PeatDepth-ML: A Global Map of Peat Depth Predicted using Machine Learning

Peatlands are major carbon stores that are sensitive to climate change and increasingly affected by human activity. Accurate assessment of carbon stocks and modelling of peatland responses to future climate scenarios requires robust information on peat depth. We developed PeatDepth-ML, a machine learning framework that predicts global peat depths using a comprehensive database of peat depth measurements for training and validation. Building on an existing framework for mapping peatland extent, we incorporated new environmental datasets relevant to peat formation, revised cross-validation procedures, and introduced a custom scoring metric to improve predictions of deep peat deposits. To evaluate model sensitivity to sampling bias inherent in the training data, we applied a bootstrapping approach. Model performance, assessed using a blocked leave-one-out approach, yielded a root mean square error of 70.1 ± 0.9 cm and a mean bias error of 2.1 ± 0.7 cm, performing as well as or better than previously published models. The global map produced by PeatDepth-ML predicts a median peat depth of 134 cm (IQR: 87 - 187) over areas with more than 30 cm of peat. Like other regression-based models, PeatDepth-ML tended to predict toward mean training depths. An area of applicability analysis suggests the model has good
applicability globally with the exception of some coastal and several mountainous regions like the Andes and the highlands of Borneo and New Guinea. Predictor selection was highly sensitive to training data subsets that arose from the bootstrapping approach, occasionally resulting in regional variations in accuracy. The bootstrapping approach and our area of applicability analysis thus clearly demonstrates the prime importance of quality training data in data-driven approaches like PeatDepth-ML.
Using our predicted peat depth map, together with peatland extent and literature-derived estimates of bulk density and organic carbon content, we estimate global peat carbon stocks at 327–373 Pg C, consistent with previous global estimates.

The PeatDepth-ML (PD-ML) output is a spatially continuous global peat depth (average depth of peatlands within each grid cell rather than average peat depth over the entire grid cell) map at a 5 arcmintue resolution.