GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020

Abstract

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).