Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations
Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical dep...
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ftdoajarticles:oai:doaj.org/article:c4e3c5bfc9964faa8fc9cc7635a6fd46 2023-05-15T14:35:08+02:00 Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations Zheng Shi Tingyan Xing Jie Guang Yong Xue Yahui Che 2019-04-01T00:00:00Z https://doi.org/10.3390/rs11080891 https://doaj.org/article/c4e3c5bfc9964faa8fc9cc7635a6fd46 EN eng MDPI AG https://www.mdpi.com/2072-4292/11/8/891 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11080891 https://doaj.org/article/c4e3c5bfc9964faa8fc9cc7635a6fd46 Remote Sensing, Vol 11, Iss 8, p 891 (2019) Arctic AATSR AOD snow Science Q article 2019 ftdoajarticles https://doi.org/10.3390/rs11080891 2022-12-31T11:23:40Z Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) is derived using the advanced along-track scanning radiometer (AATSR) instrument. The basic idea is to utilize the dual-viewing observation capability of AATSR to reduce the impacts of AOD uncertainties introduced by the absolute wavelength-dependent error on surface reflectance estimation. AOD is derived assuming that the satellite observed surface reflectance ratio can be well characterized by a snow bidirectional reflectance distribution function (BRDF) model with a certain correction direct from satellite top of the atmosphere (TOA) observation. The aerosol types include an Arctic haze aerosol obtained from campaign measurement and Arctic background aerosol (maritime aerosol) types. The proper aerosol type is selected during the iteration step based on the minimization residual. The algorithm has been used over Spitsbergen for the spring period (April–May) and the AOD spatial distribution indicates that the retrieval AOD can capture the Arctic haze event. The comparison with AERONET observations shows promising results, with a correlation coefficient R = 0.70. The time series analysis shows no systematical biases between AATSR retrieved AOD and AERONET observed ones. Article in Journal/Newspaper Arctic Climate change Spitsbergen Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 11 8 891 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic AATSR AOD snow Science Q |
spellingShingle |
Arctic AATSR AOD snow Science Q Zheng Shi Tingyan Xing Jie Guang Yong Xue Yahui Che Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations |
topic_facet |
Arctic AATSR AOD snow Science Q |
description |
Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) is derived using the advanced along-track scanning radiometer (AATSR) instrument. The basic idea is to utilize the dual-viewing observation capability of AATSR to reduce the impacts of AOD uncertainties introduced by the absolute wavelength-dependent error on surface reflectance estimation. AOD is derived assuming that the satellite observed surface reflectance ratio can be well characterized by a snow bidirectional reflectance distribution function (BRDF) model with a certain correction direct from satellite top of the atmosphere (TOA) observation. The aerosol types include an Arctic haze aerosol obtained from campaign measurement and Arctic background aerosol (maritime aerosol) types. The proper aerosol type is selected during the iteration step based on the minimization residual. The algorithm has been used over Spitsbergen for the spring period (April–May) and the AOD spatial distribution indicates that the retrieval AOD can capture the Arctic haze event. The comparison with AERONET observations shows promising results, with a correlation coefficient R = 0.70. The time series analysis shows no systematical biases between AATSR retrieved AOD and AERONET observed ones. |
format |
Article in Journal/Newspaper |
author |
Zheng Shi Tingyan Xing Jie Guang Yong Xue Yahui Che |
author_facet |
Zheng Shi Tingyan Xing Jie Guang Yong Xue Yahui Che |
author_sort |
Zheng Shi |
title |
Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations |
title_short |
Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations |
title_full |
Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations |
title_fullStr |
Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations |
title_full_unstemmed |
Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations |
title_sort |
aerosol optical depth over the arctic snow-covered regions derived from dual-viewing satellite observations |
publisher |
MDPI AG |
publishDate |
2019 |
url |
https://doi.org/10.3390/rs11080891 https://doaj.org/article/c4e3c5bfc9964faa8fc9cc7635a6fd46 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Spitsbergen |
genre_facet |
Arctic Climate change Spitsbergen |
op_source |
Remote Sensing, Vol 11, Iss 8, p 891 (2019) |
op_relation |
https://www.mdpi.com/2072-4292/11/8/891 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11080891 https://doaj.org/article/c4e3c5bfc9964faa8fc9cc7635a6fd46 |
op_doi |
https://doi.org/10.3390/rs11080891 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
8 |
container_start_page |
891 |
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1766308010959831040 |