Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica
The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve...
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ftdoajarticles:oai:doaj.org/article:2a02cd4cd85c4ce9b42590e227f0659e 2023-05-15T14:01:53+02:00 Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica Zhiwei Chen Rong Jin Liqiang Zhang Ke Chen Qingxia Li 2023-03-01T00:00:00Z https://doi.org/10.3390/rs15051396 https://doaj.org/article/2a02cd4cd85c4ce9b42590e227f0659e EN eng MDPI AG https://www.mdpi.com/2072-4292/15/5/1396 https://doaj.org/toc/2072-4292 doi:10.3390/rs15051396 2072-4292 https://doaj.org/article/2a02cd4cd85c4ce9b42590e227f0659e Remote Sensing, Vol 15, Iss 1396, p 1396 (2023) conjoint inversion algorithm snow temperature profiles microwave brightness temperature infrared brightness temperature Science Q article 2023 ftdoajarticles https://doi.org/10.3390/rs15051396 2023-03-12T01:28:58Z The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve the snow temperature profiles by combining microwave and infrared brightness temperatures. This paper proposes a conjoint inversion algorithm to retrieve snow temperature profiles by combining multi-frequency microwave brightness temperature (BT) with infrared BT, in which different weight functions of microwave BT at different frequencies are adopted, and the atmosphere influence has also been corrected. The snow temperature profile data are retrieved based on AMSR2 microwave BT data and MODIS infrared BT data in 2017 and 2018, which are evaluated by comparing with the measured snow temperature at Dome-C station. The results confirm that the inverted snow temperature profiles are consistent with the field observation data from the Dome-C station. Multi-frequency microwave brightness temperature can be used to invert the snow temperature profiles; however, the inverted snow surface temperature is more accurate by combining the infrared BT with the microwave BT in the conjoint inversion algorithm. Article in Journal/Newspaper Antarc* Antarctica Ice Sheet Directory of Open Access Journals: DOAJ Articles Remote Sensing 15 5 1396 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
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conjoint inversion algorithm snow temperature profiles microwave brightness temperature infrared brightness temperature Science Q |
spellingShingle |
conjoint inversion algorithm snow temperature profiles microwave brightness temperature infrared brightness temperature Science Q Zhiwei Chen Rong Jin Liqiang Zhang Ke Chen Qingxia Li Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica |
topic_facet |
conjoint inversion algorithm snow temperature profiles microwave brightness temperature infrared brightness temperature Science Q |
description |
The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve the snow temperature profiles by combining microwave and infrared brightness temperatures. This paper proposes a conjoint inversion algorithm to retrieve snow temperature profiles by combining multi-frequency microwave brightness temperature (BT) with infrared BT, in which different weight functions of microwave BT at different frequencies are adopted, and the atmosphere influence has also been corrected. The snow temperature profile data are retrieved based on AMSR2 microwave BT data and MODIS infrared BT data in 2017 and 2018, which are evaluated by comparing with the measured snow temperature at Dome-C station. The results confirm that the inverted snow temperature profiles are consistent with the field observation data from the Dome-C station. Multi-frequency microwave brightness temperature can be used to invert the snow temperature profiles; however, the inverted snow surface temperature is more accurate by combining the infrared BT with the microwave BT in the conjoint inversion algorithm. |
format |
Article in Journal/Newspaper |
author |
Zhiwei Chen Rong Jin Liqiang Zhang Ke Chen Qingxia Li |
author_facet |
Zhiwei Chen Rong Jin Liqiang Zhang Ke Chen Qingxia Li |
author_sort |
Zhiwei Chen |
title |
Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica |
title_short |
Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica |
title_full |
Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica |
title_fullStr |
Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica |
title_full_unstemmed |
Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica |
title_sort |
conjoint inversion of snow temperature profiles from microwave and infrared brightness temperature in antarctica |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15051396 https://doaj.org/article/2a02cd4cd85c4ce9b42590e227f0659e |
genre |
Antarc* Antarctica Ice Sheet |
genre_facet |
Antarc* Antarctica Ice Sheet |
op_source |
Remote Sensing, Vol 15, Iss 1396, p 1396 (2023) |
op_relation |
https://www.mdpi.com/2072-4292/15/5/1396 https://doaj.org/toc/2072-4292 doi:10.3390/rs15051396 2072-4292 https://doaj.org/article/2a02cd4cd85c4ce9b42590e227f0659e |
op_doi |
https://doi.org/10.3390/rs15051396 |
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Remote Sensing |
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