ArticleScientific data2024
Global annual wetland dataset at 30 m with a fine classification system from 2000 to 2022.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 67 citations in OpenAlex.
- Article
- A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- An integrated global resource of wetland microbiomes linking environmental metadata, community profiles, and genome-resolved metabolic traits.Scientific data · 2026Article
- Dynamic maps of plastic-mulched farmlands in Northeast China from 1985 to 2025.Scientific data · 2026Article
- Hybrid spatiotemporal modeling of nutrient cycling in wetland ecosystems using advanced mapping techniques and machine learning approaches.Scientific reports · 2026Article
- Swamp-AI: a deep learning model for monitoring wetlands change across the globe.Scientific reports · 2026Article
- National coastal wetland mapping over the last four decades: An annual classification with high accuracy.Scientific data · 2026Article
- Article
- GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020.Scientific data · 2025Article
- Coarse land cover datasets bias Arctic-Boreal wetland methane budgets.Communications earth & environment · 2025Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wetlands play a key role in maintaining ecological balance and climate regulation. However, due to the complex and variable spectral characteristics of wetlands, there are no publicly available global 30-meter time-series wetland dynamic datasets at present. In this study, we present novel global 30 m annual wetland maps (GWL_FCS30D) using time-series Landsat imagery on the Google Earth Engine platform, covering the period of 2000-2022 and containing eight wetland subcategories. Specifically, we make full use of our prior globally distributed wetland training sample pool, and adopt the local adaptive classification and spatiotemporal consistency checking algorithm to generate annual wetland maps. The GWL_FCS30D maps were found to achieve an overall accuracy and Kappa coefficient of 86.95 ± 0.44% and 0.822, respectively, in 2020, and show great temporal variability in the United States and the European Union. We expect the dataset would provide vital support for wetland ecosystems protection and sustainable development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.