Geographically weighted ensemble learning for forest aboveground carbon storage estimation with uncertainty quantification using sentinel-2 and spaceborne LiDAR data

Accurate estimation of forest aboveground carbon storage (AGC) is essential for understanding the global carbon cycle and carbon sequestration, yet conventional models often assume spatial stationarity, leading to systematic biases in heterogeneous landscapes. This study integrated Sentinel‑2 imagery and spaceborne LiDAR within a geographical random forest (GRF) framework coupled with quantile regression for uncertainty quantification and SHapley Additive exPlanations (SHAP) for interpretability, enabling confidence‑informed AGC mapping and a spatially explicit elucidation of variable importance. The fusion of LiDAR and Sentinel‑2 data consistently improved the accuracy over optical‑only data, and local models outperformed global models in both study areas. The fused GRF achieved R2 values of 0.710 in Jixi (medium effect) and 0.538 in Xintian (small effect). Uncertainty patterns differed markedly: bivariate local Moran’s I revealed a high‑AGC–low‑uncertainty cluster in Jixi versus a high‑AGC–high‑uncertainty cluster in Xintian. SHAP analysis revealed that textural features and vegetation indices dominated in Jixi, whereas spectral bands dominated in Xintian, with the dominant predictors shifting spatially. By integrating these methods into a unified workflow, the framework jointly addresses spatial nonstationarity, prediction uncertainty, and model interpretability, providing a methodological basis for AGC monitoring in complex and disturbed landscapes.