Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4709-2022
https://doi.org/10.5194/gmd-15-4709-2022
Model description paper
 | 
20 Jun 2022
Model description paper |  | 20 Jun 2022

A map of global peatland extent created using machine learning (Peat-ML)

Joe R. Melton, Ed Chan, Koreen Millard, Matthew Fortier, R. Scott Winton, Javier M. Martín-López, Hinsby Cadillo-Quiroz, Darren Kidd, and Louis V. Verchot

Viewed

Total article views: 5,958 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,163 1,704 91 5,958 63 71
  • HTML: 4,163
  • PDF: 1,704
  • XML: 91
  • Total: 5,958
  • BibTeX: 63
  • EndNote: 71
Views and downloads (calculated since 14 Feb 2022)
Cumulative views and downloads (calculated since 14 Feb 2022)

Viewed (geographical distribution)

Total article views: 5,958 (including HTML, PDF, and XML) Thereof 5,602 with geography defined and 356 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 Jun 2024
Download
Short summary
Peat-ML is a high-resolution global peatland extent map generated using machine learning techniques. Peatlands are important in the global carbon and water cycles, but their extent is poorly known. We generated Peat-ML using drivers of peatland formation including climate, soil, geomorphology, and vegetation data, and we train the model with regional peatland maps. Our accuracy estimation approaches suggest Peat-ML is of similar or higher quality than other available peatland mapping products.