A textural approach to improving snow depth estimates in the Weddell Sea
© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Mei, M. J., & Maksym, T. A textural approach to improving snow depth estimates in the Weddell Sea. Remote Sensing, 12(9), (2020): 1494-1494, doi...
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ftwhoas:oai:darchive.mblwhoilibrary.org:1912/26010 2023-05-15T13:48:31+02:00 A textural approach to improving snow depth estimates in the Weddell Sea Mei, M. Jeffrey Maksym, Ted 2020-05-08 https://hdl.handle.net/1912/26010 unknown MDPI https://doi.org/10.3390/rs12091494 Mei, M. J., & Maksym, T. (2020). A textural approach to improving snow depth estimates in the Weddell Sea. Remote Sensing, 12(9), 1494-1494. https://hdl.handle.net/1912/26010 doi:10.3390/rs12091494 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ CC-BY Mei, M. J., & Maksym, T. (2020). A textural approach to improving snow depth estimates in the Weddell Sea. Remote Sensing, 12(9), 1494-1494. doi:10.3390/rs12091494 Sea ice Morphology Texture Segmentation Snow depth Article 2020 ftwhoas https://doi.org/10.3390/rs12091494 2022-10-22T22:57:09Z © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Mei, M. J., & Maksym, T. A textural approach to improving snow depth estimates in the Weddell Sea. Remote Sensing, 12(9), (2020): 1494-1494, doi:10.3390/rs12091494. The snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2. Snow redistributed by wind collects around areas of deformed ice and forms a wide variety of features on sea ice; the morphology of these features may provide some indication of the mean snow depth. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow using snow surface freeboard measurements from Operation IceBridge campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, even when they have different absolute snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth with ∼22% error. We show that at the floe scale (∼180 m), snow depth can be directly estimated from the snow surface with ∼20% error using deep learning techniques, and that the learned filters are comparable to standard textural analysis techniques. This error drops to ∼14% when averaged over 1.5 km scales. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, as compared to current methods. Such methods may be useful for reducing uncertainty in Antarctic sea ice thickness estimates from ICESat-2. This research was funded by National Aeronautics and Space Administration grant number NNX15AC69G and the US National Science Foundation grant number ANT-1341513. Article in Journal/Newspaper Antarc* Antarctic Sea ice Weddell Sea Woods Hole Scientific Community: WHOAS (Woods Hole Open Access Server) Antarctic Weddell Weddell Sea Remote Sensing 12 9 1494 |
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Open Polar |
collection |
Woods Hole Scientific Community: WHOAS (Woods Hole Open Access Server) |
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ftwhoas |
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topic |
Sea ice Morphology Texture Segmentation Snow depth |
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Sea ice Morphology Texture Segmentation Snow depth Mei, M. Jeffrey Maksym, Ted A textural approach to improving snow depth estimates in the Weddell Sea |
topic_facet |
Sea ice Morphology Texture Segmentation Snow depth |
description |
© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Mei, M. J., & Maksym, T. A textural approach to improving snow depth estimates in the Weddell Sea. Remote Sensing, 12(9), (2020): 1494-1494, doi:10.3390/rs12091494. The snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2. Snow redistributed by wind collects around areas of deformed ice and forms a wide variety of features on sea ice; the morphology of these features may provide some indication of the mean snow depth. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow using snow surface freeboard measurements from Operation IceBridge campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, even when they have different absolute snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth with ∼22% error. We show that at the floe scale (∼180 m), snow depth can be directly estimated from the snow surface with ∼20% error using deep learning techniques, and that the learned filters are comparable to standard textural analysis techniques. This error drops to ∼14% when averaged over 1.5 km scales. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, as compared to current methods. Such methods may be useful for reducing uncertainty in Antarctic sea ice thickness estimates from ICESat-2. This research was funded by National Aeronautics and Space Administration grant number NNX15AC69G and the US National Science Foundation grant number ANT-1341513. |
format |
Article in Journal/Newspaper |
author |
Mei, M. Jeffrey Maksym, Ted |
author_facet |
Mei, M. Jeffrey Maksym, Ted |
author_sort |
Mei, M. Jeffrey |
title |
A textural approach to improving snow depth estimates in the Weddell Sea |
title_short |
A textural approach to improving snow depth estimates in the Weddell Sea |
title_full |
A textural approach to improving snow depth estimates in the Weddell Sea |
title_fullStr |
A textural approach to improving snow depth estimates in the Weddell Sea |
title_full_unstemmed |
A textural approach to improving snow depth estimates in the Weddell Sea |
title_sort |
textural approach to improving snow depth estimates in the weddell sea |
publisher |
MDPI |
publishDate |
2020 |
url |
https://hdl.handle.net/1912/26010 |
geographic |
Antarctic Weddell Weddell Sea |
geographic_facet |
Antarctic Weddell Weddell Sea |
genre |
Antarc* Antarctic Sea ice Weddell Sea |
genre_facet |
Antarc* Antarctic Sea ice Weddell Sea |
op_source |
Mei, M. J., & Maksym, T. (2020). A textural approach to improving snow depth estimates in the Weddell Sea. Remote Sensing, 12(9), 1494-1494. doi:10.3390/rs12091494 |
op_relation |
https://doi.org/10.3390/rs12091494 Mei, M. J., & Maksym, T. (2020). A textural approach to improving snow depth estimates in the Weddell Sea. Remote Sensing, 12(9), 1494-1494. https://hdl.handle.net/1912/26010 doi:10.3390/rs12091494 |
op_rights |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/rs12091494 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
9 |
container_start_page |
1494 |
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1766249379154362368 |