and an overall accuracy of 85.09 % in the United States. Meanwhile, comparisons with five released 10- or 30-m land-cover products also demonstrate that GLC_FCS10 has higher accuracy and captures more diverse land-cover information than three of the released global 10-m land-cover products. In summary, the novel GLC_FCS10 land-cover maps can provide important support for high-resolution land-cover related research and applications. The GLC_FCS10 can be freely access via https://doi.org/10.5281/zenodo.14729665 (Liu and Zhang, 2025).