Retrieval and validation of long-term aerosol optical depth from AVHRR data over China

Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study...

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Published in:International Journal of Digital Earth
Main Authors: Chunlin Jin, Yong Xue, Xingxing Jiang, Shuhui Wu, Yuxin Sun
Format: Article in Journal/Newspaper
Language:English
Published: Taylor & Francis Group 2022
Subjects:
Online Access:https://doi.org/10.1080/17538947.2022.2138590
https://doaj.org/article/c98af3f00e9d4f09a223c1541c9cf223
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spelling ftdoajarticles:oai:doaj.org/article:c98af3f00e9d4f09a223c1541c9cf223 2023-10-09T21:44:12+02:00 Retrieval and validation of long-term aerosol optical depth from AVHRR data over China Chunlin Jin Yong Xue Xingxing Jiang Shuhui Wu Yuxin Sun 2022-12-01T00:00:00Z https://doi.org/10.1080/17538947.2022.2138590 https://doaj.org/article/c98af3f00e9d4f09a223c1541c9cf223 EN eng Taylor & Francis Group http://dx.doi.org/10.1080/17538947.2022.2138590 https://doaj.org/toc/1753-8947 https://doaj.org/toc/1753-8955 1753-8947 1753-8955 doi:10.1080/17538947.2022.2138590 https://doaj.org/article/c98af3f00e9d4f09a223c1541c9cf223 International Journal of Digital Earth, Vol 15, Iss 1, Pp 1817-1832 (2022) long-term aod avhrr joint retrieval brdf shape Mathematical geography. Cartography GA1-1776 article 2022 ftdoajarticles https://doi.org/10.1080/17538947.2022.2138590 2023-09-24T00:35:56Z Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the ‘Beijing’, ‘Dalanzadgad’, ‘NCU_Taiwan’ and ‘Kanpur’ stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR ‘Deep Blue’ aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Merra ENVELOPE(12.615,12.615,65.816,65.816) International Journal of Digital Earth 15 1 1817 1832
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic long-term aod
avhrr
joint retrieval
brdf shape
Mathematical geography. Cartography
GA1-1776
spellingShingle long-term aod
avhrr
joint retrieval
brdf shape
Mathematical geography. Cartography
GA1-1776
Chunlin Jin
Yong Xue
Xingxing Jiang
Shuhui Wu
Yuxin Sun
Retrieval and validation of long-term aerosol optical depth from AVHRR data over China
topic_facet long-term aod
avhrr
joint retrieval
brdf shape
Mathematical geography. Cartography
GA1-1776
description Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the ‘Beijing’, ‘Dalanzadgad’, ‘NCU_Taiwan’ and ‘Kanpur’ stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR ‘Deep Blue’ aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively.
format Article in Journal/Newspaper
author Chunlin Jin
Yong Xue
Xingxing Jiang
Shuhui Wu
Yuxin Sun
author_facet Chunlin Jin
Yong Xue
Xingxing Jiang
Shuhui Wu
Yuxin Sun
author_sort Chunlin Jin
title Retrieval and validation of long-term aerosol optical depth from AVHRR data over China
title_short Retrieval and validation of long-term aerosol optical depth from AVHRR data over China
title_full Retrieval and validation of long-term aerosol optical depth from AVHRR data over China
title_fullStr Retrieval and validation of long-term aerosol optical depth from AVHRR data over China
title_full_unstemmed Retrieval and validation of long-term aerosol optical depth from AVHRR data over China
title_sort retrieval and validation of long-term aerosol optical depth from avhrr data over china
publisher Taylor & Francis Group
publishDate 2022
url https://doi.org/10.1080/17538947.2022.2138590
https://doaj.org/article/c98af3f00e9d4f09a223c1541c9cf223
long_lat ENVELOPE(12.615,12.615,65.816,65.816)
geographic Merra
geographic_facet Merra
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source International Journal of Digital Earth, Vol 15, Iss 1, Pp 1817-1832 (2022)
op_relation http://dx.doi.org/10.1080/17538947.2022.2138590
https://doaj.org/toc/1753-8947
https://doaj.org/toc/1753-8955
1753-8947
1753-8955
doi:10.1080/17538947.2022.2138590
https://doaj.org/article/c98af3f00e9d4f09a223c1541c9cf223
op_doi https://doi.org/10.1080/17538947.2022.2138590
container_title International Journal of Digital Earth
container_volume 15
container_issue 1
container_start_page 1817
op_container_end_page 1832
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