Multi-angle aerosol optical depth retrieval method based on improved surface reflectance
Retrieval of terrestrial aerosol optical depth (AOD) has been a challenge for satellite Earth observations, mainly due to the difficulty of estimating surface reflectance caused by land-atmosphere coupling. Current satellite AOD retrieval products have low spatial resolution under complex surface pr...
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ftcopernicus:oai:publications.copernicus.org:amtd114916 2023-12-24T10:07:37+01:00 Multi-angle aerosol optical depth retrieval method based on improved surface reflectance Chen, Lijuan Wang, Ren Fei, Ying Fang, Peng Zha, Yong Chen, Haishan 2023-11-22 application/pdf https://doi.org/10.5194/amt-2023-204 https://amt.copernicus.org/preprints/amt-2023-204/ eng eng doi:10.5194/amt-2023-204 https://amt.copernicus.org/preprints/amt-2023-204/ eISSN: 1867-8548 Text 2023 ftcopernicus https://doi.org/10.5194/amt-2023-204 2023-11-27T17:24:17Z Retrieval of terrestrial aerosol optical depth (AOD) has been a challenge for satellite Earth observations, mainly due to the difficulty of estimating surface reflectance caused by land-atmosphere coupling. Current satellite AOD retrieval products have low spatial resolution under complex surface processes. In this study, based on our previous studies of AOD retrieval, we further improved the estimation method of surface reflectance by establishing an error correction model and then obtained a more accurate AOD. A lookup table is constructed using the Second Simulation of Satellite Signal in the Solar Spectrum (6S) to obtain high-precision retrieval of AOD. The retrieval accuracy of the algorithm is verified by AERONET (Aerosol Robotic Network) observations. The results indicate that the retrieved AOD based on the improved method of this study has advantages in fewer missing AOD pixels and finer spatial resolution, as compared to the MODIS AOD product and our previous estimation method. Among the nine MISR angles, the optimal correlation coefficient (R) of retrieved AOD and observed AOD can reach 0.89. Root mean square error (RMSE) and relative mean bias (RMB) can reach a minimum values of 0.20 and 0.32, respectively. This study will help to further improve the accuracy of retrieving multi-angle AOD at large spatial scales and long time series. Text Aerosol Robotic Network Copernicus Publications: E-Journals |
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Open Polar |
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Copernicus Publications: E-Journals |
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English |
description |
Retrieval of terrestrial aerosol optical depth (AOD) has been a challenge for satellite Earth observations, mainly due to the difficulty of estimating surface reflectance caused by land-atmosphere coupling. Current satellite AOD retrieval products have low spatial resolution under complex surface processes. In this study, based on our previous studies of AOD retrieval, we further improved the estimation method of surface reflectance by establishing an error correction model and then obtained a more accurate AOD. A lookup table is constructed using the Second Simulation of Satellite Signal in the Solar Spectrum (6S) to obtain high-precision retrieval of AOD. The retrieval accuracy of the algorithm is verified by AERONET (Aerosol Robotic Network) observations. The results indicate that the retrieved AOD based on the improved method of this study has advantages in fewer missing AOD pixels and finer spatial resolution, as compared to the MODIS AOD product and our previous estimation method. Among the nine MISR angles, the optimal correlation coefficient (R) of retrieved AOD and observed AOD can reach 0.89. Root mean square error (RMSE) and relative mean bias (RMB) can reach a minimum values of 0.20 and 0.32, respectively. This study will help to further improve the accuracy of retrieving multi-angle AOD at large spatial scales and long time series. |
format |
Text |
author |
Chen, Lijuan Wang, Ren Fei, Ying Fang, Peng Zha, Yong Chen, Haishan |
spellingShingle |
Chen, Lijuan Wang, Ren Fei, Ying Fang, Peng Zha, Yong Chen, Haishan Multi-angle aerosol optical depth retrieval method based on improved surface reflectance |
author_facet |
Chen, Lijuan Wang, Ren Fei, Ying Fang, Peng Zha, Yong Chen, Haishan |
author_sort |
Chen, Lijuan |
title |
Multi-angle aerosol optical depth retrieval method based on improved surface reflectance |
title_short |
Multi-angle aerosol optical depth retrieval method based on improved surface reflectance |
title_full |
Multi-angle aerosol optical depth retrieval method based on improved surface reflectance |
title_fullStr |
Multi-angle aerosol optical depth retrieval method based on improved surface reflectance |
title_full_unstemmed |
Multi-angle aerosol optical depth retrieval method based on improved surface reflectance |
title_sort |
multi-angle aerosol optical depth retrieval method based on improved surface reflectance |
publishDate |
2023 |
url |
https://doi.org/10.5194/amt-2023-204 https://amt.copernicus.org/preprints/amt-2023-204/ |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
eISSN: 1867-8548 |
op_relation |
doi:10.5194/amt-2023-204 https://amt.copernicus.org/preprints/amt-2023-204/ |
op_doi |
https://doi.org/10.5194/amt-2023-204 |
_version_ |
1786207701515108352 |