Articles | Volume 15, issue 12
Geosci. Model Dev., 15, 4709–4738, 2022
https://doi.org/10.5194/gmd-15-4709-2022
Geosci. Model Dev., 15, 4709–4738, 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 et al.

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

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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.