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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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Claim

Machine‑learning‑driven high‑accuracy spatial reconstruction of global wetlands

Research datakeyboard_double_arrow_right Dataset 27 Jul 2025Embargo end date: 27 Jul 2025Publisher:Zenodo
Authors: He, Baoshi;

doi: 10.5281/zenodo.16494911 , 10.5281/zenodo.18494396 , 10.5281/zenodo.16494910

Machine‑learning‑driven high‑accuracy spatial reconstruction of global wetlands

Abstract

Using a machine learning model (Random Forest) incorporating anthropogenic features, we reconstructed global wetland distributions under future SSP126, SSP245, SSP370, and SSP585 scenarios based on outputs from seven climate models (ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CESM2-WACCM, CMCC-ESM2, CNRM-CM6-1, and GFDL-ESM4). The spatial resolution is 0.25°, with global wetland distribution maps (in GeoTIFF format) generated every five years from 2015 to 2100, and annually from 2086 to 2100.

Related Organizations
Keywords

machine learning, wetland

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average