Abstract. Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and automatic method of combining the advantages of spectral-generalization and automatic-sample-extraction strategies was proposed, and then an accurate global 30 m impervious-surface dynamic dataset (GISD30) for 1985 to 2020 was produced using time-series Landsat imagery on the Google Earth Engine cloud computing platform. Firstly, the global training samples and corresponding reflectance spectra were automatically derived from prior global 30 m land-cover products after employing the multitemporal compositing method and relative radiometric normalization. Then, spatiotemporal adaptive classification models, trained with the migrated reflectance spectra of impervious surfaces from 2020 and transferred pervious-surface samples in each epoch for every 5∘×5∘ geographical tile, were applied to map the impervious surface in each period. Furthermore, a spatiotemporal-consistency correction method was presented to minimize the effects of independent classification errors and improve the spatiotemporal consistency of impervious-surface dynamics. Our global 30 m impervious-surface dynamic model achieved an overall accuracy of 90.1 % and a kappa coefficient of 0.865 using 23 322 global time-series validation samples. Cross-comparisons with five existing global 30 m impervious-surface products further indicated that our GISD30 dynamic product achieved the best performance in capturing the spatial distributions and spatiotemporal dynamics of impervious surfaces in various impervious landscapes. The statistical results indicated that the global impervious surface has doubled in the past 35 years, from 5.116×105 km2 in 1985 to 10.871×105 km2 in 2020, and Asia saw the largest increase in impervious surface area compared to other continents, with a total increase of 2.946×105 km2. Therefore, it was concluded that our global 30 m impervious-surface dynamic dataset is an accurate and promising product and could provide vital support in monitoring regional or global urbanization as well as in related applications. The global 30 m impervious-surface dynamic dataset from 1985 to 2020 generated in this paper is free to access at https://doi.org/10.5281/zenodo.5220816 (Liu et al., 2021b).

Abstract. Wetlands, often called the “kidneys of the earth”, play an important role in maintaining ecological balance, conserving water resources, replenishing groundwater and controlling soil erosion. Wetland mapping is very challenging because of its complicated temporal dynamics and large spatial and spectral heterogeneity. An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping by combining an automatic sample extraction method, existing multi-sourced products, satellite time-series images and a stratified classification strategy. This approach allowed for the generation of the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal tidal wetland sub-categories (mangrove, salt marsh and tidal flats), which was developed using Google Earth Engine platform. We first combined existing multi-sourced global wetland products, expert knowledge, training sample refinement rules and visual interpretation to generate large and geographically distributed wetland training samples. Second, we integrated the Landsat reflectance time-series products and Sentinel-1 synthetic aperture radar (SAR) imagery to generate various water-level and phenological information to capture the complicated temporal dynamics and spectral heterogeneity of wetlands. Third, we applied a stratified classification strategy and the local adaptive random forest classification models to produce the wetland dataset with a fine classification system at each 5∘×5∘geographical tile in 2020. Lastly, GWL_FCS30, mosaicked by 961 5∘×5∘ regional wetland maps, was validated using 25 708 validation samples, which achieved an overall accuracy of 86.44 % and a kappa coefficient of 0.822. The cross-comparisons with other global wetland products demonstrated that the GWL_FCS30 dataset performed better in capturing the spatial patterns of wetlands and had significant advantages over the diversity of wetland sub-categories. The statistical analysis showed that the global wetland area reached 6.38 million km2, including 6.03 million km2 of inland wetlands and 0.35 million km2 of coastal tidal wetlands, approximately 72.96 % of which were distributed poleward of 40∘ N. Therefore, we can conclude that the proposed method is suitable for large-area wetland mapping and that the GWL_FCS30 dataset is an accurate wetland mapping product that has the potential to provide vital support for wetland management. The GWL_FCS30 dataset in 2020 is freely available at https://doi.org/10.5281/zenodo.7340516 (Liu et al., 2022).