Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods
文献类型：期刊论文
| 作者 | Wang, Huifang1; Wang, Min2; Jiang, Pan3; Ma, Fanshu1; Gao, Yanhu1; Gu, Xinchen4,5; Luan, Qingzu1 |
| 刊名 | ATMOSPHERE
 |
| 出版日期 | 2025-05-28 |
| 卷号 | 16期号:6页码:655 |
| 关键词 | FY-3D/4A AODHi-8 AHI Modis HGALSS machine learning algorithms daily monitoring interpolation and reconstruction |
| DOI | 10.3390/atmos16060655 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | The satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps in AOD products from satellites like Fengyun significantly hinder continuous, seamless environmental monitoring capabilities, posing challenges for the long-term analysis of atmospheric pollution trends, responses to sudden ecological events, and disaster management. This study aims to develop a high-precision method to fill spatial AOD missing values and generate daily full-coverage AOD products for the Beijing-Tianjin-Hebei region in 2021 by integrating multi-dimensional data, including meteorological models, multi-source remote sensing, surface conditions, and nighttime light parameters, and applying machine learning methods. A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R2) of 0.93. Seasonal evaluation further indicated that the model's simulation was best in winter. Variable importance analysis identified relative humidity (RH) as the most critical factor influencing model results. The reconstructed full-coverage AOD product exhibited a spatial distribution trend of significantly higher values in the southern plain areas compared to mountainous regions, consistent with the actual aerosol distribution patterns in the Beijing-Tianjin-Hebei area. Moreover, the product demonstrated overall smoothness and high accuracy. This research lays the foundation for establishing a long-term, 1 km resolution, daily spatially continuous AOD product for the Beijing-Tianjin-Hebei region and beyond, providing more robust data support for addressing regional and larger-scale environmental challenges. |
| URL标识 | 查看原文 |
| WOS关键词 | PARTICULATE MATTER ; AIR-QUALITY ; CLIMATE ; POLLUTION ; ATHENS ; HEALTH |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001515316600001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214553]  |
| 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 | Luan, Qingzu |
| 作者单位 | 1.Beijing Municipal Climate Ctr, Beijing 100089, Peoples R China; 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 3.Southwest Univ Sci & Technol, Sch Econ & Management, Mianyang 621010, Peoples R China; 4.Tianjin Univ, Sch Architectural Engn, Tianjin 300072, Peoples R China; 5.Inst Water Resources & Hydropower Res, Beijing 100044, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Huifang,Wang, Min,Jiang, Pan,et al. Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods[J]. ATMOSPHERE,2025,16(6):655. |
| APA | Wang, Huifang.,Wang, Min.,Jiang, Pan.,Ma, Fanshu.,Gao, Yanhu.,...&Luan, Qingzu.(2025).Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods.ATMOSPHERE,16(6),655. |
| MLA | Wang, Huifang,et al."Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods".ATMOSPHERE 16.6(2025):655. |
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。