Satellite data-driven estimation of daily and 500 m net ecosystem exchange over China during 2003-2020
文献类型：期刊论文
| 作者 | Wang, Xian2; Zhang, Yongqiang2; Zhang, Xuanze2; He, Shaoyang2; Kong, Dongdong3; Tian, Jing2; Wei, Haoshan2; Wang, Longhao2; Quan, Yu1; Zheng, Yufeng1 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2025-12-15 |
| 卷号 | 331页码:115047 |
| 关键词 | Net ecosystem exchange Carbon sequestration Process-based modeling Remote sensing |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2025.115047 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The terrestrial biosphere plays a critical role in mitigating climate change by absorbing anthropogenic CO2. However, accurately quantifying the net ecosystem exchange (NEE), which is a key indicator for monitoring carbon sequestration of terrestrial ecosystems, remains a major challenge. Widely used products from large-scale ecosystem models and atmospheric inversions operate at coarse resolutions (0.25 degrees or greater), which hinders the ability to resolve the carbon dynamics of heterogeneous landscapes and poses a significant challenge to understanding the impact of climate and land-use changes. In this study, a remote sensing data-driven water-carbon coupling model with the incorporation of terrestrial carbon cycle processes, Penman-Monteith-Leuning Version 2 Carbon (PMLV2C), is developed for estimating daily NEE over China at a 500 m resolution. The parameters of PML-V2C model were well calibrated against observations from 41 eddy covariance (EC) flux tower sites across nine plant functional types (PFTs) over China, demonstrating a strong performance for daily NEE estimates (r = 0.71, RMSE = 1.85 g C m(-2) day(-1)). The model is only slightly degraded when compared with independent global FLUXNET data across 157 sites, demonstrating its robustness and transferability across diverse climates and biomes. Applying the model from 2003 to 2020, our product revealed a significant enhancement of China's terrestrial carbon sink with an increasing trend of 0.041 Tg C yr(-2) (p < 0.01). This enhancement was primarily driven by the increasing GPP from forests in Southern China, grasslands in Northern China, and croplands across the East and North China Plain. Our high-resolution, process-based NEE product driven by satellite data provides a new evaluation of the effectiveness of ecosystem restoration and land management policies, offering valuable insights for achieving national carbon neutrality goals. |
| URL标识 | 查看原文 |
| WOS关键词 | EDDY COVARIANCE MEASUREMENTS ; LAND CARBON SINK ; SOIL RESPIRATION ; TERRESTRIAL ECOSYSTEMS ; TEMPERATURE-DEPENDENCE ; FOREST ECOSYSTEMS ; ENERGY-BALANCE ; CLIMATE-CHANGE ; CO2 EXCHANGE ; MODEL |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001588780400001 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217577]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Zhang, Yongqiang; Zhang, Xuanze |
| 作者单位 | 1.Ordos Meteorol Bur, High Resolut Satellite Branch Ctr Ordos, Ordos 017004, Inner Mongolia, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China; 3.China Univ Geosci, Sch Environm Studies, Dept Atmospher Sci, Wuhan, Peoples R China; 4.CSIRO Oceans & Atmosphere, Private Bag 1, Aspendale, Vic 3195, Australia |
| 推荐引用方式 GB/T 7714 | Wang, Xian,Zhang, Yongqiang,Zhang, Xuanze,et al. Satellite data-driven estimation of daily and 500 m net ecosystem exchange over China during 2003-2020[J]. REMOTE SENSING OF ENVIRONMENT,2025,331:115047. |
| APA | Wang, Xian.,Zhang, Yongqiang.,Zhang, Xuanze.,He, Shaoyang.,Kong, Dongdong.,...&Wang, Yingping.(2025).Satellite data-driven estimation of daily and 500 m net ecosystem exchange over China during 2003-2020.REMOTE SENSING OF ENVIRONMENT,331,115047. |
| MLA | Wang, Xian,et al."Satellite data-driven estimation of daily and 500 m net ecosystem exchange over China during 2003-2020".REMOTE SENSING OF ENVIRONMENT 331(2025):115047. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。