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
Xiao Zhang, Liangyun Liu, Tingting Zhao, Wenhan Zhang, Linlin Guan, Ming Bai, Xidong Chen
Abstract
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 in 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 56 121 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 κ 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 accessed via https://doi.org/10.5281/zenodo.14729665 (Liu and Zhang, 2025).