Harmonizing satellite and ground NO2 observations in China: A multi-sensor framework for scenario-specific calibration
文献类型：期刊论文
| 作者 | Gu, Jianbin4,5; Liang, Xiaoxia3; Song, Shipeng2,4; Li, Yichen4,5; Chen, Liangfu4,5; Tao, Jinhua4,5; Tian, Yanfang1 |
| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 230页码:486-494 |
| 关键词 | Nitrogen dioxide (NO2) TROPOMI OMI Satellite-ground validation Air pollution extremes Scenario-specific calibration |
| ISSN号 | 0924-2716 |
| DOI | 10.1016/j.isprsjprs.2025.09.028 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | Satellite-based nitrogen dioxide (NO2) retrievals exhibit scenario-dependent discrepancies due to varying environmental conditions, yet systematic evaluations of their performance across heterogeneous regions remain limited. This study presents a comparative analysis of TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) NO2 products over China (2019-2023) using 1700+ ground-based monitoring stations through a tripartite framework: (1) evaluation of spatiotemporal consistency with ground observations, (2) analysis of long-term emission trends using deseasonalized data, and (3) scenario-specific validation across seasonal cycles and extreme pollution episodes. Results demonstrate TROPOMI's superior performance in capturing fine-scale pollution patterns, showing strong correlations with ground measurements in urban areas (e.g. Beijing: R = 0.81) and during extreme events (R = 0.97), while OMI systematically underestimates urban concentrations by 15 % (R = 0.72). Seasonal analysis reveals that TROPOMI and OMI data correlate much more strongly with ground-based measurements under stable winter conditions (R = 0.85 and 0.82, respectively) than in summer, when performance is affected by photochemical processes (R = 0.23 and 0.13, respectively). A unified error model integrating all analytical components is developed to identify the drivers of satellite-ground discrepancies. Applied to the Beijing's January 2022 episode, the model attributes the observed biases primarily to extreme pollution events (gamma = 0.68). Our results emphasize TROPOMI's superiority for real-time urban air quality management and OMI's utility for regional trend assessments. This work provides actionable insights for optimizing satellite-ground monitoring systems, supporting targeted emission control strategies under China's evolving atmospheric policies. |
| URL标识 | 查看原文 |
| WOS关键词 | TROPOSPHERIC NO2 ; MAX-DOAS ; IN-SITU ; OMI ; TROPOMI ; SURFACE ; INSTRUMENT ; IMPACT |
| WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001590405800001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217437]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Liangfu |
| 作者单位 | 1.Chinese Res Inst Environm Sci, State Key Lab Environm Criteria & Risk Assessment, Beijing 100012, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 3.Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100089, Peoples R China; 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 5.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing & Digital Earth, 20 Datun Rd, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Gu, Jianbin,Liang, Xiaoxia,Song, Shipeng,et al. Harmonizing satellite and ground NO2 observations in China: A multi-sensor framework for scenario-specific calibration[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2025,230:486-494. |
| APA | Gu, Jianbin.,Liang, Xiaoxia.,Song, Shipeng.,Li, Yichen.,Chen, Liangfu.,...&Tian, Yanfang.(2025).Harmonizing satellite and ground NO2 observations in China: A multi-sensor framework for scenario-specific calibration.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,230,486-494. |
| MLA | Gu, Jianbin,et al."Harmonizing satellite and ground NO2 observations in China: A multi-sensor framework for scenario-specific calibration".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 230(2025):486-494. |
入库方式： OAI收割
来源：地理科学与资源研究所
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