Estimating Penetration-Related X-Band InSAR Elevation Bias: A Study over the Greenland Ice Sheet
Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) sys...
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ftmdpi:oai:mdpi.com:/2072-4292/11/24/2903/ 2023-09-05T13:19:47+02:00 Estimating Penetration-Related X-Band InSAR Elevation Bias: A Study over the Greenland Ice Sheet Sahra Abdullahi Birgit Wessel Martin Huber Anna Wendleder Achim Roth Claudia Kuenzer agris 2019-12-05 application/pdf https://doi.org/10.3390/rs11242903 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs11242903 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 11; Issue 24; Pages: 2903 InSAR height penetration bias cryosphere TanDEM-X Greenland ice sheet DEM Text 2019 ftmdpi https://doi.org/10.3390/rs11242903 2023-08-13T23:52:30Z Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R2 = 68% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection. Text Greenland Ice Sheet MDPI Open Access Publishing Greenland Remote Sensing 11 24 2903 |
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Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
InSAR height penetration bias cryosphere TanDEM-X Greenland ice sheet DEM |
spellingShingle |
InSAR height penetration bias cryosphere TanDEM-X Greenland ice sheet DEM Sahra Abdullahi Birgit Wessel Martin Huber Anna Wendleder Achim Roth Claudia Kuenzer Estimating Penetration-Related X-Band InSAR Elevation Bias: A Study over the Greenland Ice Sheet |
topic_facet |
InSAR height penetration bias cryosphere TanDEM-X Greenland ice sheet DEM |
description |
Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R2 = 68% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection. |
format |
Text |
author |
Sahra Abdullahi Birgit Wessel Martin Huber Anna Wendleder Achim Roth Claudia Kuenzer |
author_facet |
Sahra Abdullahi Birgit Wessel Martin Huber Anna Wendleder Achim Roth Claudia Kuenzer |
author_sort |
Sahra Abdullahi |
title |
Estimating Penetration-Related X-Band InSAR Elevation Bias: A Study over the Greenland Ice Sheet |
title_short |
Estimating Penetration-Related X-Band InSAR Elevation Bias: A Study over the Greenland Ice Sheet |
title_full |
Estimating Penetration-Related X-Band InSAR Elevation Bias: A Study over the Greenland Ice Sheet |
title_fullStr |
Estimating Penetration-Related X-Band InSAR Elevation Bias: A Study over the Greenland Ice Sheet |
title_full_unstemmed |
Estimating Penetration-Related X-Band InSAR Elevation Bias: A Study over the Greenland Ice Sheet |
title_sort |
estimating penetration-related x-band insar elevation bias: a study over the greenland ice sheet |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
url |
https://doi.org/10.3390/rs11242903 |
op_coverage |
agris |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
Remote Sensing; Volume 11; Issue 24; Pages: 2903 |
op_relation |
Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs11242903 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs11242903 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
24 |
container_start_page |
2903 |
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1776200606188306432 |