Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data
High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low tempor...
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ftmdpi:oai:mdpi.com:/2072-4292/13/12/2376/ 2023-08-20T03:59:12+02:00 Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data Lijuan Chen Ying Fei Ren Wang Peng Fang Jiamei Han Yong Zha agris 2021-06-18 application/pdf https://doi.org/10.3390/rs13122376 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs13122376 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 12; Pages: 2376 aerosol optical depth high resolution spectral conversion GOCI MODIS Text 2021 ftmdpi https://doi.org/10.3390/rs13122376 2023-08-01T01:58:44Z High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low temporal resolution deficiency of polar orbiting satellite. In this study, we proposed an algorithm for retrieving high temporal resolution AOD using GOCI data and then applied the algorithm in the Yangtze River Delta, a typical region suffering severe air pollution issues. Based on Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by MODIS V5.2 algorithm and MODIS Bidirectional Reflectance Distribution Function (BRDF) data, after spectral conversion between MODIS and GOCI, the GOCI surface reflectance at different solar angles were obtained and used to retrieve AOD. Five indicators including correlation coefficient (R), significant level of the correlation (p value), mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) were employed to analyze the errors between the Aerosol Robotic Network (AERONET) observed AOD and the GOCI retrieved AOD. The results showed that the GOCI AOD retrieved by the continental aerosol look-up table was consistent with the AERONET AOD (R > 0.7, p ≤ 0.05). The highest R value of Taihu Station and Xuzhou CUMT Station are both 0.84 (8:30 a.m.); the minimum RMSE at Taihu and Xuzhou-CUMT stations were 0.2077 (11:30 a.m.) and 0.1937 (10:30 a.m.), respectively. Moreover, the results suggested that the greater the solar angle of the GOCI sensor, the higher the AOD retrieval accuracy, while the retrieved AOD at noon exhibited the largest error as assessed by MAE and MRE. We concluded that the inaccurate estimation of surface reflectance was the root cause of the retrieval errors. This study has implications in providing a deep understanding of the effects of solar angle changes on retrieving AOD using GOCI. Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 13 12 2376 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
aerosol optical depth high resolution spectral conversion GOCI MODIS |
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aerosol optical depth high resolution spectral conversion GOCI MODIS Lijuan Chen Ying Fei Ren Wang Peng Fang Jiamei Han Yong Zha Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data |
topic_facet |
aerosol optical depth high resolution spectral conversion GOCI MODIS |
description |
High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low temporal resolution deficiency of polar orbiting satellite. In this study, we proposed an algorithm for retrieving high temporal resolution AOD using GOCI data and then applied the algorithm in the Yangtze River Delta, a typical region suffering severe air pollution issues. Based on Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by MODIS V5.2 algorithm and MODIS Bidirectional Reflectance Distribution Function (BRDF) data, after spectral conversion between MODIS and GOCI, the GOCI surface reflectance at different solar angles were obtained and used to retrieve AOD. Five indicators including correlation coefficient (R), significant level of the correlation (p value), mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) were employed to analyze the errors between the Aerosol Robotic Network (AERONET) observed AOD and the GOCI retrieved AOD. The results showed that the GOCI AOD retrieved by the continental aerosol look-up table was consistent with the AERONET AOD (R > 0.7, p ≤ 0.05). The highest R value of Taihu Station and Xuzhou CUMT Station are both 0.84 (8:30 a.m.); the minimum RMSE at Taihu and Xuzhou-CUMT stations were 0.2077 (11:30 a.m.) and 0.1937 (10:30 a.m.), respectively. Moreover, the results suggested that the greater the solar angle of the GOCI sensor, the higher the AOD retrieval accuracy, while the retrieved AOD at noon exhibited the largest error as assessed by MAE and MRE. We concluded that the inaccurate estimation of surface reflectance was the root cause of the retrieval errors. This study has implications in providing a deep understanding of the effects of solar angle changes on retrieving AOD using GOCI. |
format |
Text |
author |
Lijuan Chen Ying Fei Ren Wang Peng Fang Jiamei Han Yong Zha |
author_facet |
Lijuan Chen Ying Fei Ren Wang Peng Fang Jiamei Han Yong Zha |
author_sort |
Lijuan Chen |
title |
Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data |
title_short |
Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data |
title_full |
Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data |
title_fullStr |
Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data |
title_full_unstemmed |
Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data |
title_sort |
retrieval of high temporal resolution aerosol optical depth using the goci remote sensing data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13122376 |
op_coverage |
agris |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing; Volume 13; Issue 12; Pages: 2376 |
op_relation |
https://dx.doi.org/10.3390/rs13122376 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13122376 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
12 |
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2376 |
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1774721447033307136 |