Integrating PolSAR and Optical Data for Forest Aboveground Biomass Estimation with an Interpretable Bayesian-Optimized XGBoost Model

As a pivotal indicator in terrestrial ecosystems, forest aboveground biomass (AGB) reflects the capacity for carbon sequestration, the sustenance of biodiversity, and the provision of key ecosystem services. Precise quantification of AGB is therefore fundamental to evaluating forest quality and optimizing management strategies. However, there are bottlenecks in estimating forest AGB from a single data source, and traditional parameter optimization methods are not competent in complex environmental areas. This study proposes an interpretable Bayesian-optimized XGBoost model to improve forest AGB estimation, integrating polarimetric SAR (PolSAR) and optical remote-sensing data for forest AGB mapping in Quanzhou County, southern China. The results demonstrate that the proposed Bayesian-optimized XGBoost (BO-XGBoost) significantly outperforms traditional non-parametric models, achieving a final R 2 of 0.75 and root-mean-square error (RMSE) of 9.82 Mg/ha. The integration of PolSAR and optical data improved forest AGB estimation accuracy compared with using single data sources alone, reducing the RMSEs by 36.2% and 20.9%, respectively. Furthermore, the proposed method enhances the interpretability of the contributions made by remote-sensing features to forest AGB modeling, offering a new reference for future forest surveys and resource monitoring, which is particularly valuable for sustainable forestry development.