Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4137-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4137-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters
Caiyi Jin
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079,
China
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079,
China
Collaborative Innovation Center of Geospatial Technology, Wuhan
430079, China
Key Laboratory of Geospace Environment and Geodesy (Ministry of
Education), Wuhan University, Wuhan 430079, China
Tongwen Li
CORRESPONDING AUTHOR
School of Geospatial Engineering and Science, Sun Yat-Sen University,
Zhuhai 519082, China
Yuan Wang
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079,
China
Liangpei Zhang
Collaborative Innovation Center of Geospatial Technology, Wuhan
430079, China
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
The semi-empirical physical approach derives PM2.5 with strong physical significance. However,...