Work

Evaluating modeling approaches for estimating net ecosystem exchange in Canadian peatlands

Year: 2026

Type: article

Abstract: Peatlands are globally significant carbon reservoirs, yet models for peatland carbon cycling are often limited to the site level. To enable the prediction of carbon dioxide (CO 2 ) fluxes in peatlands where no in situ measurements exist, machine learning algorithms must be trained on in situ CO 2 flux measurements from multiple sites. In this study, year-round eddy covariance (EC)-derived net ecosystem exchange (NEE) measurements and 32 hydroclimatic predictor variables were compiled for 21 Canadian peatland sites spanning seven ecoregions. A comprehensive feature selection workflow carried out on the predictor variables (features) identified that model performance stabilized at four features, which were: evapotranspiration, burn area index, normalized difference water index, and modeled soil moisture. Four machine learning algorithms: ElasticNet Regression (EN), Light Gradient-Boosting Machine (LGBM), Random Forest Regression (RF), and Support Vector Regression (SVR) were trained and evaluated using held-out test data. The best performing model was the LGBM model, which was then assessed for generalizability via a leave-one-ecoregion-out sensitivity analysis, which highlighted the necessity of ensuring predictor variables fall within the range of the training data before applying this model framework to additional sites. These findings offer a framework for regional scaling to improve Canada’s spatially explicit CO 2 emission estimates.

Source: Scientific Reports

DOI: 10.1038/s41598-026-67275-6