Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements
Geostationary satellites observe the earth surface and atmosphere with a short repeat time, thus, providing aerosol parameters with high temporal resolution, which contributes to the air quality monitoring. Due to the limited information content in satellite data, and the coupling between the signal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/4/980 |
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| author | Xingfeng Chen Limin Zhao Fengjie Zheng Jiaguo Li Lei Li Haonan Ding Kainan Zhang Shumin Liu Donghui Li Gerrit de Leeuw |
| author_facet | Xingfeng Chen Limin Zhao Fengjie Zheng Jiaguo Li Lei Li Haonan Ding Kainan Zhang Shumin Liu Donghui Li Gerrit de Leeuw |
| author_sort | Xingfeng Chen |
| collection | DOAJ |
| description | Geostationary satellites observe the earth surface and atmosphere with a short repeat time, thus, providing aerosol parameters with high temporal resolution, which contributes to the air quality monitoring. Due to the limited information content in satellite data, and the coupling between the signals received from the surface and the atmosphere, the accurate retrieval of multiple aerosol parameters over land is difficult. With the strategy of taking full advantage of satellite measurement information, here we propose a neural network AEROsol retrieval framework for geostationary satellite (NNAeroG), which can potentially be applied to different instruments to obtain various aerosol parameters. NNAeroG was applied to the Advanced Himawari Imager on Himawari-8 and the results were evaluated versus independent ground-based sun photometer reference data. The aerosol optical depth, Ångström exponent and fine mode fraction produced by the NNAeroG method are significantly better than the official JAXA aerosol products. With spectral bands selection, the use of thermal infrared bands is meaningful for aerosol retrieval. |
| first_indexed | 2024-03-09T21:08:13Z |
| format | Article |
| id | doaj.art-76f6cb41b5b34e97b7531f85806ce4e0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:08:13Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-76f6cb41b5b34e97b7531f85806ce4e02023-11-23T21:55:10ZengMDPI AGRemote Sensing2072-42922022-02-0114498010.3390/rs14040980Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral MeasurementsXingfeng Chen0Limin Zhao1Fengjie Zheng2Jiaguo Li3Lei Li4Haonan Ding5Kainan Zhang6Shumin Liu7Donghui Li8Gerrit de Leeuw9Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Space Information, Space Engineering University, Beijing 101416, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Severe Weather (LASW), Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Earth Sciences and Resources, Chang’an University, Xi’an 710054, ChinaSchool of Software, Jiangxi University of Science and Technology, Nanchang 330013, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaGeostationary satellites observe the earth surface and atmosphere with a short repeat time, thus, providing aerosol parameters with high temporal resolution, which contributes to the air quality monitoring. Due to the limited information content in satellite data, and the coupling between the signals received from the surface and the atmosphere, the accurate retrieval of multiple aerosol parameters over land is difficult. With the strategy of taking full advantage of satellite measurement information, here we propose a neural network AEROsol retrieval framework for geostationary satellite (NNAeroG), which can potentially be applied to different instruments to obtain various aerosol parameters. NNAeroG was applied to the Advanced Himawari Imager on Himawari-8 and the results were evaluated versus independent ground-based sun photometer reference data. The aerosol optical depth, Ångström exponent and fine mode fraction produced by the NNAeroG method are significantly better than the official JAXA aerosol products. With spectral bands selection, the use of thermal infrared bands is meaningful for aerosol retrieval.https://www.mdpi.com/2072-4292/14/4/980aerosolneural networkgeostationary satellitefine mode fractiontemporal |
| spellingShingle | Xingfeng Chen Limin Zhao Fengjie Zheng Jiaguo Li Lei Li Haonan Ding Kainan Zhang Shumin Liu Donghui Li Gerrit de Leeuw Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements Remote Sensing aerosol neural network geostationary satellite fine mode fraction temporal |
| title | Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements |
| title_full | Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements |
| title_fullStr | Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements |
| title_full_unstemmed | Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements |
| title_short | Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements |
| title_sort | neural network aerosol retrieval for geostationary satellite nnaerog based on temporal spatial and spectral measurements |
| topic | aerosol neural network geostationary satellite fine mode fraction temporal |
| url | https://www.mdpi.com/2072-4292/14/4/980 |
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