Retrieve Ice Velocities and Invert Spatial Rigidity of the Larsen C Ice Shelf Based on Sentinel-1 Interferometric Data
The Larsen C Ice Shelf (LCIS) is the largest ice shelf in the Antarctica Peninsula, and its state can be considered to be an indicator of local climate change. The goal of this paper is to invert the rigidity of the LCIS based on the interferometric synthetic aperture radar (InSAR) technique using S...
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ftmdpi:oai:mdpi.com:/2072-4292/13/12/2361/ 2023-08-20T04:01:57+02:00 Retrieve Ice Velocities and Invert Spatial Rigidity of the Larsen C Ice Shelf Based on Sentinel-1 Interferometric Data Faming Gong Kui Zhang Shujun Liu agris 2021-06-17 application/pdf https://doi.org/10.3390/rs13122361 EN eng Multidisciplinary Digital Publishing Institute Engineering Remote Sensing https://dx.doi.org/10.3390/rs13122361 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 12; Pages: 2361 InSAR ice velocity field Sentinel-1 images missing data recovering ice rigidity inversion Text 2021 ftmdpi https://doi.org/10.3390/rs13122361 2023-08-01T01:58:08Z The Larsen C Ice Shelf (LCIS) is the largest ice shelf in the Antarctica Peninsula, and its state can be considered to be an indicator of local climate change. The goal of this paper is to invert the rigidity of the LCIS based on the interferometric synthetic aperture radar (InSAR) technique using Sentinel-1 images. A targeted processing chain is first used to obtain reliable interferometric phase measurements under the circumstance of rapid ice flow. Unfortunately, only the descending data are available, which disallows the corresponding 2-D velocity field to be directly obtained from such measurements. A new approach is thus proposed to estimate the interferometric phase-based 2-D velocity field with the assistance of speckle tracking offsets. This approach establishes an implicit relationship between range and azimuth displacements based on speckle tracking observations. By taking advantage of such a relationship, the equivalent interferometric signals in the azimuth direction are estimated, thereby recovering the interferometric phase-based 2-D ice velocity field of the LCIS. To further investigate the state of the LCIS, the recovered 2-D velocity field is utilized to invert the ice rigidity. The shallow-shelf approximation (SSA) is the core of the reverse model, which is closely dependent on boundary conditions, including kinematic and dynamic conditions. The experimental results demonstrate that the spatial distribution of the rigidity varies approximately from 70 MPa·s1/3 to 300 MPa·s1/3. This rigidity distribution can reproduce a similar ice flow pattern to the observations. Text Antarc* Antarctica Ice Shelf MDPI Open Access Publishing Remote Sensing 13 12 2361 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
InSAR ice velocity field Sentinel-1 images missing data recovering ice rigidity inversion |
spellingShingle |
InSAR ice velocity field Sentinel-1 images missing data recovering ice rigidity inversion Faming Gong Kui Zhang Shujun Liu Retrieve Ice Velocities and Invert Spatial Rigidity of the Larsen C Ice Shelf Based on Sentinel-1 Interferometric Data |
topic_facet |
InSAR ice velocity field Sentinel-1 images missing data recovering ice rigidity inversion |
description |
The Larsen C Ice Shelf (LCIS) is the largest ice shelf in the Antarctica Peninsula, and its state can be considered to be an indicator of local climate change. The goal of this paper is to invert the rigidity of the LCIS based on the interferometric synthetic aperture radar (InSAR) technique using Sentinel-1 images. A targeted processing chain is first used to obtain reliable interferometric phase measurements under the circumstance of rapid ice flow. Unfortunately, only the descending data are available, which disallows the corresponding 2-D velocity field to be directly obtained from such measurements. A new approach is thus proposed to estimate the interferometric phase-based 2-D velocity field with the assistance of speckle tracking offsets. This approach establishes an implicit relationship between range and azimuth displacements based on speckle tracking observations. By taking advantage of such a relationship, the equivalent interferometric signals in the azimuth direction are estimated, thereby recovering the interferometric phase-based 2-D ice velocity field of the LCIS. To further investigate the state of the LCIS, the recovered 2-D velocity field is utilized to invert the ice rigidity. The shallow-shelf approximation (SSA) is the core of the reverse model, which is closely dependent on boundary conditions, including kinematic and dynamic conditions. The experimental results demonstrate that the spatial distribution of the rigidity varies approximately from 70 MPa·s1/3 to 300 MPa·s1/3. This rigidity distribution can reproduce a similar ice flow pattern to the observations. |
format |
Text |
author |
Faming Gong Kui Zhang Shujun Liu |
author_facet |
Faming Gong Kui Zhang Shujun Liu |
author_sort |
Faming Gong |
title |
Retrieve Ice Velocities and Invert Spatial Rigidity of the Larsen C Ice Shelf Based on Sentinel-1 Interferometric Data |
title_short |
Retrieve Ice Velocities and Invert Spatial Rigidity of the Larsen C Ice Shelf Based on Sentinel-1 Interferometric Data |
title_full |
Retrieve Ice Velocities and Invert Spatial Rigidity of the Larsen C Ice Shelf Based on Sentinel-1 Interferometric Data |
title_fullStr |
Retrieve Ice Velocities and Invert Spatial Rigidity of the Larsen C Ice Shelf Based on Sentinel-1 Interferometric Data |
title_full_unstemmed |
Retrieve Ice Velocities and Invert Spatial Rigidity of the Larsen C Ice Shelf Based on Sentinel-1 Interferometric Data |
title_sort |
retrieve ice velocities and invert spatial rigidity of the larsen c ice shelf based on sentinel-1 interferometric data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13122361 |
op_coverage |
agris |
genre |
Antarc* Antarctica Ice Shelf |
genre_facet |
Antarc* Antarctica Ice Shelf |
op_source |
Remote Sensing; Volume 13; Issue 12; Pages: 2361 |
op_relation |
Engineering Remote Sensing https://dx.doi.org/10.3390/rs13122361 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13122361 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
12 |
container_start_page |
2361 |
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1774712345841369088 |