Fusing AMSR-E and QuikSCAT Imagery for Improved Sea Ice Recognition

Yu, Peter; Clausi, David A.; Howell, Stephen E. L.

doi:10.1109/tgrs.2009.2013632

Cited by 29 publications

(15 citation statements)

References 30 publications

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“…The detection of autumn refreeze has been attempted only in one case [33] as this change is less distinctive in Ku-band. Beside its use for seasonal snowmelt detection, SeaWinds has been also investigated at high latitudes for glaciological studies over Greenland [7,8], lake ice phenology [50] and sea ice monitoring [9,10,51].…”

Section: Discussionmentioning

confidence: 99%

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Ten Years of SeaWinds on QuikSCAT for Snow Applications

Bartsch

2010

Remote Sensing

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The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages.

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Section: Discussionmentioning

confidence: 99%

“…Changes in river discharge have been reported from several river basins within the last decades [4][5][6]. The focus of this review is on unglaciated terrain, but several studies also demonstrated the use of Ku-band scatterometer data for ice cap [7,8] and seasonal sea ice monitoring [9,10].…”

Section: Introductionmentioning

confidence: 98%

Ten Years of SeaWinds on QuikSCAT for Snow Applications

Bartsch

2010

Remote Sensing

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“…Previous studies have used QuikSCAT and AMSR-E data to complement each other for melt onset detection in order to reduce uncertainty and improve detection of snow cover and melt (Foster et al 2011). Combining AMSR-E and QuikSCAT has also been found to improve sea ice mapping (Yu et al 2009).…”

Section: Passive and Active Microwave Datamentioning

confidence: 99%

Early snowmelt events: detection, distribution, and significance in a major sub-arctic watershed

Semmens

Ramage

Bartsch

et al. 2013

Environ. Res. Lett.

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High latitude drainage basins are experiencing higher average temperatures, earlier snowmelt onset in spring, and an increase in rain on snow (ROS) events in winter, trends that climate models project into the future. Snowmelt-dominated basins are most sensitive to winter temperature increases that influence the frequency of ROS events and the timing and duration of snowmelt, resulting in changes to spring runoff. Of specific interest in this study are early melt events that occur in late winter preceding melt onset in the spring. The study focuses on satellite determination and characterization of these early melt events using the Yukon River Basin (Canada/USA) as a test domain. The timing of these events was estimated using data from passive (Advanced Microwave Scanning Radiometer-EOS (AMSR-E)) and active (SeaWinds on Quick Scatterometer (QuikSCAT)) microwave remote sensors, employing detection algorithms for brightness temperature (AMSR-E) and radar backscatter (QuikSCAT). The satellite detected events were validated with ground station meteorological and hydrological data, and the spatial and temporal variability of the events across the entire river basin was characterized. Possible causative factors for the detected events, including ROS, fog, and positive air temperatures, were determined by comparing the timing of the events to parameters from SnowModel and National Centers for Environmental Prediction North American Regional Reanalysis (NARR) outputs, and weather station data. All melt events coincided with above freezing temperatures, while a limited number corresponded to ROS (determined from SnowModel and ground data) and a majority to fog occurrence (determined from NARR). The results underscore the significant influence that warm air intrusions have on melt in some areas and demonstrate the large temporal and spatial variability over years and regions. The study provides a method for melt detection and a baseline from which to assess future change.

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“…Similar trends are observed in Cband sea-ice maps. We suggest that during periods of rapid ice growth and retreat when the ice signature evolves rapidly, active (radars), and passive sensors have different sensitivities to diffuse edges, and that by coupling scatterometer and radiometer data, the accuracy of ice maps and ice classifications can be improved [55].…”

Section: A Sea Ice Extent Mappingmentioning

confidence: 99%

Polar Applications of Spaceborne Scatterometers

Long

2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Wind scatterometers were originally developed for observation of near-surface winds over the ocean. They retrieve wind indirectly by measuring the normalized radar cross section (σ o ) of the surface, and estimating the wind via a geophysical model function relating σ o to the vector wind. The σ o measurements have proven to be remarkably capable in studies of the polar regions where they can map snow cover; detect the freeze/thaw state of forest, tundra, and ice; map and classify sea ice; and track icebergs. Further, a long time series of scatterometer σ o observations is available to support climate studies. In addition to fundamental scientific research, scatterometer data are operationally used for sea-ice mapping to support navigation. Scatterometers are, thus, invaluable tools for monitoring the polar regions. In this paper, a brief review of some of the polar applications of spaceborne wind scatterometer data is provided. The paper considers both C-band and Ku-band scatterometers, and the relative merits of fan-beam and pencil-beam scatterometers in polar remote sensing are discussed.

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Fusing AMSR-E and QuikSCAT Imagery for Improved Sea Ice Recognition

Cited by 29 publications

References 30 publications

Ten Years of SeaWinds on QuikSCAT for Snow Applications

Ten Years of SeaWinds on QuikSCAT for Snow Applications

Early snowmelt events: detection, distribution, and significance in a major sub-arctic watershed

Polar Applications of Spaceborne Scatterometers

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