Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018JD028759 |
Estimating Regional Ground-Level PM2.5 Directly From Satellite Top-Of-Atmosphere Reflectance Using Deep Belief Networks | |
Shen, Huanfeng1,2,3; Li, Tongwen1; Yuan, Qiangqiang2,4; Zhang, Liangpei2,5 | |
2018-12-27 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
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ISSN | 2169-897X |
EISSN | 2169-8996 |
出版年 | 2018 |
卷号 | 123期号:24页码:13875-13886 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
英文摘要 | Almost all remote sensing atmospheric PM2.5 estimation methods need satellite aerosol optical depth (AOD) products, which are often retrieved from top-of-atmosphere (TOA) reflectance via an atmospheric radiative transfer model. Then, is it possible to estimate ground-level PM2.5 directly from satellite TOA reflectance without a physical model? In this study, this challenging work was achieved based on a machine learning model. Specifically, we established the relationship between PM2.5, satellite TOA reflectance, observation angles, and meteorological factors in a deep learning architecture (denoted as Ref-PM modeling). This relationship was trained with station PM2.5 measurements, and then the PM2.5 values of those locations without stations could be retrieved. Taking the Wuhan Urban Agglomeration as a case study, the results demonstrate that, compared with AOD-PM modeling, the Ref-PM modeling obtains a competitive performance, with sample-based cross-validated R-2 and root-mean-square error values of 0.87 and 9.89g/m(3), respectively. Also, the TOA-reflectance-derived PM2.5 has a finer resolution and a larger spatial coverage than the AOD-derived PM2.5. This work provides an alternative technique to estimate ground-level PM2.5, and may have the potential to promote the application in atmospheric environmental monitoring. |
英文关键词 | PM2 5 satellite remote sensing TOA reflectance deep learning |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000455876300015 |
WOS关键词 | AEROSOL OPTICAL-THICKNESS ; FINE PARTICULATE MATTER ; LONG-TERM EXPOSURE ; GLOBAL BURDEN ; MODIS AOD ; RETRIEVAL ; CHINA ; ALGORITHM ; DEPTH ; LAND |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/32742 |
专题 | 气候变化 |
作者单位 | 1.Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Hubei, Peoples R China; 2.Collaborat Innovat Ctr Geospatial Technol, Wuhan, Hubei, Peoples R China; 3.Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan, Hubei, Peoples R China; 4.Wuhan Univ, Sch Geodesy & Geomat, Wuhan, Hubei, Peoples R China; 5.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Hubei, Peoples R China |
推荐引用方式 GB/T 7714 | Shen, Huanfeng,Li, Tongwen,Yuan, Qiangqiang,et al. Estimating Regional Ground-Level PM2.5 Directly From Satellite Top-Of-Atmosphere Reflectance Using Deep Belief Networks[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2018,123(24):13875-13886. |
APA | Shen, Huanfeng,Li, Tongwen,Yuan, Qiangqiang,&Zhang, Liangpei.(2018).Estimating Regional Ground-Level PM2.5 Directly From Satellite Top-Of-Atmosphere Reflectance Using Deep Belief Networks.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,123(24),13875-13886. |
MLA | Shen, Huanfeng,et al."Estimating Regional Ground-Level PM2.5 Directly From Satellite Top-Of-Atmosphere Reflectance Using Deep Belief Networks".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 123.24(2018):13875-13886. |
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