Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8
Due to the limitations in the number of satellites and the swath width of satellites (determined by the field of view and height of satellites), it is impossible to monitor global aerosol distribution using polar orbiting satellites at a high frequency. This limits the applicability of aerosol optic...
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Online Access: | http://hdl.handle.net/10545/624597 https://doi.org/10.1109/TGRS.2019.2944949 |
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ftunivderby:oai:derby.openrepository.com:10545/624597 2023-05-15T13:06:57+02:00 Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8 Xie, Yanqing Xue, Yong Guang, Jie Mei, Linlu She, Lu Li, Ying Che, Yahui Fan, Cheng China University of Mining and Technology, XuzhouChina State Key Laboratory of Remote Sensing Science University of Bremen, Bremen, Germany Ningxia University, Yinchuan, China University of Derby 2019-11-07 http://hdl.handle.net/10545/624597 https://doi.org/10.1109/TGRS.2019.2944949 en eng IEEE https://ieeexplore.ieee.org/abstract/document/8894178/keywords#keywords Xie, Y., Xue, Y., Guang, J., Mei, L., She, L., Li, Y., Che, Y. and Fan, C., (2019). 'Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8'. IEEE Transactions on Geoscience and Remote Sensing, pp. 1-12. 01962892 doi:10.1109/TGRS.2019.2944949 http://hdl.handle.net/10545/624597 IEEE Transactions on Geoscience and Remote Sensing Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ CC-BY Big Data Aerosols Monitoring Geostationary satellites Remote sensing Earth Optical sensors Article 2019 ftunivderby https://doi.org/10.1109/TGRS.2019.2944949 2020-09-04T06:43:55Z Due to the limitations in the number of satellites and the swath width of satellites (determined by the field of view and height of satellites), it is impossible to monitor global aerosol distribution using polar orbiting satellites at a high frequency. This limits the applicability of aerosol optical depth (AOD) data sets in many fields, such as atmospheric pollutant monitoring and climate change research, where a high-temporal data resolution may be required. Although geostationary satellites have a high-temporal resolution and an extensive observation range, three or more satellites are required to achieve global monitoring of aerosols. In this article, we obtain an hourly and global AOD data set by integrating AOD data sets from four geostationary weather satellites [Geostationary Operational Environmental Satellite (GOES-16), Meteosat Second Generation (MSG-1), MSG-4, and Himawari-8]. The integrated data set will expand the application range beyond the four individual AOD data sets. The integrated geostationary satellite AOD data sets from April to August 2018 were validated using Aerosol Robotic Network (AERONET) data. The data set results were validated against: the mean absolute error, mean bias error, relative mean bias, and root-mean-square error, and values obtained were 0.07, 0.01, 1.08, and 0.11, respectively. The ratio of the error of satellite retrieval within ±(0.05 + 0.2 x AODAERONET) is 0.69. The spatial coverage and accuracy of the MODIS/C61/AOD product released by NASA were also analyzed as a representative of polar orbit satellites. The analysis results show that the integrated AOD data set has similar accuracy to that of the MODIS/AOD data set and has higher temporal resolution and spatial coverage than the MODIS/AOD data set. N/A Article in Journal/Newspaper Aerosol Robotic Network UDORA - The University of Derby Online Research Archive IEEE Transactions on Geoscience and Remote Sensing 58 3 1538 1549 |
institution |
Open Polar |
collection |
UDORA - The University of Derby Online Research Archive |
op_collection_id |
ftunivderby |
language |
English |
topic |
Big Data Aerosols Monitoring Geostationary satellites Remote sensing Earth Optical sensors |
spellingShingle |
Big Data Aerosols Monitoring Geostationary satellites Remote sensing Earth Optical sensors Xie, Yanqing Xue, Yong Guang, Jie Mei, Linlu She, Lu Li, Ying Che, Yahui Fan, Cheng Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8 |
topic_facet |
Big Data Aerosols Monitoring Geostationary satellites Remote sensing Earth Optical sensors |
description |
Due to the limitations in the number of satellites and the swath width of satellites (determined by the field of view and height of satellites), it is impossible to monitor global aerosol distribution using polar orbiting satellites at a high frequency. This limits the applicability of aerosol optical depth (AOD) data sets in many fields, such as atmospheric pollutant monitoring and climate change research, where a high-temporal data resolution may be required. Although geostationary satellites have a high-temporal resolution and an extensive observation range, three or more satellites are required to achieve global monitoring of aerosols. In this article, we obtain an hourly and global AOD data set by integrating AOD data sets from four geostationary weather satellites [Geostationary Operational Environmental Satellite (GOES-16), Meteosat Second Generation (MSG-1), MSG-4, and Himawari-8]. The integrated data set will expand the application range beyond the four individual AOD data sets. The integrated geostationary satellite AOD data sets from April to August 2018 were validated using Aerosol Robotic Network (AERONET) data. The data set results were validated against: the mean absolute error, mean bias error, relative mean bias, and root-mean-square error, and values obtained were 0.07, 0.01, 1.08, and 0.11, respectively. The ratio of the error of satellite retrieval within ±(0.05 + 0.2 x AODAERONET) is 0.69. The spatial coverage and accuracy of the MODIS/C61/AOD product released by NASA were also analyzed as a representative of polar orbit satellites. The analysis results show that the integrated AOD data set has similar accuracy to that of the MODIS/AOD data set and has higher temporal resolution and spatial coverage than the MODIS/AOD data set. N/A |
author2 |
China University of Mining and Technology, XuzhouChina State Key Laboratory of Remote Sensing Science University of Bremen, Bremen, Germany Ningxia University, Yinchuan, China University of Derby |
format |
Article in Journal/Newspaper |
author |
Xie, Yanqing Xue, Yong Guang, Jie Mei, Linlu She, Lu Li, Ying Che, Yahui Fan, Cheng |
author_facet |
Xie, Yanqing Xue, Yong Guang, Jie Mei, Linlu She, Lu Li, Ying Che, Yahui Fan, Cheng |
author_sort |
Xie, Yanqing |
title |
Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8 |
title_short |
Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8 |
title_full |
Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8 |
title_fullStr |
Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8 |
title_full_unstemmed |
Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8 |
title_sort |
deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8 |
publisher |
IEEE |
publishDate |
2019 |
url |
http://hdl.handle.net/10545/624597 https://doi.org/10.1109/TGRS.2019.2944949 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
https://ieeexplore.ieee.org/abstract/document/8894178/keywords#keywords Xie, Y., Xue, Y., Guang, J., Mei, L., She, L., Li, Y., Che, Y. and Fan, C., (2019). 'Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8'. IEEE Transactions on Geoscience and Remote Sensing, pp. 1-12. 01962892 doi:10.1109/TGRS.2019.2944949 http://hdl.handle.net/10545/624597 IEEE Transactions on Geoscience and Remote Sensing |
op_rights |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1109/TGRS.2019.2944949 |
container_title |
IEEE Transactions on Geoscience and Remote Sensing |
container_volume |
58 |
container_issue |
3 |
container_start_page |
1538 |
op_container_end_page |
1549 |
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1766027859486310400 |