Volume IV-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 143-147, 2018
https://doi.org/10.5194/isprs-annals-IV-3-143-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 143-147, 2018
https://doi.org/10.5194/isprs-annals-IV-3-143-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Apr 2018

23 Apr 2018

REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM2.5 USING SATELLITE REMOTE SENSING

Tongwen Li1, Chengyue Zhang1, Huanfeng Shen1,4, Qiangqiang Yuan2,4, and Liangpei Zhang3,4 Tongwen Li et al.
  • 1School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
  • 2School of Geodesy and Geomatics, Wuhan University, Wuhan, China
  • 3The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
  • 4The Collaborative Innovation Center for Geospatial Technology, Wuhan, China

Keywords: PM2.5, Satellite remote sensing, Real-time, Seamless, Deep learning, Spatio-temporal fusion

Abstract. Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM2.5. However, the satellite-based monitoring of ground-level PM2.5 is still challenging. First, the previously used polar-orbiting satellite observations, which can be usually acquired only once per day, are hard to monitor PM2.5 in real time. Second, many data gaps exist in satellitederived PM2.5 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 PM2.5 in a deep learning architecture. On this basis, the satellite-derived PM2.5 in conjunction with ground PM2.5 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 PM2.5 distributions. The results demonstrate that Harawari-8 satellite-based deep learning model achieves a satisfactory performance (out-of-sample cross-validation R2 = 0.80, RMSE = 17.49 μg/m3) for the estimation of PM2.5. The missing data in satellite-derive PM2.5 are accurately recovered, with R2 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 PM2.5.