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

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

doi:https://doi.org/10.5194/gmd-15-4709-2022

Articles | Volume 15, issue 12

https://doi.org/10.5194/gmd-15-4709-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-15-4709-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Data sets

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 https://doi.org/10.5281/zenodo.5794336

Model code and software

Code for A map of global peatland extent created using machine learning (Peat-ML) Joe R. Melton, Ed Chan, and Matthew Fortier https://doi.org/10.5281/zenodo.6345309

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.