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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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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1779321507422404608 |