Mapping daily 1-km resolution XCO2 in China using deep learning and multi-source data
文献类型：期刊论文
| 作者 | Shao, Wei1,5; Yue, Tianxiang1,5; Zhang, Lili1,2; Tian, Wenjie1,2; Wang, Hao1,5; Zhou, Haowei3; Wu, Chenchen1,5; Zhang, Liqiang4 |
| 刊名 | RESOURCES CONSERVATION AND RECYCLING
 |
| 出版日期 | 2026-03-01 |
| 卷号 | 227页码:108755 |
| 关键词 | CO 2 concentration Deep learning Centroid trajectories Anthropogenic carbon emissions Data reconstruction |
| ISSN号 | 0921-3449 |
| DOI | 10.1016/j.resconrec.2025.108755 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | High-resolution spatiotemporal column-averaged CO2 (XCO2) data is essential for understanding anthropogenic carbon emissions, but current satellite limitations hinder detailed analysis. To address this, we develop the Spatial-Temporal Attention XCO2 Network (STAXN) to improve prediction accuracy by capturing spatialtemporal variability and multiscale influences of auxiliary variables. Monte Carlo validation demonstrates robust performance, with an RMSE of 0.90 ppm and an R2 of 0.97. Using this model, we generate a 1-km resolution daily XCO2 dataset for China (2015-2020) and analyze XCO2 anomaly patterns. Seasonal XCO2 anomalies peak in summer and winter, with nighttime light exhibiting strong positive effects (beta = 0.134, 0.107), and GPP exerting the most substantial adverse influence in winter (beta = -0.200). The centroid trajectories of XCO2 anomalies exhibit consistent seasonal shifts, shaped by regional disparities in carbon efficiency, industrial structure, and emission intensity. These findings offer valuable insights into China's carbon emission dynamics, informing policy and management strategies. |
| URL标识 | 查看原文 |
| WOS关键词 | ORBITING CARBON OBSERVATORY-2 ; GRAVITY MOVEMENT ; INCREASING CO2 ; DIOXIDE ; TEMPERATURE ; RETRIEVALS ; SATELLITE ; EMISSIONS ; ECOSYSTEM ; EXCHANGE |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001650424200003 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219748]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Yue, Tianxiang; Zhang, Lili |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; 2.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; 3.Univ Southampton, Sch Geog & Environm Sci, Southampton SO17 1BJ, England; 4.Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China 5.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Shao, Wei,Yue, Tianxiang,Zhang, Lili,et al. Mapping daily 1-km resolution XCO2 in China using deep learning and multi-source data[J]. RESOURCES CONSERVATION AND RECYCLING,2026,227:108755. |
| APA | Shao, Wei.,Yue, Tianxiang.,Zhang, Lili.,Tian, Wenjie.,Wang, Hao.,...&Zhang, Liqiang.(2026).Mapping daily 1-km resolution XCO2 in China using deep learning and multi-source data.RESOURCES CONSERVATION AND RECYCLING,227,108755. |
| MLA | Shao, Wei,et al."Mapping daily 1-km resolution XCO2 in China using deep learning and multi-source data".RESOURCES CONSERVATION AND RECYCLING 227(2026):108755. |
入库方式： OAI收割
来源：地理科学与资源研究所
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