A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea
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 feature on sea ice; the morphology of...
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2020
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Online Access: | https://doi.org/10.3390/rs12091494 https://doaj.org/article/0df9e7b01ecd4b4792cb8488cea8359b |
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ftdoajarticles:oai:doaj.org/article:0df9e7b01ecd4b4792cb8488cea8359b 2023-05-15T13:33:03+02:00 A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea M. Jeffrey Mei Ted Maksym 2020-05-01T00:00:00Z https://doi.org/10.3390/rs12091494 https://doaj.org/article/0df9e7b01ecd4b4792cb8488cea8359b EN eng MDPI AG https://www.mdpi.com/2072-4292/12/9/1494 https://doaj.org/toc/2072-4292 doi:10.3390/rs12091494 2072-4292 https://doaj.org/article/0df9e7b01ecd4b4792cb8488cea8359b Remote Sensing, Vol 12, Iss 1494, p 1494 (2020) sea ice morphology texture segmentation snow depth Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12091494 2022-12-31T07:30:22Z 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 feature 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. Article in Journal/Newspaper Antarc* Antarctic Sea ice Weddell Sea Directory of Open Access Journals: DOAJ Articles Antarctic Weddell Sea Weddell Remote Sensing 12 9 1494 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
sea ice morphology texture segmentation snow depth Science Q |
spellingShingle |
sea ice morphology texture segmentation snow depth Science Q M. Jeffrey Mei Ted Maksym A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea |
topic_facet |
sea ice morphology texture segmentation snow depth Science Q |
description |
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 feature 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. |
format |
Article in Journal/Newspaper |
author |
M. Jeffrey Mei Ted Maksym |
author_facet |
M. Jeffrey Mei Ted Maksym |
author_sort |
M. Jeffrey Mei |
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 AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12091494 https://doaj.org/article/0df9e7b01ecd4b4792cb8488cea8359b |
geographic |
Antarctic Weddell Sea Weddell |
geographic_facet |
Antarctic Weddell Sea Weddell |
genre |
Antarc* Antarctic Sea ice Weddell Sea |
genre_facet |
Antarc* Antarctic Sea ice Weddell Sea |
op_source |
Remote Sensing, Vol 12, Iss 1494, p 1494 (2020) |
op_relation |
https://www.mdpi.com/2072-4292/12/9/1494 https://doaj.org/toc/2072-4292 doi:10.3390/rs12091494 2072-4292 https://doaj.org/article/0df9e7b01ecd4b4792cb8488cea8359b |
op_doi |
https://doi.org/10.3390/rs12091494 |
container_title |
Remote Sensing |
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12 |
container_issue |
9 |
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1494 |
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1766038046168317952 |