%0 Dataset
%T ARC-2024: 10m Resolution Land Cover Map of  Circumpolar Arctic in 2024
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/bd8018b1-ed52-48c3-9e7d-59a1799cb6d0
%W NCDC
%A Shuai Yanmin
%A Qu Ge
%A Huo Sihui
%A Ma Xianwei
%K Circumpolar Arctic;Land cover mapping;Time series classification;Tundra vegetation;Sentinel-2
%X Arctic land cover mapping faces significant challenges, primarily stemming from pronounced spatial heterogeneity, complex surface characteristics, and extreme environmental conditions. To address these challenges, this study developed the ARC-2024 Circumpolar Arctic land cover data product, which features 10-meter spatial resolution and encompasses diverse tundra vegetation types. ARC-2024 employs a specialized Arctic classification system comprising 10 categories: Low-shrub (LS), Dwarf-shrub (DS), Grass (GR), Small, low-growing forb (SF), Moss and Lichen (ML), Water (WA), Wetlands (WL), and Ice and Snow (IS), specifically designed to characterize the ecological diversity of Arctic environments. ARC-2024 is constructed based on a time-series matching classification approach that, through a partial phenological signal matching mechanism, effectively overcomes the traditional dependence on complete and equidistant time series, making it particularly suitable for large-scale classification tasks where cloud-free observations exhibit spatiotemporally uneven distribution.Validation results demonstrate that ARC-2024 exhibits exceptional classification performance, achieving an overall accuracy of 87.78% and a Kappa coefficient of 0.858, representing an average improvement of over 16% compared to existing products. Particularly noteworthy is the product's outstanding performance in identifying fine-grained tundra vegetation types, including low shrubs, dwarf shrubs, grasslands, and small forbs. These vegetation types are often overlooked in existing land cover products but receive focused attention in ARC-2024, achieving significant accuracy improvements.ARC-2024 demonstrates significant advantages in spatial heterogeneity identification, enabling fine-scale characterization of complex regional features such as shrub-herbaceous ecotones. This data product represents an important advancement in Arctic land cover mapping and can provide crucial data support for polar ecologi