Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network
Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter...
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ftdoajarticles:oai:doaj.org/article:54edb3ec46444883b917ae155152531b 2023-05-15T13:11:41+02:00 Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network A. Braakmann-Folgmann C. Donlon 2019-09-01T00:00:00Z https://doi.org/10.5194/tc-13-2421-2019 https://doaj.org/article/54edb3ec46444883b917ae155152531b EN eng Copernicus Publications https://www.the-cryosphere.net/13/2421/2019/tc-13-2421-2019.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-13-2421-2019 1994-0416 1994-0424 https://doaj.org/article/54edb3ec46444883b917ae155152531b The Cryosphere, Vol 13, Pp 2421-2438 (2019) Environmental sciences GE1-350 Geology QE1-996.5 article 2019 ftdoajarticles https://doi.org/10.5194/tc-13-2421-2019 2022-12-31T14:22:07Z Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge. A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath >1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn–dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are ... Article in Journal/Newspaper albedo Arctic Sea ice The Cryosphere Directory of Open Access Journals: DOAJ Articles Arctic The Cryosphere 13 9 2421 2438 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 A. Braakmann-Folgmann C. Donlon Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge. A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath >1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn–dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are ... |
format |
Article in Journal/Newspaper |
author |
A. Braakmann-Folgmann C. Donlon |
author_facet |
A. Braakmann-Folgmann C. Donlon |
author_sort |
A. Braakmann-Folgmann |
title |
Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network |
title_short |
Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network |
title_full |
Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network |
title_fullStr |
Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network |
title_full_unstemmed |
Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network |
title_sort |
estimating snow depth on arctic sea ice using satellite microwave radiometry and a neural network |
publisher |
Copernicus Publications |
publishDate |
2019 |
url |
https://doi.org/10.5194/tc-13-2421-2019 https://doaj.org/article/54edb3ec46444883b917ae155152531b |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
albedo Arctic Sea ice The Cryosphere |
genre_facet |
albedo Arctic Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 13, Pp 2421-2438 (2019) |
op_relation |
https://www.the-cryosphere.net/13/2421/2019/tc-13-2421-2019.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-13-2421-2019 1994-0416 1994-0424 https://doaj.org/article/54edb3ec46444883b917ae155152531b |
op_doi |
https://doi.org/10.5194/tc-13-2421-2019 |
container_title |
The Cryosphere |
container_volume |
13 |
container_issue |
9 |
container_start_page |
2421 |
op_container_end_page |
2438 |
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1766248503518953472 |