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A GEDI-Sentinel-2 integration framework for wall-to-wall retrieval of forest overstory plant area index

文献类型：期刊论文

作者Jia, Duo3,4; Bo, Yanchen1,2; Wang, Cangjiao5; Pang, Yong5
刊名AGRICULTURAL AND FOREST METEOROLOGY
出版日期2026-05-01
卷号382页码:111117
关键词GEDI Overstory PAI Sentinel-2 Forests
ISSN号0168-1923
DOI10.1016/j.agrformet.2026.111117
产权排序1
文献子类Article
英文摘要Accurate estimation of forest overstory leaf area index (LAI) plays a critical role in carbon cycle modeling and climate change studies. Current large-scale overstory LAI products, which are primarily derived from multi-angle observations, are incompatible with moderate-to-high-resolution multispectral satellites such as Sentinel-2 and Landsat due to their fixed observation geometries. This limitation has hindered the development of large-scale forest overstory LAI products at moderate-to-high spatial resolutions. Although integrating vertical structure measurements from the Global Ecosystem Dynamics Investigation (GEDI) with multispectral data offers a potential solution, significant challenges remain related to spatiotemporal inconsistencies, including spatial mismatches and asynchronous acquisition timing, which limit the characterization of seasonal variation. To overcome these challenges, we developed an innovative GEDI-Sentinel-2 integration framework for wall-to-wall retrieval of forest overstory plant area index (PAI) at Sentinel-2 ' s spatiotemporal resolution. The proposed framework incorporates a vegetation parameter-based physical constraint approach to reduce spatiotemporal inconsistencies and establishes a physically-based nonlinear mapping between GEDI-derived overstory PAI and Sentinel-2 observations. This relationship is parameterized using heterogeneous ensemble regression to enable temporal estimation of overstory PAI. Validation across diverse forest types demonstrated robust performance, with relative mean absolute errors (rMAE) of 14.98-28.10% against digital hemispherical photography (DHP) references and 20.89-38.44% against airborne lidar references, while effectively capturing seasonal overstory PAI dynamics. Compared with integration approaches that do not account for GEDI-Sentinel-2 spatiotemporal inconsistencies, the proposed physical constraint approach substantially improved accuracy, reducing rMAE by 9.00-44.65% (DHP) and 20.32-30.30% (airborne lidar). With GEDI's operational timeline extended through 2031, this framework provides a reliable long-term solution for large-scale monitoring of forest overstory PAI at moderate-to-high spatial resolutions.
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WOS关键词TROPICAL RAIN-FORESTS ; LEAF-AREA ; UNDERSTORY VEGETATION ; GLOBAL PRODUCTS ; REFLECTANCE ; CANOPY ; LAI ; SENTINEL-2 ; LANDSAT ; LIDAR
WOS研究方向Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001716250800001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/221267]  
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Bo, Yanchen
作者单位1.Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China;
2.Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China;
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Natl Ecosyst Sci Data Ctr, Beijing 100101, Peoples R China;
5.Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;
推荐引用方式
GB/T 7714		 Jia, Duo,Bo, Yanchen,Wang, Cangjiao,et al. A GEDI-Sentinel-2 integration framework for wall-to-wall retrieval of forest overstory plant area index[J]. AGRICULTURAL AND FOREST METEOROLOGY,2026,382:111117.
APA Jia, Duo,Bo, Yanchen,Wang, Cangjiao,&Pang, Yong.(2026).A GEDI-Sentinel-2 integration framework for wall-to-wall retrieval of forest overstory plant area index.AGRICULTURAL AND FOREST METEOROLOGY,382,111117.
MLA Jia, Duo,et al."A GEDI-Sentinel-2 integration framework for wall-to-wall retrieval of forest overstory plant area index".AGRICULTURAL AND FOREST METEOROLOGY 382(2026):111117.

入库方式： OAI收割

来源：地理科学与资源研究所

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