Estimating Arctic sea ice melt pond fraction and assessing ice type separability during advanced melt
Arctic sea ice is rapidly declining in extent, thickness, volume and age, with the majority of the decline in extent observed at the end of the melt season. Advanced melt is a thermodynamic regime and is characterized by the formation of melt ponds on the sea ice surface, which have a lower surface...
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Remote Sensing
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ftuvicpubl:oai:dspace.library.uvic.ca:1828/9313 2023-05-15T13:11:44+02:00 Estimating Arctic sea ice melt pond fraction and assessing ice type separability during advanced melt Nasonova, Sasha Scharien, Randy 2017 application/pdf https://dspace.library.uvic.ca//handle/1828/9313 English en eng Remote Sensing Nasonova, S.; Scharien, R.K.; Haas, C.; Howell, S.E.L. Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice. Remote Sens. 2018, 10, 37. https://dspace.library.uvic.ca//handle/1828/9313 Available to the World Wide Web Arctic Sea ice Synthetic aperture radar Compact polarimetry Melt pond fraction Object-based image analysis Remote sensing Thesis 2017 ftuvicpubl 2022-05-19T06:12:25Z Arctic sea ice is rapidly declining in extent, thickness, volume and age, with the majority of the decline in extent observed at the end of the melt season. Advanced melt is a thermodynamic regime and is characterized by the formation of melt ponds on the sea ice surface, which have a lower surface albedo (0.2-0.4) than the surrounding ice (0.5-0.7) allowing more shortwave radiation to enter the system. The loss of multiyear ice (MYI) may have a profound impact on the energy balance of the system because melt ponds on first-year ice (FYI) comprise up to 70% of the ice surface during advanced melt, compared to 40% on MYI. Despite the importance of advanced melt to the ocean-sea ice-atmosphere system, advanced melt and the extent to which winter conditions influence it remain poorly understood due to the highly dynamic nature of melt pond formation and evolution, and a lack of reliable observations during this time. In order to establish quantitative links between winter and subsequent advanced melt conditions, and assess the effects of scale and choice of aggregation features on the relationships, three data aggregation approaches at varied spatial scales were used to compare high resolution satellite GeoEye-1 optical images of melt pond covered sea ice to winter airborne laser scanner surface roughness and electromagnetic induction sea ice thickness measurements. The findings indicate that winter sea ice thickness has a strong association with melt pond fraction (fp) for FYI and MYI. FYI winter surface roughness is correlated with fp, whereas for MYI no association with fp was found. Satellite-borne synthetic aperture radar (SAR) data are heavily relied upon for sea ice observation; however, during advanced melt the reliability of observations is reduced. In preparation for the upcoming launch of the RADARSAT Constellation Mission (RCM), the Kolmogorov-Smirnov (KS) statistical test was used to assess the ability of simulated RCM parameters and grey level co-occurrence matrix (GLCM) derived texture features to ... Thesis albedo Arctic Arctic Sea ice University of Victoria (Canada): UVicDSpace Arctic |
institution |
Open Polar |
collection |
University of Victoria (Canada): UVicDSpace |
op_collection_id |
ftuvicpubl |
language |
English |
topic |
Arctic Sea ice Synthetic aperture radar Compact polarimetry Melt pond fraction Object-based image analysis Remote sensing |
spellingShingle |
Arctic Sea ice Synthetic aperture radar Compact polarimetry Melt pond fraction Object-based image analysis Remote sensing Nasonova, Sasha Estimating Arctic sea ice melt pond fraction and assessing ice type separability during advanced melt |
topic_facet |
Arctic Sea ice Synthetic aperture radar Compact polarimetry Melt pond fraction Object-based image analysis Remote sensing |
description |
Arctic sea ice is rapidly declining in extent, thickness, volume and age, with the majority of the decline in extent observed at the end of the melt season. Advanced melt is a thermodynamic regime and is characterized by the formation of melt ponds on the sea ice surface, which have a lower surface albedo (0.2-0.4) than the surrounding ice (0.5-0.7) allowing more shortwave radiation to enter the system. The loss of multiyear ice (MYI) may have a profound impact on the energy balance of the system because melt ponds on first-year ice (FYI) comprise up to 70% of the ice surface during advanced melt, compared to 40% on MYI. Despite the importance of advanced melt to the ocean-sea ice-atmosphere system, advanced melt and the extent to which winter conditions influence it remain poorly understood due to the highly dynamic nature of melt pond formation and evolution, and a lack of reliable observations during this time. In order to establish quantitative links between winter and subsequent advanced melt conditions, and assess the effects of scale and choice of aggregation features on the relationships, three data aggregation approaches at varied spatial scales were used to compare high resolution satellite GeoEye-1 optical images of melt pond covered sea ice to winter airborne laser scanner surface roughness and electromagnetic induction sea ice thickness measurements. The findings indicate that winter sea ice thickness has a strong association with melt pond fraction (fp) for FYI and MYI. FYI winter surface roughness is correlated with fp, whereas for MYI no association with fp was found. Satellite-borne synthetic aperture radar (SAR) data are heavily relied upon for sea ice observation; however, during advanced melt the reliability of observations is reduced. In preparation for the upcoming launch of the RADARSAT Constellation Mission (RCM), the Kolmogorov-Smirnov (KS) statistical test was used to assess the ability of simulated RCM parameters and grey level co-occurrence matrix (GLCM) derived texture features to ... |
author2 |
Scharien, Randy |
format |
Thesis |
author |
Nasonova, Sasha |
author_facet |
Nasonova, Sasha |
author_sort |
Nasonova, Sasha |
title |
Estimating Arctic sea ice melt pond fraction and assessing ice type separability during advanced melt |
title_short |
Estimating Arctic sea ice melt pond fraction and assessing ice type separability during advanced melt |
title_full |
Estimating Arctic sea ice melt pond fraction and assessing ice type separability during advanced melt |
title_fullStr |
Estimating Arctic sea ice melt pond fraction and assessing ice type separability during advanced melt |
title_full_unstemmed |
Estimating Arctic sea ice melt pond fraction and assessing ice type separability during advanced melt |
title_sort |
estimating arctic sea ice melt pond fraction and assessing ice type separability during advanced melt |
publisher |
Remote Sensing |
publishDate |
2017 |
url |
https://dspace.library.uvic.ca//handle/1828/9313 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
albedo Arctic Arctic Sea ice |
genre_facet |
albedo Arctic Arctic Sea ice |
op_relation |
Nasonova, S.; Scharien, R.K.; Haas, C.; Howell, S.E.L. Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice. Remote Sens. 2018, 10, 37. https://dspace.library.uvic.ca//handle/1828/9313 |
op_rights |
Available to the World Wide Web |
_version_ |
1766248755328188416 |