Articles | Volume 10, issue 2
https://doi.org/10.5194/tc-10-913-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-913-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
26 Apr 2016
Research article |
| 26 Apr 2016
Open-source feature-tracking algorithm for sea ice drift retrieval from Sentinel-1 SAR imagery
Stefan Muckenhuber
CORRESPONDING AUTHOR
Nansen Environmental and Remote Sensing Center (NERSC), Thormøhlensgate 47, 5006 Bergen, Norway
Anton Andreevich Korosov
Nansen Environmental and Remote Sensing Center (NERSC), Thormøhlensgate 47, 5006 Bergen, Norway
Stein Sandven
Nansen Environmental and Remote Sensing Center (NERSC), Thormøhlensgate 47, 5006 Bergen, Norway
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44 citations as recorded by crossref.
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- Deep learning outperforms existing algorithms in glacier surface velocity estimation with high-resolution data – the example of Austerdalsbreen, Norway H. Zandler et al. https://doi.org/10.3389/frsen.2025.1586933
- Enhanced Delaunay Triangulation Sea Ice Tracking Algorithm with Combining Feature Tracking and Pattern Matching M. Zhang et al. https://doi.org/10.3390/rs12030581
- Ocean Eddy Signature on SAR‐Derived Sea Ice Drift and Vorticity A. Cassianides et al. https://doi.org/10.1029/2020GL092066
- Arctic sea ice motion retrieval from multisource SAR images using a keypoint-free feature tracking algorithm T. Gao et al. https://doi.org/10.1016/j.isprsjprs.2025.09.013
- An improved optical flow method to estimate Arctic sea ice velocity (winter 2014–2016) H. Li et al. https://doi.org/10.1007/s13131-021-1867-2
- Long-Term Analysis of Sea Ice Drift in the Western Ross Sea, Antarctica, at High and Low Spatial Resolution U. Farooq et al. https://doi.org/10.3390/rs12091402
- Development of a Novel Sea Ice Drift Detection Algorithm (SIDDA) Using SAR Data P. Joshi et al. https://doi.org/10.1007/s12524-025-02118-x
- A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data A. Korosov & P. Rampal https://doi.org/10.3390/rs9030258
- Evaluating Landfast Sea Ice Ridging near UtqiaġVik Alaska Using TanDEM-X Interferometry M. Marbouti et al. https://doi.org/10.3390/rs12081247
- On the Detection and Long-Term Path Visualisation of A-68 Iceberg L. Lopez-Lopez et al. https://doi.org/10.3390/rs13030460
- Estimating statistical errors in retrievals of ice velocity and deformation parameters from satellite images and buoy arrays W. Dierking et al. https://doi.org/10.5194/tc-14-2999-2020
- Extraction of Sea Ice Cover by Sentinel-1 SAR Based on Support Vector Machine With Unsupervised Generation of Training Data X. Li et al. https://doi.org/10.1109/TGRS.2020.3007789
- The role of cross-polarization in producing high-resolution pan-Arctic sea ice motion from the RADARSAT Constellation Mission over several years A. Komarov et al. https://doi.org/10.1016/j.rse.2025.115175
- Arctic sea ice motion retrieval via vision foundation model and cross-sensor SAR T. Gao et al. https://doi.org/10.1016/j.isprsjprs.2026.06.041
- Unsupervised domain adaptive object detection for assembly quality inspection X. Zhu et al. https://doi.org/10.1016/j.procir.2022.09.038
- Automated retrieval of internal wave phase speed and direction from pairs of SAR images with different look directions S. Furtney et al. https://doi.org/10.1016/j.rse.2024.114084
- Application of Feature Tracking Using K-Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking B. He et al. https://doi.org/10.1109/JSTARS.2022.3178117
- On the Localization Accuracy of Deformation Zones Retrieved from SAR-Based Sea Ice Drift Vector Fields A. Frost et al. https://doi.org/10.3390/rs17162801
- Pan-Antarctic evaluation of National Snow and Ice Data Center (NSIDC) sea ice drift product using high-resolution SAR and buoy data Y. Koo et al. https://doi.org/10.5194/eo-1-129-2026
- Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission T. Lavergne et al. https://doi.org/10.5194/tc-15-3681-2021
- Impact of Sea Ice Drift Retrieval Errors, Discretization and Grid Type on Calculations of Ice Deformation J. Griebel & W. Dierking https://doi.org/10.3390/rs10030393
- Sea ice drift vector extraction based on feature matching using CS-1 Images Y. Yang & T. Xie https://doi.org/10.1088/1742-6596/2718/1/012012
- Effects of Arctic Warming on Microbes and Methane in Different Land Types in Svalbard F. Zhang et al. https://doi.org/10.3390/w13223296
- Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel J. Park et al. https://doi.org/10.1109/TGRS.2017.2765248
- A Framework for Fine-Resolution and Spatially Continuous Arctic Sea Ice Drift Retrieval Using Multisensor Data X. Wang et al. https://doi.org/10.1109/TGRS.2024.3394882
- Partial Shape Recognition for Sea Ice Motion Retrieval in the Marginal Ice Zone from Sentinel-1 and Sentinel-2 M. Wang et al. https://doi.org/10.3390/rs13214473
- Sea ice export through the Fram Strait derived from a combined model and satellite data set C. Min et al. https://doi.org/10.5194/tc-13-3209-2019
- Ice Floe Tracker: An algorithm to automatically retrieve Lagrangian trajectories via feature matching from moderate-resolution visual imagery R. Lopez-Acosta et al. https://doi.org/10.1016/j.rse.2019.111406
- Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imagery M. Li et al. https://doi.org/10.1016/j.jag.2022.102908
- Sea Ice Drift Tracking From Sequential SAR Images Using Accelerated-KAZE Features D. Demchev et al. https://doi.org/10.1109/TGRS.2017.2703084
- Application of the Combined Feature Tracking and Maximum Cross-Correlation Algorithm to the Extraction of Sea Ice Motion Data From GF-3 Imagery M. Li et al. https://doi.org/10.1109/JSTARS.2022.3166897
- A Method to Improve High-Resolution Sea Ice Drift Retrievals in the Presence of Deformation Zones J. Griebel & W. Dierking https://doi.org/10.3390/rs9070718
- Retrieval of Sea Ice Drift From the Central Arctic to the Fram Strait Based on Sequential Sentinel-1 SAR Data Y. Qiu & X. Li https://doi.org/10.1109/TGRS.2022.3226223
- An adaptive machine learning approach to improve automatic iceberg detection from SAR images M. Barbat et al. https://doi.org/10.1016/j.isprsjprs.2019.08.015
- Improvement of Sea Ice Drift Extraction Based on Feature Tracking from C-SAR/01 Imagery Y. Yang et al. https://doi.org/10.1109/JSTARS.2024.3403919
- An improvement in accuracy and spatial resolution of the pattern-matching sea ice drift from SAR imagery X. Wang et al. https://doi.org/10.1080/17538947.2023.2264918
- Open-source sea ice drift algorithm for Sentinel-1 SAR imagery using a combination of feature tracking and pattern matching S. Muckenhuber & S. Sandven https://doi.org/10.5194/tc-11-1835-2017
- Pan-Arctic sea ice drift data at 400 m grids based on spaceborne SAR Y. Qiu & X. Li https://doi.org/10.1038/s41597-026-06683-9
- Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking C. Li et al. https://doi.org/10.1109/JSTARS.2022.3196026
- Modeling pan-Arctic seasonal and interannual landfast sea ice thickness and snow depth between 1979 and 2021 Z. Wang et al. https://doi.org/10.1080/17538947.2024.2376253
- Comparing SAR-Based Short Time-Lag Cross Correlation and Doppler-Derived Sea Ice Drift Velocities T. Kramer et al. https://doi.org/10.1109/TGRS.2017.2769222
- A Hausdorff-guided deep learning approach for tracking the motion of rotating Arctic ice floes A. Wu et al. https://doi.org/10.1016/j.oceaneng.2026.127007
- A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) T. Lavergne & E. Down https://doi.org/10.5194/essd-15-5807-2023
44 citations as recorded by crossref.
