Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis
Using a case study approach, the utility of synergistic RADARSAT-2 (R2) and Sentinel-1 (S1) synthetic aperture radar (SAR) imagery is demonstrated for ocean feature signature analysis in the vicinity of the Gulf Stream. The R2 and S1 images considered are either spatially adjacent or spatially overl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-09-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2019.1662284 |
| _version_ | 1797661092188520448 |
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| author | Wesley Van Wychen Paris W. Vachon John Wolfe Katerina Biron |
| author_facet | Wesley Van Wychen Paris W. Vachon John Wolfe Katerina Biron |
| author_sort | Wesley Van Wychen |
| collection | DOAJ |
| description | Using a case study approach, the utility of synergistic RADARSAT-2 (R2) and Sentinel-1 (S1) synthetic aperture radar (SAR) imagery is demonstrated for ocean feature signature analysis in the vicinity of the Gulf Stream. The R2 and S1 images considered are either spatially adjacent or spatially overlapping, and were quasi-simultaneously collected (i.e., within minutes of each other). Spatially adjacent R2 and S1 imagery allows ocean feature signatures to be delineated over large spatial areas, while spatially overlapping R2 and S1 imagery collected within short time intervals provides independent ‘looks’ at the same ocean features. This permits determination of the surface displacement of features, potentially leading to improved classification of the origin of ocean feature signatures (quasi-stationary features are likely related to sea surface temperature fronts, while mobile features are likely related to atmospheric conditions). Further, we demonstrate how the use of S1 Level-2 products (i.e. radial velocity datasets) can be leveraged as contextual data to improve the interpretation and classification of ocean feature signatures extracted from R2 imagery. Despite the straight-forward approach taken here, this work demonstrates that there are practical, real-world applications that would benefit from exploiting these on-going imaging opportunities in operational environments. |
| first_indexed | 2024-03-11T18:40:44Z |
| format | Article |
| id | doaj.art-0022e35c01d24dbaaa80509472d2f43e |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:40:44Z |
| publishDate | 2019-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-0022e35c01d24dbaaa80509472d2f43e2023-10-12T13:36:23ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712019-09-0145559160210.1080/07038992.2019.16622841662284Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature AnalysisWesley Van Wychen0Paris W. Vachon1John Wolfe2Katerina Biron3Department of Geography and Environmental Management, University of WaterlooDefence Research and Development Canada, Ottawa Research CentreDefence Research and Development Canada, Ottawa Research CentreDefence Research and Development Canada, Ottawa Research CentreUsing a case study approach, the utility of synergistic RADARSAT-2 (R2) and Sentinel-1 (S1) synthetic aperture radar (SAR) imagery is demonstrated for ocean feature signature analysis in the vicinity of the Gulf Stream. The R2 and S1 images considered are either spatially adjacent or spatially overlapping, and were quasi-simultaneously collected (i.e., within minutes of each other). Spatially adjacent R2 and S1 imagery allows ocean feature signatures to be delineated over large spatial areas, while spatially overlapping R2 and S1 imagery collected within short time intervals provides independent ‘looks’ at the same ocean features. This permits determination of the surface displacement of features, potentially leading to improved classification of the origin of ocean feature signatures (quasi-stationary features are likely related to sea surface temperature fronts, while mobile features are likely related to atmospheric conditions). Further, we demonstrate how the use of S1 Level-2 products (i.e. radial velocity datasets) can be leveraged as contextual data to improve the interpretation and classification of ocean feature signatures extracted from R2 imagery. Despite the straight-forward approach taken here, this work demonstrates that there are practical, real-world applications that would benefit from exploiting these on-going imaging opportunities in operational environments.http://dx.doi.org/10.1080/07038992.2019.1662284 |
| spellingShingle | Wesley Van Wychen Paris W. Vachon John Wolfe Katerina Biron Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis Canadian Journal of Remote Sensing |
| title | Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis |
| title_full | Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis |
| title_fullStr | Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis |
| title_full_unstemmed | Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis |
| title_short | Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis |
| title_sort | synergistic radarsat 2 and sentinel 1 sar images for ocean feature analysis |
| url | http://dx.doi.org/10.1080/07038992.2019.1662284 |
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