An Analysis of Past and Future Changes in the Ice Cover of Two High-Arctic Lakes Based on Synthetic Aperture Radar (SAR) and Landsat Imagery

Cook, Timothy L.; Bradley, Raymond S.

doi:10.1657/1938-4246-42.1.9

Cited by 20 publications

(30 citation statements)

References 32 publications

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“…Segmentation results show that no ice was detected on the selected lakes on 14 July 2006, 10% ice 9 July 2007, 17% ice on 6 July 2009, 14% ice on 14 July 2010 and 9% ice on 10 July 2011. These results are acceptable considering that previous investigations of lake-ice break-up using C-band SAR assigned WCI dates on the dates when the areal fraction of open water first exceeded 90% [20]. Visual analysis of the segmentation results indicates that Lake Tusikvoak (ROI # 5, see Table 1 and Figure 1) maintains its ice cover later in the summer, being the lake with the latest WCI date.…”

Section: Water-clear-of-icesupporting

confidence: 50%

“…FO detection is complicated by the low σ° contrast between the open water and the newly formed ice [20], by the σ° sensitivity to wind speed and direction, as well as the ice structure and thickness, roughness of the ice interface, all resulting in higher returns, and snow wetness [22] that results in lower returns. On shallow lakes, the transition from an ice cover with minimum air inclusions (bubbles) at the beginning of the ice season to an ice layer with increased bubble density as ice growth progresses complicates the σ° analysis during freeze-up.…”

Section: Synthetic Aperture Radar (Sar)mentioning

confidence: 99%

“…Based on previous findings that suggest that the HV σ° does not provide the optimum discrimination between ice and water particularly at the beginning of MO [20], only HH-polarized RADARSAT-2 images were used in the current study. Yet, wind speed prior to the lakes becoming completely ice free affects the HH-polarized imagery, resulting in radar returns similar to those from open water in windy conditions.…”

Section: Water-clear-of-icementioning

confidence: 99%

“…To overcome these limitations, SAR observations provide a feasible alternative. With a history of over two decades, SAR systems have demonstrated their ability to monitor ice phenology of Arctic and sub-Arctic lakes through analysis of the σ° temporal evolution, at C-band [12,[16][17][18][19][20][21][22][23] and Ku-band frequencies [23].…”

mentioning

confidence: 99%

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Ice Freeze-up and Break-up Detection of Shallow Lakes in Northern Alaska with Spaceborne SAR

et al. 2015

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Shallow lakes, with depths less than ca. 3.5-4 m, are a ubiquitous feature of the Arctic Alaskan Coastal Plain, covering up to 40% of the land surface. With such an extended areal coverage, lakes and their ice regimes represent an important component of the cryosphere. The duration of the ice season has major implications for the regional and local climate, as well as for the physical and biogeochemical processes of the lakes. With day and night observations in all weather conditions, synthetic aperture radar (SAR) sensors provide year-round acquisitions. Monitoring the evolution of radar backscatter (σ°) is useful for detecting the timing of the beginning and end of the ice season. Analysis of the temporal evolution of C-band σ° from Advanced Synthetic Aperture Radar (ASAR) Wide Swath and RADARSAT-2 ScanSAR, with a combined frequency of acquisitions from two to five days, was employed to evaluate the potential of SAR to detect the timing of key lake-ice events. SAR observations from 2005 to 2011 were compared to outputs of the Canadian Lake Ice Model (CLIMo). Model simulations fall within similar ranges with those of the SAR observations, with a mean difference between SAR observations and model simulations of only one day for water-clear-of-ice (WCI) from 2006 to 2010. For freeze onset (FO), larger mean differences were observed. SAR analysis shows that the mean FO date for these OPEN ACCESS Remote Sens. 2015, 7 6134 shallow coastal lakes is 30 September and the mean WCI date is 5 July. Results reveal that greater variability existed in the mean FO date (up to 26 days) than in that of melt onset (MO) (up to 12 days) and in that of WCI (6 days). Additionally, this study also identifies limitations and provides recommendations for future work using C-band SAR for monitoring the lake-ice phenology of shallow Arctic lakes.

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Section: Water-clear-of-icesupporting

confidence: 50%

Section: Synthetic Aperture Radar (Sar)mentioning

confidence: 99%

Section: Water-clear-of-icementioning

confidence: 99%

mentioning

confidence: 99%

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Ice Freeze-up and Break-up Detection of Shallow Lakes in Northern Alaska with Spaceborne SAR

et al. 2015

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“…Optical remote sensing in the Arctic is limited due to polar night and often persistent cloud cover [15,16]. Active microwave radar signals, on the other hand, penetrate through cloud cover and allow for systematic monitoring of lake ice phenology [17][18][19][20]. Moreover, the difference in radar backscatter intensities between grounded (bedfast) and floating lake ice allows for mapping of these areas and estimation of the timing of grounding.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Bedfast Ice and Ice Phenology in Lakes of the Lena River Delta Using TerraSAR-X Backscatter and Coherence Time Series

et al. 2016

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Thermokarst lakes and ponds are major elements of permafrost landscapes, occupying up to 40% of the land area in some Arctic regions. Shallow lakes freeze to the bed, thus preventing permafrost thaw underneath them and limiting the length of the period with greenhouse gas production in the unfrozen lake sediments. Radar remote sensing permits to distinguish lakes with bedfast ice due to the difference in backscatter intensities from bedfast and floating ice. This study investigates the potential of a unique time series of three-year repeat-pass TerraSAR-X (TSX) imagery with high temporal (11 days) and spatial (10 m) resolution for monitoring bedfast ice as well as ice phenology of lakes in the zone of continuous permafrost in the Lena River Delta, Siberia. TSX backscatter intensity is shown to be an excellent tool for monitoring floating versus bedfast lake ice as well as ice phenology. TSX-derived timing of ice grounding and the ice growth model CLIMo are used to retrieve the ice thicknesses of the bedfast ice at points where in situ ice thickness measurements were available. Comparison shows good agreement in the year of field measurements. Additionally, for the first time, an 11-day sequential interferometric coherence time series is analyzed as a supplementary approach for the bedfast ice monitoring. The coherence time series detects most of the ice grounding as well as spring snow/ice melt onset. Overall, the results show the great value of TSX time series for monitoring Arctic lake ice and provide a basis for various applications: for instance, derivation of shallow lakes bathymetry, evaluation of winter water resources and locating fish winter habitat as well as estimation of taliks extent in permafrost.

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Remote sensing of lake and river ice

Duguay

Bernier

Gauthier

et al. 2014

Remote Sensing of the Cryosphere

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An Analysis of Past and Future Changes in the Ice Cover of Two High-Arctic Lakes Based on Synthetic Aperture Radar (SAR) and Landsat Imagery

Cited by 20 publications

References 32 publications

Ice Freeze-up and Break-up Detection of Shallow Lakes in Northern Alaska with Spaceborne SAR

Ice Freeze-up and Break-up Detection of Shallow Lakes in Northern Alaska with Spaceborne SAR

Monitoring Bedfast Ice and Ice Phenology in Lakes of the Lena River Delta Using TerraSAR-X Backscatter and Coherence Time Series

Remote sensing of lake and river ice

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