Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1
Changing Arctic sea-ice extent and melt season duration, and increasing economic interest in the Arctic have prompted the need for enhanced marine ecosystem studies and improvements to dynamical and forecast models. Sea-ice melt pond fraction fp has been shown to be correlated with the September min...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2018-07-01
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| Series: | Annals of Glaciology |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S026030551700043X/type/journal_article |
| _version_ | 1811157302577725440 |
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| author | RK Scharien R Segal JJ Yackel SEL Howell S Nasonova |
| author_facet | RK Scharien R Segal JJ Yackel SEL Howell S Nasonova |
| author_sort | RK Scharien |
| collection | DOAJ |
| description | Changing Arctic sea-ice extent and melt season duration, and increasing economic interest in the Arctic have prompted the need for enhanced marine ecosystem studies and improvements to dynamical and forecast models. Sea-ice melt pond fraction fp has been shown to be correlated with the September minimum ice extent due to its impact on ice albedo and heat uptake. Ice forecasts should benefit from knowledge of fp as melt ponds form several months in advance of ice retreat. This study goes further back by examining the potential to predict fp during winter using backscatter data from the commonly available Sentinel-1 synthetic aperture radar. An object-based image analysis links the winter and spring thermodynamic states of first-year and multiyear sea-ice types. Strong correlations between winter backscatter and spring fp, detected from high-resolution visible to near infrared imagery, are observed, and models for the retrieval of fp from Sentinel-1 data are provided (r2 ≥ 0.72). The models utilize HH polarization channel backscatter that is routinely acquired over the Arctic from the two-satellite Sentinel-1 constellation mission, as well as other past, current and future SAR missions operating in the same C-band frequency. Predicted fp is generally representative of major ice types first-year ice and multiyear ice during the stage in seasonal melt pond evolution where fp is closely related to spatial variations in ice topography. |
| first_indexed | 2024-04-10T05:04:59Z |
| format | Article |
| id | doaj.art-92a4dac683d64abb8878138ff1715f37 |
| institution | Directory Open Access Journal |
| issn | 0260-3055 1727-5644 |
| language | English |
| last_indexed | 2024-04-10T05:04:59Z |
| publishDate | 2018-07-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Annals of Glaciology |
| spelling | doaj.art-92a4dac683d64abb8878138ff1715f372023-03-09T12:27:34ZengCambridge University PressAnnals of Glaciology0260-30551727-56442018-07-015914816210.1017/aog.2017.43Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1RK Scharien0R Segal1JJ Yackel2SEL Howell3S Nasonova4Department of Geography, University of Victoria, Canada. E-mail:Department of Geography, University of Victoria, Canada. E-mail:Department of Geography, University of Calgary, Cryosphere Climate Research Group, CanadaClimate Research Division, Environment and Climate Change Canada, Toronto, CanadaDepartment of Geography, University of Victoria, Canada. E-mail:Changing Arctic sea-ice extent and melt season duration, and increasing economic interest in the Arctic have prompted the need for enhanced marine ecosystem studies and improvements to dynamical and forecast models. Sea-ice melt pond fraction fp has been shown to be correlated with the September minimum ice extent due to its impact on ice albedo and heat uptake. Ice forecasts should benefit from knowledge of fp as melt ponds form several months in advance of ice retreat. This study goes further back by examining the potential to predict fp during winter using backscatter data from the commonly available Sentinel-1 synthetic aperture radar. An object-based image analysis links the winter and spring thermodynamic states of first-year and multiyear sea-ice types. Strong correlations between winter backscatter and spring fp, detected from high-resolution visible to near infrared imagery, are observed, and models for the retrieval of fp from Sentinel-1 data are provided (r2 ≥ 0.72). The models utilize HH polarization channel backscatter that is routinely acquired over the Arctic from the two-satellite Sentinel-1 constellation mission, as well as other past, current and future SAR missions operating in the same C-band frequency. Predicted fp is generally representative of major ice types first-year ice and multiyear ice during the stage in seasonal melt pond evolution where fp is closely related to spatial variations in ice topography.https://www.cambridge.org/core/product/identifier/S026030551700043X/type/journal_articlemelt–surfaceremote sensingsea ice |
| spellingShingle | RK Scharien R Segal JJ Yackel SEL Howell S Nasonova Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1 Annals of Glaciology melt–surface remote sensing sea ice |
| title | Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1 |
| title_full | Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1 |
| title_fullStr | Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1 |
| title_full_unstemmed | Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1 |
| title_short | Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1 |
| title_sort | linking winter and spring thermodynamic sea ice states at critical scales using an object based image analysis of sentinel 1 |
| topic | melt–surface remote sensing sea ice |
| url | https://www.cambridge.org/core/product/identifier/S026030551700043X/type/journal_article |
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