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Explainable HybridEnsemble approach with golden jackal optimization for AGB estimation using multi-sensor remote sensing
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DOI：10.3389/frsen.2026.1804569.png)
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Abstract
En 中文
Accurate spatial measurement of aboveground biomass (AGB) is essential for assessing carbon stocks in the forest ecosystem. To enhance this estimation; integrating active and passive Earth Observation data with advanced machine learning techniques offers a promising approach. This study presents an integrated HybridEnsemble model with golden jackal optimization for AGB estimation and evaluates its predictive performance against individual base-learners; including categorical boosting (CatBoost); extreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM); and adaptive boosting (AdaBoost) via the synergistic application of Explainable Artificial Intelligence (XAI) and active and passive datasets. The findings revealed a clear performance ranking among these models; with the HybridEnsemble Golden Jackal Optimization (HGJO) model identified as the most effective; which yielded a correlation coefficient (R2) of 0.821 and a Relative Root Mean Square Error (rRMSE) of 16.30%. This performance was followed by CatBoost (R2 = 0.816; rRMSE = 16.51%); LightGBM (R2 = 0.804; rRMSE = 17.05%); XGBoost (R2 = 0.802; rRMSE = 17.13%); and AdaBoost (R2 = 0.731; rRMSE = 19.97%); with all comparisons reported at 95% confidence intervals. XAI revealed that predictors from optical sensors (passive) were strongly correlated with AGB and played a significant role in predicting AGB; while features derived from SAR (synthetic aperture radar; an active sensor); less influential; provided unique backscatter and context-specific insights that enhanced the model’s performance. Forecast results indicate an increasing trend. Additionally; the analysis revealed that future AGB accumulation in the subtropical forest of Hong Kong will be highly variable and strongly dependent on initial biomass levels; with high-biomass plots likely to see the greatest gains. However; spatial uncertainty in AGB predictions varied across the study area; with higher uncertainties observed in forested areas and lower uncertainties in urban areas. Overall; this study not only enhances understanding of optimized hybrid ensemble models for biomass prediction but also offers valuable insights for forecasting forest dynamics; supporting sustainable forest management and carbon stock monitoring globally.
Keywords:
CatBoost
Sentinel-2
aboveground biomass
ALOS-2 PARLSAR-2
golden jackal optimization
hybridensemble
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3.7
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