Aboveground biomass mapping of Canada with SAR and optical satellite observations aided by active learning

National forest inventory (NFI) data has become an indispensable reference for model training and validation when estimating forest aboveground biomass (AGB) using satellite observations. However, obtaining statistically sufficient NFI data for model training is challenging for countries with vast land areas and extensive forest coverage like Canada. This study aims to directly upscale all available NFI data into high-resolution (30-m) spatially continuous AGB and explicit uncertainties maps across Canada’s treed land, using seasonal Sentinel 1&2 and yearly mosaic of L-band Synthetic Aperture Radar (SAR) observations. To address the poor performance with limited training dataset, failure to extrapolate prediction beyond the bound of the training dataset and cannot provide spatially explicit uncertainties that are inherent to the commonly used Random Forest (RF) model, the Gaussian Process Regression (GPR) model and active learning optimization was introduced. The models were trained using stratified 10-fold cross-validation (ST10CV) and optimized by Euclidean distance-based diversity with bidirectional active learning (EBD-BDAL) before extrapolated on the Google Earth Engine (GEE) platform. The GPR model optimized with EBD-BDAL estimated Canada’s 2020 treed land AGB at 40.68 ± 6.8 Pg, with managed and unmanaged forests accounting for 82 % and 18 %, respectively. Trees outside forest ecosystems account for 2 % (0.8 Pg AGB) of total AGB in Canada’s treed land, and there are 0.1134 Pg AGB within urban treed lands. The uncertainty analysis showed that the GPR model demonstrated superior extrapolation capability for high AGB forests while maintaining lower relative uncertainty. The ST10CV results showed that the GPR model performed better than RF with or without EBD-BDAL optimization. The proposed NFI upscaling framework based on the GPR model and EBD-BDAL optimization shows great potential for national AGB mapping based on limited NFI data and seasonal satellite observations.