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

Xinshao Zhou, Zhiqiang Wang, Zhaosheng Wang (), Yonghong Wang, Chaokui Li and Tian Huang
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Xinshao Zhou: College of Information and Electronic Engineering, Hunan City University, Yiyang 413000, China
Zhiqiang Wang: Hunan Key Laboratory of Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area, Changsha 410004, China
Zhaosheng Wang: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Yonghong Wang: Hunan Provincial Engineering Research Center of Dongting Lake Regional Ecological Dnviroment Intelligent Monitoring and Disaster Prevention and Mitigation Technology, Yiyang 413000, China
Chaokui Li: National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411100, China
Tian Huang: Hunan Provincial Engineering Research Center of Dongting Lake Regional Ecological Dnviroment Intelligent Monitoring and Disaster Prevention and Mitigation Technology, Yiyang 413000, China

Sustainability, 2025, vol. 17, issue 21, 1-19

Abstract: 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.

Keywords: aboveground biomass; Sentinel-2; ALOS-2; Bayesian optimization; ensemble learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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