Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data.
The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (...
Published in: | IEEE Transactions on Geoscience and Remote Sensing |
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Online Access: | https://doi.org/10.1109/TGRS.2018.2867000 https://repository.derby.ac.uk/download/c66e95f032df6c733aa37b1245896da7aee22f62703e8b2b89a4b9771103a03c/1689/license.txt |
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ftunivderby:oai:repository.derby.ac.uk:931xq 2023-06-11T04:03:03+02:00 Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. She, Lu Xue, Yong Yang, Xihua Leys, John Guang, Jie Che, Yahui Fan, Cheng Xie, Yanqing Li, Ying University of Derby Chinese Academy of Sciences New South Wales Office of Environment and Heritage 2018 application/octet-stream https://doi.org/10.1109/TGRS.2018.2867000 https://repository.derby.ac.uk/download/c66e95f032df6c733aa37b1245896da7aee22f62703e8b2b89a4b9771103a03c/1689/license.txt unknown IEEE https://repository.derby.ac.uk/item/931xq/joint-retrieval-of-aerosol-optical-depth-and-surface-reflectance-over-land-using-geostationary-satellite-data ISSN:0196-2892 ISSN:1558-0644 https://repository.derby.ac.uk/download/c66e95f032df6c733aa37b1245896da7aee22f62703e8b2b89a4b9771103a03c/1689/license.txt https://doi.org/10.1109/TGRS.2018.2867000 She, Lu, Xue, Yong, Yang, Xihua, Leys, John, Guang, Jie, Che, Yahui, Fan, Cheng, Xie, Yanqing and Li, Ying 2018. Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2018.2867000 Big Data analytics journal-article 2018 ftunivderby https://doi.org/10.1109/TGRS.2018.2867000 2023-05-08T13:34:13Z The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: R = 0.88, root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of ± 0.2· AODAERONET± 0.05. A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land. Article in Journal/Newspaper Aerosol Robotic Network UDORA - The University of Derby Online Research Archive IEEE Transactions on Geoscience and Remote Sensing 57 3 1489 1501 |
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Open Polar |
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UDORA - The University of Derby Online Research Archive |
op_collection_id |
ftunivderby |
language |
unknown |
topic |
Big Data analytics |
spellingShingle |
Big Data analytics She, Lu Xue, Yong Yang, Xihua Leys, John Guang, Jie Che, Yahui Fan, Cheng Xie, Yanqing Li, Ying Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. |
topic_facet |
Big Data analytics |
description |
The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: R = 0.88, root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of ± 0.2· AODAERONET± 0.05. A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land. |
author2 |
University of Derby Chinese Academy of Sciences New South Wales Office of Environment and Heritage |
format |
Article in Journal/Newspaper |
author |
She, Lu Xue, Yong Yang, Xihua Leys, John Guang, Jie Che, Yahui Fan, Cheng Xie, Yanqing Li, Ying |
author_facet |
She, Lu Xue, Yong Yang, Xihua Leys, John Guang, Jie Che, Yahui Fan, Cheng Xie, Yanqing Li, Ying |
author_sort |
She, Lu |
title |
Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. |
title_short |
Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. |
title_full |
Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. |
title_fullStr |
Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. |
title_full_unstemmed |
Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. |
title_sort |
joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. |
publisher |
IEEE |
publishDate |
2018 |
url |
https://doi.org/10.1109/TGRS.2018.2867000 https://repository.derby.ac.uk/download/c66e95f032df6c733aa37b1245896da7aee22f62703e8b2b89a4b9771103a03c/1689/license.txt |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
https://repository.derby.ac.uk/item/931xq/joint-retrieval-of-aerosol-optical-depth-and-surface-reflectance-over-land-using-geostationary-satellite-data ISSN:0196-2892 ISSN:1558-0644 https://repository.derby.ac.uk/download/c66e95f032df6c733aa37b1245896da7aee22f62703e8b2b89a4b9771103a03c/1689/license.txt https://doi.org/10.1109/TGRS.2018.2867000 She, Lu, Xue, Yong, Yang, Xihua, Leys, John, Guang, Jie, Che, Yahui, Fan, Cheng, Xie, Yanqing and Li, Ying 2018. Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2018.2867000 |
op_doi |
https://doi.org/10.1109/TGRS.2018.2867000 |
container_title |
IEEE Transactions on Geoscience and Remote Sensing |
container_volume |
57 |
container_issue |
3 |
container_start_page |
1489 |
op_container_end_page |
1501 |
_version_ |
1768392715182014464 |