REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM<sub>2.5</sub> USING SATELLITE REMOTE SENSING
Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM<sub>2.5</sub>. However, the satellite-based monitoring of ground-level PM<sub>2.5</sub> is still challenging. First, the previously used polar-orbiting satellite observa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-04-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-3/143/2018/isprs-annals-IV-3-143-2018.pdf |
| Summary: | Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM<sub>2.5</sub>. However, the satellite-based monitoring of ground-level PM<sub>2.5</sub> is still challenging. First, the previously used polar-orbiting satellite observations, which can be usually acquired only once per day, are hard to monitor PM<sub>2.5</sub> in real time. Second, many data gaps exist in satellitederived PM<sub>2.5</sub> due to the cloud contamination. In this paper, the hourly geostationary satellite (i.e., Harawari-8) observations were adopted for the real-time monitoring of PM<sub>2.5</sub> in a deep learning architecture. On this basis, the satellite-derived PM<sub>2.5</sub> in conjunction with ground PM<sub>2.5</sub> measurements are incorporated into a spatio-temporal fusion model to fill the data gaps. Using Wuhan Urban Agglomeration as an example, we have successfully derived the real-time and seamless PM<sub>2.5</sub> distributions. The results demonstrate that Harawari-8 satellite-based deep learning model achieves a satisfactory performance (out-of-sample cross-validation R<sup>2</sup> = 0.80, RMSE = 17.49 μg/m<sup>3</sup>) for the estimation of PM<sub>2.5</sub>. The missing data in satellite-derive PM<sub>2.5</sub> are accurately recovered, with R<sup>2</sup> between recoveries and ground measurements of 0.75. Overall, this study has inherently provided an effective strategy for the realtime and seamless monitoring of ground-level PM<sub>2.5</sub>. |
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| ISSN: | 2194-9042 2194-9050 |