Comparing SAR based short time-lag cross-correlation and Doppler derived sea ice drift velocities
Accepted manuscript version. Published version available in IEEE Transactions on Geoscience and Remote Sensing 2018, 56(4). This paper shows initial results from estimating Doppler radial surface velocities (RVLs) over Arctic sea ice using the Sentinel-1A (S1A) satellite. Our study presents the firs...
Published in: | IEEE Transactions on Geoscience and Remote Sensing |
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Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2017
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Subjects: | |
Online Access: | https://hdl.handle.net/10037/12572 https://doi.org/10.1109/TGRS.2017.2769222 |
_version_ | 1829303112373895168 |
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author | Kræmer, Thomas Johnsen, Harald Brekke, Camilla Engen, Geir |
author_facet | Kræmer, Thomas Johnsen, Harald Brekke, Camilla Engen, Geir |
author_sort | Kræmer, Thomas |
collection | University of Tromsø: Munin Open Research Archive |
container_issue | 4 |
container_start_page | 1898 |
container_title | IEEE Transactions on Geoscience and Remote Sensing |
container_volume | 56 |
description | Accepted manuscript version. Published version available in IEEE Transactions on Geoscience and Remote Sensing 2018, 56(4). This paper shows initial results from estimating Doppler radial surface velocities (RVLs) over Arctic sea ice using the Sentinel-1A (S1A) satellite. Our study presents the first quantitative comparison between ice drift derived from the Doppler shifts and drift derived using time-series methods over comparable time scales. We compare the Doppler-derived ice velocities with global positioning system tracks from a drifting ice station as well as vector fields derived using traditional cross correlation between a pair of S1A and Radarsat-2 images with a time lag of only 25 min. A strategy is provided for precise calibration of the Doppler values in the context of the S1A level-2 ocean RVL product. When comparing the two methods, root-mean-squared errors (RMSEs) of 7 cm/s were found for the extra wide (EW4) and EW5 swaths, while the highest RMSE of 32 cm/s was obtained for the EW1 swath. Though the agreement is not perfect, our experiment demonstrates that the Doppler technique is capable of measuring a signal from the ice if the ice is fast moving. However, for typical ice speeds, the uncertainties quickly grow beyond the speeds we are trying to measure. Finally, we show how the application of an antenna pattern correction reduces a bias in the estimated Doppler offsets. |
format | Article in Journal/Newspaper |
genre | Arctic Arctic Sea ice |
genre_facet | Arctic Arctic Sea ice |
geographic | Arctic The Sentinel |
geographic_facet | Arctic The Sentinel |
id | ftunivtroemsoe:oai:munin.uit.no:10037/12572 |
institution | Open Polar |
language | English |
long_lat | ENVELOPE(73.317,73.317,-52.983,-52.983) |
op_collection_id | ftunivtroemsoe |
op_container_end_page | 1908 |
op_doi | https://doi.org/10.1109/TGRS.2017.2769222 |
op_relation | IEEE Transactions on Geoscience and Remote Sensing info:eu-repo/grantAgreement/RCN/NORDSATS/195143/Jurisdiction/Arctic Earth Observation and Surveillance Technologies// info:eu-repo/grantAgreement/RCN/SFI/237906/NORWAY/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ FRIDAID 1509376 doi:10.1109/TGRS.2017.2769222 https://hdl.handle.net/10037/12572 |
op_rights | openAccess |
publishDate | 2017 |
publisher | Institute of Electrical and Electronics Engineers (IEEE) |
record_format | openpolar |
spelling | ftunivtroemsoe:oai:munin.uit.no:10037/12572 2025-04-13T14:11:19+00:00 Comparing SAR based short time-lag cross-correlation and Doppler derived sea ice drift velocities Kræmer, Thomas Johnsen, Harald Brekke, Camilla Engen, Geir 2017-11-23 https://hdl.handle.net/10037/12572 https://doi.org/10.1109/TGRS.2017.2769222 eng eng Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Geoscience and Remote Sensing info:eu-repo/grantAgreement/RCN/NORDSATS/195143/Jurisdiction/Arctic Earth Observation and Surveillance Technologies// info:eu-repo/grantAgreement/RCN/SFI/237906/NORWAY/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ FRIDAID 1509376 doi:10.1109/TGRS.2017.2769222 https://hdl.handle.net/10037/12572 openAccess VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering visualisering signalbehandling bildeanalyse: 429 VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation visualization signal processing image processing: 429 Journal article Tidsskriftartikkel Peer reviewed 2017 ftunivtroemsoe https://doi.org/10.1109/TGRS.2017.2769222 2025-03-14T05:17:55Z Accepted manuscript version. Published version available in IEEE Transactions on Geoscience and Remote Sensing 2018, 56(4). This paper shows initial results from estimating Doppler radial surface velocities (RVLs) over Arctic sea ice using the Sentinel-1A (S1A) satellite. Our study presents the first quantitative comparison between ice drift derived from the Doppler shifts and drift derived using time-series methods over comparable time scales. We compare the Doppler-derived ice velocities with global positioning system tracks from a drifting ice station as well as vector fields derived using traditional cross correlation between a pair of S1A and Radarsat-2 images with a time lag of only 25 min. A strategy is provided for precise calibration of the Doppler values in the context of the S1A level-2 ocean RVL product. When comparing the two methods, root-mean-squared errors (RMSEs) of 7 cm/s were found for the extra wide (EW4) and EW5 swaths, while the highest RMSE of 32 cm/s was obtained for the EW1 swath. Though the agreement is not perfect, our experiment demonstrates that the Doppler technique is capable of measuring a signal from the ice if the ice is fast moving. However, for typical ice speeds, the uncertainties quickly grow beyond the speeds we are trying to measure. Finally, we show how the application of an antenna pattern correction reduces a bias in the estimated Doppler offsets. Article in Journal/Newspaper Arctic Arctic Sea ice University of Tromsø: Munin Open Research Archive Arctic The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) IEEE Transactions on Geoscience and Remote Sensing 56 4 1898 1908 |
spellingShingle | VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering visualisering signalbehandling bildeanalyse: 429 VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation visualization signal processing image processing: 429 Kræmer, Thomas Johnsen, Harald Brekke, Camilla Engen, Geir Comparing SAR based short time-lag cross-correlation and Doppler derived sea ice drift velocities |
title | Comparing SAR based short time-lag cross-correlation and Doppler derived sea ice drift velocities |
title_full | Comparing SAR based short time-lag cross-correlation and Doppler derived sea ice drift velocities |
title_fullStr | Comparing SAR based short time-lag cross-correlation and Doppler derived sea ice drift velocities |
title_full_unstemmed | Comparing SAR based short time-lag cross-correlation and Doppler derived sea ice drift velocities |
title_short | Comparing SAR based short time-lag cross-correlation and Doppler derived sea ice drift velocities |
title_sort | comparing sar based short time-lag cross-correlation and doppler derived sea ice drift velocities |
topic | VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering visualisering signalbehandling bildeanalyse: 429 VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation visualization signal processing image processing: 429 |
topic_facet | VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering visualisering signalbehandling bildeanalyse: 429 VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation visualization signal processing image processing: 429 |
url | https://hdl.handle.net/10037/12572 https://doi.org/10.1109/TGRS.2017.2769222 |