Integrating PolSAR and Optical Data for Forest Aboveground Biomass Estimation with an Interpretable Bayesian-Optimized XGBoost Model
文献类型：期刊论文
| 作者 | Zhou, Xinshao4; Wang, Zhiqiang3; Wang, Zhaosheng1,2; Wang, Yonghong5; Li, Chaokui6; Huang, Tian5 |
| 刊名 | SUSTAINABILITY
 |
| 出版日期 | 2025-11-01 |
| 卷号 | 17期号:21页码:9749 |
| 关键词 | aboveground biomass Sentinel-2 ALOS-2 Bayesian optimization ensemble learning |
| DOI | 10.3390/su17219749 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | 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 R2 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. |
| URL标识 | 查看原文 |
| WOS关键词 | INDIVIDUAL TREES ; CARBON ; CHINA ; AIRBORNE ; VOLUME ; INDEX |
| WOS研究方向 | Science & Technology - Other Topics ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001613107300001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217750]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Wang, Zhaosheng |
| 作者单位 | 1.China Geol Surve, Kunming Nat Resources Comprehens Invest Ctr, Technol Innovat Ctr Nat Ecosyst Carbon Sequestrat, MNR, Kunming 650100, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 3.Hunan Key Lab Remote Sensing Monitoring Ecol Envir, Changsha 410004, Peoples R China; 4.Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China; 5.Hunan Prov Engn Res Ctr Dongting Lake Reg Ecol Dnv, Yiyang 413000, Peoples R China; 6.Hunan Univ Sci & Technol, Natl Local Joint Engn Lab Geospatial Informat Tech, Xiangtan 411100, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Xinshao,Wang, Zhiqiang,Wang, Zhaosheng,et al. Integrating PolSAR and Optical Data for Forest Aboveground Biomass Estimation with an Interpretable Bayesian-Optimized XGBoost Model[J]. SUSTAINABILITY,2025,17(21):9749. |
| APA | Zhou, Xinshao,Wang, Zhiqiang,Wang, Zhaosheng,Wang, Yonghong,Li, Chaokui,&Huang, Tian.(2025).Integrating PolSAR and Optical Data for Forest Aboveground Biomass Estimation with an Interpretable Bayesian-Optimized XGBoost Model.SUSTAINABILITY,17(21),9749. |
| MLA | Zhou, Xinshao,et al."Integrating PolSAR and Optical Data for Forest Aboveground Biomass Estimation with an Interpretable Bayesian-Optimized XGBoost Model".SUSTAINABILITY 17.21(2025):9749. |
入库方式： OAI收割
来源：地理科学与资源研究所
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