Estimating Aboveground Biomass of Mangrove Forests in Indonesia Using Spatial Attention Coupled Bayesian Aggregator

Mangroves play a crucial part in the worldwide blue carbon cycle because they store a lot of carbon in their biomass and soil. Accurate estimation of aboveground biomass (AGB) is essential for quantifying carbon stocks and understanding ecological responses to climate and human disturbances. However, regional-scale AGB mapping remains difficult due to fragmented mangrove distributions, limited field data, and cross-site heterogeneity. To address these challenges, we propose a Spatial Attention Coupled Bayesian Aggregator (SAC-BA), which integrates field measurements with multi-source remote sensing (Landsat 8, Sentinel-1), terrain data, and climate variables using advanced ensemble learning. Four machine learning models (Random Forest (RF), Cubist, Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost)) were first trained, and their outputs were fused using Bayesian model averaging with spatial attention weights and constraints based on Local Indicators of Spatial Association (LISAs), which identify spatial clusters (e.g., high–high, low–low) to improve accuracy and spatial coherence. SAC-BA achieved the highest performance (coefficient of determination (R2) = 0.82, root mean square error = 29.90 Mg/ha), outperforming all individual models and traditional BMA. The resulting 30-m AGB map of Indonesian mangroves in 2017 estimated a total of 217.17 × 106 Mg, with a mean of 103.20 Mg/ha. The predicted AGB map effectively captured spatial variability, reduced noise at ecological boundaries, and maintained high confidence predictions in core mangrove zones. These results highlight the advantages of incorporating spatial structure and uncertainty into ensemble modeling. SAC-BA provides a reliable and transferable framework for regional AGB estimation, supporting improved carbon assessment and mangrove conservation efforts.