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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Published in:Remote Sensing
Main Authors: Junliang He, Yong Zha, Jiahua Zhang, Jay Gao
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2014
Subjects:
Online Access:https://doi.org/10.3390/rs6021587
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spelling 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
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic aerosol indices
air pollution
6S model
MODIS
spellingShingle 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
container_title Remote Sensing
container_volume 6
container_issue 2
container_start_page 1587
op_container_end_page 1604
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