Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution
Aerosol optical depth (AOD) is a critical variable in estimating aerosol concentration in the atmosphere, evaluating severity of atmospheric pollution, and studying their impact on climate. With the assistance of the 6S radiative transfer model, we simulated apparent reflectancein relation to AOD in...
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ftmdpi:oai:mdpi.com:/2072-4292/6/2/1587/ 2023-08-20T03:59:11+02:00 Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution Junliang He Yong Zha Jiahua Zhang Jay Gao agris 2014-02-20 application/pdf https://doi.org/10.3390/rs6021587 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs6021587 https://creativecommons.org/licenses/by/3.0/ Remote Sensing; Volume 6; Issue 2; Pages: 1587-1604 aerosol indices air pollution 6S model MODIS Text 2014 ftmdpi https://doi.org/10.3390/rs6021587 2023-07-31T20:35:57Z Aerosol optical depth (AOD) is a critical variable in estimating aerosol concentration in the atmosphere, evaluating severity of atmospheric pollution, and studying their impact on climate. With the assistance of the 6S radiative transfer model, we simulated apparent reflectancein relation to AOD in each Moderate Resolution Imaging Spectroradiometer (MODIS) waveband in this study. The closeness of the relationship was used to identify the most and least sensitive MODIS wavebands. These two bands were then used to construct three aerosol indices (difference, ratio, and normalized difference) for estimating AOD quickly and effectively. The three indices were correlated, respectively, with in situ measured AOD at the Aerosol Robotic NETwork (AERONET) Lake Taihu, Beijing, and Xianghe stations. It is found that apparent reflectance of the blue waveband (band 3) is the most sensitive to AOD while the mid-infrared wavelength (band 7) is the least sensitive. The difference aerosol index is the most accurate in indicating aerosol-induced atmospheric pollution with a correlation coefficient of 0.585, 0.860, 0.685, and 0.333 at the Lake Taihu station, 0.721, 0.839, 0.795, and 0.629 at the Beijing station, and 0.778, 0.782, 0.837, and 0.643 at the Xianghe station in spring, summer, autumn and winter, respectively. It is concluded that the newly proposed difference aerosol index can be used effectively to study the level of aerosol-induced air pollution from MODIS satellite imagery with relative ease. Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 6 2 1587 1604 |
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English |
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aerosol indices air pollution 6S model MODIS |
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aerosol indices air pollution 6S model MODIS Junliang He Yong Zha Jiahua Zhang Jay Gao Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution |
topic_facet |
aerosol indices air pollution 6S model MODIS |
description |
Aerosol optical depth (AOD) is a critical variable in estimating aerosol concentration in the atmosphere, evaluating severity of atmospheric pollution, and studying their impact on climate. With the assistance of the 6S radiative transfer model, we simulated apparent reflectancein relation to AOD in each Moderate Resolution Imaging Spectroradiometer (MODIS) waveband in this study. The closeness of the relationship was used to identify the most and least sensitive MODIS wavebands. These two bands were then used to construct three aerosol indices (difference, ratio, and normalized difference) for estimating AOD quickly and effectively. The three indices were correlated, respectively, with in situ measured AOD at the Aerosol Robotic NETwork (AERONET) Lake Taihu, Beijing, and Xianghe stations. It is found that apparent reflectance of the blue waveband (band 3) is the most sensitive to AOD while the mid-infrared wavelength (band 7) is the least sensitive. The difference aerosol index is the most accurate in indicating aerosol-induced atmospheric pollution with a correlation coefficient of 0.585, 0.860, 0.685, and 0.333 at the Lake Taihu station, 0.721, 0.839, 0.795, and 0.629 at the Beijing station, and 0.778, 0.782, 0.837, and 0.643 at the Xianghe station in spring, summer, autumn and winter, respectively. It is concluded that the newly proposed difference aerosol index can be used effectively to study the level of aerosol-induced air pollution from MODIS satellite imagery with relative ease. |
format |
Text |
author |
Junliang He Yong Zha Jiahua Zhang Jay Gao |
author_facet |
Junliang He Yong Zha Jiahua Zhang Jay Gao |
author_sort |
Junliang He |
title |
Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution |
title_short |
Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution |
title_full |
Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution |
title_fullStr |
Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution |
title_full_unstemmed |
Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution |
title_sort |
aerosol indices derived from modis data for indicating aerosol-induced air pollution |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2014 |
url |
https://doi.org/10.3390/rs6021587 |
op_coverage |
agris |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing; Volume 6; Issue 2; Pages: 1587-1604 |
op_relation |
https://dx.doi.org/10.3390/rs6021587 |
op_rights |
https://creativecommons.org/licenses/by/3.0/ |
op_doi |
https://doi.org/10.3390/rs6021587 |
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Remote Sensing |
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6 |
container_issue |
2 |
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1587 |
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1604 |
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