Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images

Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as urban and sandy areas. Land Su...

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Published in:Remote Sensing
Main Authors: Lin Sun, Jing Wei, Muhammad Bilal, Xinpeng Tian, Chen Jia, Yamin Guo, Xueting Mi
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2015
Subjects:
AOD
Q
Online Access:https://doi.org/10.3390/rs8010023
https://doaj.org/article/aef2f36dbfd1438f8daae49d1da73ef1
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spelling ftdoajarticles:oai:doaj.org/article:aef2f36dbfd1438f8daae49d1da73ef1 2023-05-15T13:06:25+02:00 Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images Lin Sun Jing Wei Muhammad Bilal Xinpeng Tian Chen Jia Yamin Guo Xueting Mi 2015-12-01T00:00:00Z https://doi.org/10.3390/rs8010023 https://doaj.org/article/aef2f36dbfd1438f8daae49d1da73ef1 EN eng MDPI AG http://www.mdpi.com/2072-4292/8/1/23 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs8010023 https://doaj.org/article/aef2f36dbfd1438f8daae49d1da73ef1 Remote Sensing, Vol 8, Iss 1, p 23 (2015) AOD bright surfaces Landsat 8 OLI AERONET MOD04 Science Q article 2015 ftdoajarticles https://doi.org/10.3390/rs8010023 2022-12-31T16:10:43Z Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as urban and sandy areas. Land Surface Reflectance (LSR) is the key parameter that must be estimated accurately. Most current methods used to estimate AOD are applicable only in areas with low reflectance. It has historically been difficult to estimate the LSR for bright surfaces because of their complex structure and high reflectance. This paper provides a method for estimating LSR for AOD retrieval in bright areas, and the method is applied to AOD retrieval for Landsat 8 Operational Land Imager (OLI) images at 500 m spatial resolution. A LSR database was constructed with the MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09A1), and this database was also used to estimate the LSR of Landsat 8 OLI images. The AOD retrieved from the Landsat 8 OLI images was validated using the AOD measurements from four AErosol RObotic NETwork (AERONET) stations located in areas with bright surfaces. The MODIS AOD product (MOD04) was also compared with the retrieved AOD. The results demonstrate that the AOD retrieved with the new algorithm is highly consistent with the AOD derived from ground measurements, and its precision is better than that of MOD04 AOD products over bright areas. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 8 1 23
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic AOD
bright surfaces
Landsat 8 OLI
AERONET
MOD04
Science
Q
spellingShingle AOD
bright surfaces
Landsat 8 OLI
AERONET
MOD04
Science
Q
Lin Sun
Jing Wei
Muhammad Bilal
Xinpeng Tian
Chen Jia
Yamin Guo
Xueting Mi
Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images
topic_facet AOD
bright surfaces
Landsat 8 OLI
AERONET
MOD04
Science
Q
description Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as urban and sandy areas. Land Surface Reflectance (LSR) is the key parameter that must be estimated accurately. Most current methods used to estimate AOD are applicable only in areas with low reflectance. It has historically been difficult to estimate the LSR for bright surfaces because of their complex structure and high reflectance. This paper provides a method for estimating LSR for AOD retrieval in bright areas, and the method is applied to AOD retrieval for Landsat 8 Operational Land Imager (OLI) images at 500 m spatial resolution. A LSR database was constructed with the MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09A1), and this database was also used to estimate the LSR of Landsat 8 OLI images. The AOD retrieved from the Landsat 8 OLI images was validated using the AOD measurements from four AErosol RObotic NETwork (AERONET) stations located in areas with bright surfaces. The MODIS AOD product (MOD04) was also compared with the retrieved AOD. The results demonstrate that the AOD retrieved with the new algorithm is highly consistent with the AOD derived from ground measurements, and its precision is better than that of MOD04 AOD products over bright areas.
format Article in Journal/Newspaper
author Lin Sun
Jing Wei
Muhammad Bilal
Xinpeng Tian
Chen Jia
Yamin Guo
Xueting Mi
author_facet Lin Sun
Jing Wei
Muhammad Bilal
Xinpeng Tian
Chen Jia
Yamin Guo
Xueting Mi
author_sort Lin Sun
title Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images
title_short Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images
title_full Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images
title_fullStr Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images
title_full_unstemmed Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images
title_sort aerosol optical depth retrieval over bright areas using landsat 8 oli images
publisher MDPI AG
publishDate 2015
url https://doi.org/10.3390/rs8010023
https://doaj.org/article/aef2f36dbfd1438f8daae49d1da73ef1
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing, Vol 8, Iss 1, p 23 (2015)
op_relation http://www.mdpi.com/2072-4292/8/1/23
https://doaj.org/toc/2072-4292
2072-4292
doi:10.3390/rs8010023
https://doaj.org/article/aef2f36dbfd1438f8daae49d1da73ef1
op_doi https://doi.org/10.3390/rs8010023
container_title Remote Sensing
container_volume 8
container_issue 1
container_start_page 23
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