Estimating Annual Wildfire-Related Potential Above-Ground Biomass Loss in Eastern Canadian Boreal Forests Using Multi-Source Remote Sensing and XGBoost

Wildfire impact assessment requires information on both burned areas and the biomass exposed within burned landscapes. We developed a field-calibrated, multi-source remote-sensing framework to estimate above-ground biomass (AGB) and quantify annual wildfire-related potential AGB exposure across the boreal forests of Quebec and Ontario, Canada, during 2018–2024. The dataset comprised 3725 plot-year AGB observations linked to optical, Sentinel-1 C-band, ALOS L-band synthetic aperture radar, environmental, and geographic predictors. Product-wise screening reduced the 91 candidate predictors to 28. An optimized extreme gradient boosting (XGBoost) model was evaluated using five-fold grouped cross-validation, with repeated observations from each plot assigned to a single fold. The model achieved an RMSE of 25.08 ± 0.36 t ha−1, an MAE of 20.89 ± 0.39 t ha−1, and an R2 of 0.53 ± 0.02. The full multi-source configuration outperformed all reduced-source and source-only configurations, while removing ALOS L-band SAR or environmental/geographic predictors produced among the largest performance declines. The model was applied to 9937 land-cover-stratified points within wildfire polygons using predictors from the year preceding each fire. Under the complete-loss assumption, cumulative potential AGB exposure was 269.20 Mt across 6.66 Mha of effective burned area, with a 95% bootstrap interval of 254.19–284.06 Mt reflecting finite-point sampling uncertainty and an area-weighted mean exposure intensity of 40.43 t ha−1. The 2023 fire season accounted for 206.44 Mt, representing 76.7% of cumulative exposure and 73.2% of effective burned area. Effective burned area and total potential exposure were strongly correlated (r = 0.99), whereas exposure intensity followed a distinct pattern and peaked in 2022 at 46.86 t ha−1. Thus, burned area was the primary correlate of regional potential biomass exposure, whereas exposure intensity reflected variation in pre-fire biomass among burned landscapes. These estimates represent potential exposure rather than measured combustion, mortality, or carbon emissions and demonstrate the value of integrating spatially explicit pre-fire AGB with wildfire perimeters.