PeatDepth-ML: a global map of peat depth predicted using machine learning

作者：Jade Skye, Joe R. Melton, Colin Goldblatt, Angela Gallego‐Sala, Michelle Garneau, R. Scott Winton · 发表于：Biogeosciences · 年份：2026 · DOI：10.5194/bg-23-2959-2026 · 被引用次数：1 · 研究领域：Peatlands and Wetlands Ecology、Coastal wetland ecosystem dynamics、Fire effects on ecosystems

Abstract. 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...