Articles | Volume 15, issue 12
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-426', Juan Antonio Añel, 01 Mar 2022
    • AC1: 'Reply on CEC1', Joe Melton, 10 Mar 2022
  • RC1: 'Comment on gmd-2021-426', Anonymous Referee #1, 06 Apr 2022
  • RC2: 'Comment on gmd-2021-426', Anonymous Referee #2, 19 Apr 2022
  • AC2: 'Authors' reply to Reviewers (Julie Loisel and #2)', Joe Melton, 29 Apr 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Joe Melton on behalf of the Authors (04 May 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (06 May 2022) by David Lawrence
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.