Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China
The geostationary satellite Himawari-8, possessing the Advanced Himawari Imager (AHI), which features 16 spectral bands from the visible to infrared range, is suitable for aerosol observations. In this study, a new algorithm is introduced to retrieve aerosol optical depth (AOD) over land at a resolu...
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ftmdpi:oai:mdpi.com:/2072-4292/12/20/3425/ 2023-08-20T03:59:11+02:00 Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China Qingxin Wang Siwei Li Qiaolin Zeng Lin Sun Jie Yang Hao Lin agris 2020-10-19 application/pdf https://doi.org/10.3390/rs12203425 EN eng Multidisciplinary Digital Publishing Institute Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs12203425 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 20; Pages: 3425 AOD AHI single scattering albedo BRDF AERONET Text 2020 ftmdpi https://doi.org/10.3390/rs12203425 2023-08-01T00:18:29Z The geostationary satellite Himawari-8, possessing the Advanced Himawari Imager (AHI), which features 16 spectral bands from the visible to infrared range, is suitable for aerosol observations. In this study, a new algorithm is introduced to retrieve aerosol optical depth (AOD) over land at a resolution of 2 km from the AHI level 1 data. Considering the anisotropic effects of complex surface structures over land, Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) model parameters product (MCD19A3) is used to calculate the surface reflectance for Himawari-8’s view angle and band. In addition, daily BRDF model parameters are calculated in areas with dense vegetation, considering the rapid variation of surface reflectance caused by vegetation growth. Moreover, aerosol models are constructed based on long duration Aerosol Robotic Network (AERONET) single scattering albedo (SSA) values to stand for aerosol types in the retrieval algorithm. The new algorithm is applied to AHI images over Bohai Rim region from 2018 and is evaluated using the newest AERONET version 3 AOD measurements and the latest MODIS collection 6.1 AOD products. The AOD retrievals from the new algorithm show good agreement with the AERONET AOD measurements, with a correlation coefficient of 0.93 and root mean square error (RMSE) of 0.12. In addition, the new algorithm increases AOD retrievals and retrieval accuracy compared to the Japan Aerospace Exploration Agency (JAXA) aerosol products. The algorithm shows stable performance during different seasons and times, which makes it possible for use in climate or diurnal aerosol variation studies. Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 12 20 3425 |
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AOD AHI single scattering albedo BRDF AERONET |
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AOD AHI single scattering albedo BRDF AERONET Qingxin Wang Siwei Li Qiaolin Zeng Lin Sun Jie Yang Hao Lin Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China |
topic_facet |
AOD AHI single scattering albedo BRDF AERONET |
description |
The geostationary satellite Himawari-8, possessing the Advanced Himawari Imager (AHI), which features 16 spectral bands from the visible to infrared range, is suitable for aerosol observations. In this study, a new algorithm is introduced to retrieve aerosol optical depth (AOD) over land at a resolution of 2 km from the AHI level 1 data. Considering the anisotropic effects of complex surface structures over land, Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) model parameters product (MCD19A3) is used to calculate the surface reflectance for Himawari-8’s view angle and band. In addition, daily BRDF model parameters are calculated in areas with dense vegetation, considering the rapid variation of surface reflectance caused by vegetation growth. Moreover, aerosol models are constructed based on long duration Aerosol Robotic Network (AERONET) single scattering albedo (SSA) values to stand for aerosol types in the retrieval algorithm. The new algorithm is applied to AHI images over Bohai Rim region from 2018 and is evaluated using the newest AERONET version 3 AOD measurements and the latest MODIS collection 6.1 AOD products. The AOD retrievals from the new algorithm show good agreement with the AERONET AOD measurements, with a correlation coefficient of 0.93 and root mean square error (RMSE) of 0.12. In addition, the new algorithm increases AOD retrievals and retrieval accuracy compared to the Japan Aerospace Exploration Agency (JAXA) aerosol products. The algorithm shows stable performance during different seasons and times, which makes it possible for use in climate or diurnal aerosol variation studies. |
format |
Text |
author |
Qingxin Wang Siwei Li Qiaolin Zeng Lin Sun Jie Yang Hao Lin |
author_facet |
Qingxin Wang Siwei Li Qiaolin Zeng Lin Sun Jie Yang Hao Lin |
author_sort |
Qingxin Wang |
title |
Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China |
title_short |
Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China |
title_full |
Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China |
title_fullStr |
Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China |
title_full_unstemmed |
Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China |
title_sort |
retrieval and validation of aod from himawari-8 data over bohai rim region, china |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12203425 |
op_coverage |
agris |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing; Volume 12; Issue 20; Pages: 3425 |
op_relation |
Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs12203425 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs12203425 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
20 |
container_start_page |
3425 |
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1774719635722076160 |