Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks
文献类型：期刊论文
| 作者 | Li, Lianfa1,2 ; Wu, Jiajie1,2
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| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2021-03-01 |
| 卷号 | 254页码:22 |
| 关键词 | OMI-NO2 columns Imputation of missing values Full residual deep network Bagging Ground-level NO2 estimation Traffic and land-use variables Uncertainty |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2020.112257 |
| 通讯作者 | Li, Lianfa(lilf@lreis.ac.cn) |
| 英文摘要 | Compared with the limited capability of ground-level monitoring, remote sensing provides useful image data at a moderate or high spatial or temporal resolution with global coverage for monitoring of air pollutants, e.g., aerosol optical depth (AOD) observations from the MODIS for fine particulate matter (PM2.5) and Ozone Monitoring Instrument (OMI) nitrogen dioxide (NO2) vertical columns for ground-level NO2 concentration. However, the extensive nonrandom missingness of OMI-NO2 data (e.g., an approximate per-pixel missing proportion of 59% for mainland China in 2015) due to cloud contamination or high reflectance limits applicability of these data in estimation of ground-level NO2. This paper proposes the use of a full residual deep learning method to impute missing satellite-borne NO2 data (OMI-NO2) and to estimate (map) ground-level NO2 with uncertainty (coefficient of variation) at a high spatial (1 x 1 km(2)) and temporal (daily) resolution. For the large study region (mainland China except Hainan Province), the presented method achieved robust performance with a stable learning efficiency (mean test R-2: 0.98 with a small standard deviation of 0.01; mean test RMSE: 0.42 x 10(15) molecules/cm(2)) for imputation of OMI-NO2. In the model, the coordinates and elevation were used to capture the spatial variability of the OMI-NO2 columns, and fused meteorological grid data and planetary boundary layer height and ozone data from GEOS-FP were used to capture spatiotemporal variability of OMI-NO2. The evaluation with ground in situ NO2 measurements showed considerable contribution of the complete (raw observed and imputed) OMI-NO2 columns, meteorology and traffic variables to inference of ground-level NO2 (test R-2: 0.82; test RMSE: 8.80 mu g/m(3)). The complete grids of OMI-NO2 columns showed natural and smooth spatial transitions between the raw observed and imputed values. The surfaces of predicted NO2 concentration not only showed consistent distributions with OMI-NO2 at a regional and temporal scale, but also presented local spatial gradients of ground-level NO2. OMI-NO2 can be downscaled and imputed to be used as an important predictor to improve the estimation of high-resolution ground-level NO2. The reliable estimates of ground-level NO2 concentration with uncertainty can reduce the bias in estimates of NO2 exposure and subsequently evaluations of its health effects. |
| WOS关键词 | FINE PARTICULATE MATTER ; LAND-USE REGRESSION ; NITROGEN-DIOXIDE ; AIR-POLLUTION ; RELATIVE-HUMIDITY ; HUMAN HEALTH ; AMBIENT AIR ; PM2.5 ; OZONE ; EXPOSURE |
| 资助项目 | National Natural Science Foundation of China[41471376] ; National Natural Science Foundation of China[42071369] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040501] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000609166200001 |
| 出版者 | ELSEVIER SCIENCE INC |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/136275]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Lianfa |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Datun Rd, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Lianfa,Wu, Jiajie. Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks[J]. REMOTE SENSING OF ENVIRONMENT,2021,254:22. |
| APA | Li, Lianfa,&Wu, Jiajie.(2021).Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks.REMOTE SENSING OF ENVIRONMENT,254,22. |
| MLA | Li, Lianfa,et al."Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks".REMOTE SENSING OF ENVIRONMENT 254(2021):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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