Traversing Sea Ice—Linking Surface Roughness and Ice Trafficability Through SAR Polarimetry and Interferometry

Dammann, Dyre Oliver; Eicken, Hajo; Mahoney, Andrew R.; Saiet, Eyal; Meyer, Franz J.; George, J. C.

doi:10.1109/jstars.2017.2764961

Cited by 25 publications

(57 citation statements)

References 72 publications

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“…The application of InSAR-derived absolute ridge height has been demonstrated in past work [20,31], as well as the dm-scale accuracy of TanDEM-X-derived ice topography [34]. Depending on coherence and baselines, InSAR is therefore most relevant for evaluation of larger ridges several meters in height as opposed to rafting and other cm-scale changes in ice roughness.…”

Section: Discussionmentioning

confidence: 99%

“…Besides resulting in stable anchoring points, ridges and the ice surface morphology can hamper the use of parts of the coastal ice by inhibiting transportation. Therefore, ample studies have contributed to the understanding of landfast ice dynamics [18], ridge formation [8,11], and how ridges impact trafficability of the ice [19][20][21]. Through such work, ridge height has been measured using ground-based electromagnetic conductivity [21], helicopter-borne laser profilers [22], airborne laser scanners [23], structure-from-motion [20], and spaceborne altimeters [24].…”

Section: Introductionmentioning

confidence: 99%

“…TanDEM-X is a SAR constellation that allows for bistatic acquisition of images with temporal baselines in the order of milliseconds. This data has been used to assess the surface morphology and height of grounded ridges in the landfast ice near Utqiaġvik [20,31], sea ice in the Fram Strait [32,33], and icebergs in the Southern Sea [34].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Landfast Sea Ice Ridging near UtqiaġVik Alaska Using TanDEM-X Interferometry

et al. 2020

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Seasonal landfast sea ice stretches along most Arctic coastlines and serves as a platform for community travel and subsistence, industry operations, and as a habitat for marine mammals. Landfast ice can feature smooth ice and areas of m-scale roughness in the form of pressure ridges. Such ridges can significantly hamper trafficability, but if grounded can also serve to stabilize the shoreward ice. We investigate the use of synthetic aperture radar interferometry (InSAR) to assess the formation and movement of ridges in the landfast sea ice near Utqiaġvik, Alaska. The evaluation is based on the InSAR-derived surface elevation change between two TanDEM-X bistatic image pairs acquired during January 2012. We compare the results with backscatter intensity, coastal radar data, and SAR-derived ice drift and evaluate the utility of this approach and its relevance for evaluation of ridge properties, as well as landfast sea ice evolution, dynamics, and stability. Data Curation, M.M.; Writing-Original Draft Preparation, M.M.; Writing-Review & Editing

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating Landfast Sea Ice Ridging near UtqiaġVik Alaska Using TanDEM-X Interferometry

et al. 2020

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“…; Dammann et al. ). Bottomfast ice will not be subject to vertical motion and will exhibit an interferometric signature very similar to the surrounding land area.…”

Section: Methodsmentioning

confidence: 98%

“…Thus, the use of InSAR over sea ice is primarily limited to areas of otherwise (Dammert et al 1998;Morris et al 1999;Meyer et al 2011). In lagoons, floating ice will be subject to motion due to thermal expansion and contraction and vertical changes in water level that are detectable using InSAR techniques Dammann et al 2017). Bottomfast ice will not be subject to vertical motion and will exhibit an interferometric signature very similar to the surrounding land area.…”

Section: Methodsmentioning

confidence: 99%

An Interferometric Synthetic Aperture Radar (InSAR) Habitat Suitability Model to Identify Overwinter Conditions for Coregonine Whitefishes in Arctic Lagoons

et al. 2018

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Lagoons provide critical habitats for many fishes, including coregonine whitefishes, which are a mainstay in many subsistence fisheries of rural communities in Arctic Alaska. Despite their importance, little is known about the overwintering habits of whitefishes in Arctic Alaska due to the challenges associated with sampling during winter. We developed a habitat suitability (HS) model to understand the potential range of physical conditions that whitefishes experience during the Arctic winter, using three indicator lagoons that represent a range of environmental characteristics. The HS model was built using a three‐step approach. First, remote sensing that uses interferometric synthetic aperture radar (InSAR) identified areas of floating and bottomfast ice. Second, through in‐field ground‐truthing, we confirmed the presence and quality of liquid water (water depth, temperature, and dissolved oxygen) beneath the ice cover. Third, we assessed the suitability of that liquid water as habitat for whitefishes based on published literature and expert interpretation of water quality parameters. InSAR determined that 0, 65.4, and 88.2% of the three lagoons were composed of floating ice corresponding with areas of liquid water beneath a layer of ice. The HS model indicated that all three lagoons had reduced suitability as whitefish habitat in winter than in summer due to the loss of habitat because of the presence of bottomfast ice and a reduction in the quality of liquid water due to cold temperatures, high salinities, and low dissolved oxygen levels. However, only the shallowest lagoon had lethal conditions and zero suitability as whitefish habitat. The methods outlined here provide a simple, cost‐effective method to identify habitats that consistently provide critical winter habitat and integrate remote sensing in a HS model framework.

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Characterizing winter landfast sea-ice surface roughness in the Canadian Arctic Archipelago using Sentinel-1 synthetic aperture radar and the Multi-angle Imaging SpectroRadiometer

et al. 2020

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Abstract Two satellite datasets are used to characterize winter landfast first-year sea-ice (FYI), deformed FYI (DFYI) and multiyear sea-ice (MYI) roughness in the Canadian Arctic Archipelago (CAA): (1) optical Multi-angle Imaging SpectroRadiometer (MISR) and (2) synthetic aperture radar Sentinel-1. The Normalized Difference Angular Index (NDAI) roughness proxy derived from MISR, and backscatter from Sentinel-1 are intercompared. NDAI and backscatter are also compared to surface roughness derived from an airborne LiDAR track covering a subset of FYI and MYI (no DFYI). Overall, NDAI and backscatter are significantly positively correlated when all ice type samples are considered. When individual ice types are evaluated, NDAI and backscatter are only significantly correlated for DFYI. Both NDAI and backscatter are correlated with LiDAR-derived roughness (r = 0.71 and r = 0.74, respectively). The relationship between NDAI and roughness is greater for MYI than FYI, whereas for backscatter and ice roughness, the relationship is greater for FYI than MYI. Linear regression models are created for the estimation of FYI and MYI roughness from NDAI, and FYI roughness from backscatter. Results suggest that using a combination of Sentinel-1 backscatter for FYI and MISR NDAI for MYI may be optimal for mapping winter sea-ice roughness in the CAA.

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Traversing Sea Ice—Linking Surface Roughness and Ice Trafficability Through SAR Polarimetry and Interferometry

Cited by 25 publications

References 72 publications

Evaluating Landfast Sea Ice Ridging near UtqiaġVik Alaska Using TanDEM-X Interferometry

Evaluating Landfast Sea Ice Ridging near UtqiaġVik Alaska Using TanDEM-X Interferometry

An Interferometric Synthetic Aperture Radar (InSAR) Habitat Suitability Model to Identify Overwinter Conditions for Coregonine Whitefishes in Arctic Lagoons

Characterizing winter landfast sea-ice surface roughness in the Canadian Arctic Archipelago using Sentinel-1 synthetic aperture radar and the Multi-angle Imaging SpectroRadiometer

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