A 1 km gridded dataset of Northern Hemisphere permafrost active layer thickness (2000–2024)

Active layer thickness (ALT) is a key indicator of permafrost change, with important implications for soil hydrothermal conditions, carbon feedbacks, ecosystem processes, and cold-region infrastructure. However, hemispheric-scale ALT mapping remains challenging because field observations are sparse and unevenly distributed, while thaw depth is controlled by complex and nonlinear environmental interactions. Here, we developed a LightGBM–XGBoost ensemble framework to reconstruct annual ALT on a 1 km grid across the Northern Hemisphere permafrost region from 2000 to 2024. Spatial block cross-validation yielded an R 2 of 0.720 and an RMSE of 39.47 cm, while complementary leave-one-site-out cross-validation achieved an R 2 of 0.780 and an RMSE of 34.98 cm. Temporally blocked validation using all withheld observations from 2021–2024 achieved an R 2 of 0.693 and an RMSE of 26.36 cm, improving to an R 2 of 0.826 and an RMSE of 19.74 cm when evaluation was restricted to previously observed sites. Across 60 long-term monitoring sites, the observed and reconstructed mean ALT series showed close temporal agreement ( r =0.954), although site-specific year-to-year variability was substantially smoothed, with predicted detrended variability reaching only about 64 % of the observed amplitude. The reconstructed ALT showed an overall long-term increase during 2000–2024, together with pronounced geographic and ecological heterogeneity. Annual area-of-applicability layers and site-resampling uncertainty estimates are provided to characterize environmental extrapolation and sensitivity to monitoring-network composition. Validation performance was not uniform across environmental and ALT conditions, with substantially larger errors for deep-thaw conditions and sparsely represented settings. The dataset is therefore most suitable for regional- to hemispheric-scale analyses of spatial ALT patterns and long-term permafrost change, while local-scale variability, deep-thaw conditions, and extrapolated areas should be interpreted with caution. The dataset is archived and freely available at https://doi.org/10.5281/zenodo.23227837 (Wei and Chen, 2026).