- LiMOSAT: a system for deriving Lagrangian sea-ice motion from satellite data S. Chua et al. https://doi.org/10.1080/01431161.2026.2709704
- Deep learning outperforms existing algorithms in glacier surface velocity estimation with high-resolution data – the example of Austerdalsbreen, Norway H. Zandler et al. https://doi.org/10.3389/frsen.2025.1586933
- Enhanced Delaunay Triangulation Sea Ice Tracking Algorithm with Combining Feature Tracking and Pattern Matching M. Zhang et al. https://doi.org/10.3390/rs12030581
- Ocean Eddy Signature on SAR‐Derived Sea Ice Drift and Vorticity A. Cassianides et al. https://doi.org/10.1029/2020GL092066
- Arctic sea ice motion retrieval from multisource SAR images using a keypoint-free feature tracking algorithm T. Gao et al. https://doi.org/10.1016/j.isprsjprs.2025.09.013
- An improved optical flow method to estimate Arctic sea ice velocity (winter 2014–2016) H. Li et al. https://doi.org/10.1007/s13131-021-1867-2
- Long-Term Analysis of Sea Ice Drift in the Western Ross Sea, Antarctica, at High and Low Spatial Resolution U. Farooq et al. https://doi.org/10.3390/rs12091402
- Development of a Novel Sea Ice Drift Detection Algorithm (SIDDA) Using SAR Data P. Joshi et al. https://doi.org/10.1007/s12524-025-02118-x
- A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data A. Korosov & P. Rampal https://doi.org/10.3390/rs9030258
- Evaluating Landfast Sea Ice Ridging near UtqiaġVik Alaska Using TanDEM-X Interferometry M. Marbouti et al. https://doi.org/10.3390/rs12081247
- On the Detection and Long-Term Path Visualisation of A-68 Iceberg L. Lopez-Lopez et al. https://doi.org/10.3390/rs13030460
- Estimating statistical errors in retrievals of ice velocity and deformation parameters from satellite images and buoy arrays W. Dierking et al. https://doi.org/10.5194/tc-14-2999-2020
- Extraction of Sea Ice Cover by Sentinel-1 SAR Based on Support Vector Machine With Unsupervised Generation of Training Data X. Li et al. https://doi.org/10.1109/TGRS.2020.3007789
- The role of cross-polarization in producing high-resolution pan-Arctic sea ice motion from the RADARSAT Constellation Mission over several years A. Komarov et al. https://doi.org/10.1016/j.rse.2025.115175
- Arctic sea ice motion retrieval via vision foundation model and cross-sensor SAR T. Gao et al. https://doi.org/10.1016/j.isprsjprs.2026.06.041
- Unsupervised domain adaptive object detection for assembly quality inspection X. Zhu et al. https://doi.org/10.1016/j.procir.2022.09.038
- Automated retrieval of internal wave phase speed and direction from pairs of SAR images with different look directions S. Furtney et al. https://doi.org/10.1016/j.rse.2024.114084
- Application of Feature Tracking Using K-Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking B. He et al. https://doi.org/10.1109/JSTARS.2022.3178117
- On the Localization Accuracy of Deformation Zones Retrieved from SAR-Based Sea Ice Drift Vector Fields A. Frost et al. https://doi.org/10.3390/rs17162801
- Pan-Antarctic evaluation of National Snow and Ice Data Center (NSIDC) sea ice drift product using high-resolution SAR and buoy data Y. Koo et al. https://doi.org/10.5194/eo-1-129-2026
- Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission T. Lavergne et al. https://doi.org/10.5194/tc-15-3681-2021
- Impact of Sea Ice Drift Retrieval Errors, Discretization and Grid Type on Calculations of Ice Deformation J. Griebel & W. Dierking https://doi.org/10.3390/rs10030393
- Sea ice drift vector extraction based on feature matching using CS-1 Images Y. Yang & T. Xie https://doi.org/10.1088/1742-6596/2718/1/012012
- Effects of Arctic Warming on Microbes and Methane in Different Land Types in Svalbard F. Zhang et al. https://doi.org/10.3390/w13223296
- Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel J. Park et al. https://doi.org/10.1109/TGRS.2017.2765248
- A Framework for Fine-Resolution and Spatially Continuous Arctic Sea Ice Drift Retrieval Using Multisensor Data X. Wang et al. https://doi.org/10.1109/TGRS.2024.3394882
- Partial Shape Recognition for Sea Ice Motion Retrieval in the Marginal Ice Zone from Sentinel-1 and Sentinel-2 M. Wang et al. https://doi.org/10.3390/rs13214473
- Sea ice export through the Fram Strait derived from a combined model and satellite data set C. Min et al. https://doi.org/10.5194/tc-13-3209-2019
- Ice Floe Tracker: An algorithm to automatically retrieve Lagrangian trajectories via feature matching from moderate-resolution visual imagery R. Lopez-Acosta et al. https://doi.org/10.1016/j.rse.2019.111406
- Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imagery M. Li et al. https://doi.org/10.1016/j.jag.2022.102908
- Sea Ice Drift Tracking From Sequential SAR Images Using Accelerated-KAZE Features D. Demchev et al. https://doi.org/10.1109/TGRS.2017.2703084
- Application of the Combined Feature Tracking and Maximum Cross-Correlation Algorithm to the Extraction of Sea Ice Motion Data From GF-3 Imagery M. Li et al. https://doi.org/10.1109/JSTARS.2022.3166897
- A Method to Improve High-Resolution Sea Ice Drift Retrievals in the Presence of Deformation Zones J. Griebel & W. Dierking https://doi.org/10.3390/rs9070718
- Retrieval of Sea Ice Drift From the Central Arctic to the Fram Strait Based on Sequential Sentinel-1 SAR Data Y. Qiu & X. Li https://doi.org/10.1109/TGRS.2022.3226223
- An adaptive machine learning approach to improve automatic iceberg detection from SAR images M. Barbat et al. https://doi.org/10.1016/j.isprsjprs.2019.08.015
- Improvement of Sea Ice Drift Extraction Based on Feature Tracking from C-SAR/01 Imagery Y. Yang et al. https://doi.org/10.1109/JSTARS.2024.3403919
- An improvement in accuracy and spatial resolution of the pattern-matching sea ice drift from SAR imagery X. Wang et al. https://doi.org/10.1080/17538947.2023.2264918
- Open-source sea ice drift algorithm for Sentinel-1 SAR imagery using a combination of feature tracking and pattern matching S. Muckenhuber & S. Sandven https://doi.org/10.5194/tc-11-1835-2017
- Pan-Arctic sea ice drift data at 400 m grids based on spaceborne SAR Y. Qiu & X. Li https://doi.org/10.1038/s41597-026-06683-9
- Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking C. Li et al. https://doi.org/10.1109/JSTARS.2022.3196026
- Modeling pan-Arctic seasonal and interannual landfast sea ice thickness and snow depth between 1979 and 2021 Z. Wang et al. https://doi.org/10.1080/17538947.2024.2376253
- Comparing SAR-Based Short Time-Lag Cross Correlation and Doppler-Derived Sea Ice Drift Velocities T. Kramer et al. https://doi.org/10.1109/TGRS.2017.2769222
- A Hausdorff-guided deep learning approach for tracking the motion of rotating Arctic ice floes A. Wu et al. https://doi.org/10.1016/j.oceaneng.2026.127007
- A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) T. Lavergne & E. Down https://doi.org/10.5194/essd-15-5807-2023
Saved (final revised paper)
Latest update: 09 Nov 2026
Short summary
Presently, sea ice drift data do not provide sufficient resolution to estimate convergence and divergence fields on a spatial scaling of a few kilometres. Our goal is to exploit recent improvements and developments in computer vision by adopting a state-of-the-art feature-tracking algorithm to derive high-resolution sea ice drift. A computationally efficient algorithm has been considered, tuned and compared with other available feature-tracking algorithms.
Presently, sea ice drift data do not provide sufficient resolution to estimate convergence and...
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