Comparison of SeaWinds Backscatter Imaging Algorithms

Long, D.G.

doi:10.1109/jstars.2016.2626966

Cited by 20 publications

(9 citation statements)

References 43 publications

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“…Concerning increased spatial resolution and near-coastal processing, the exact knowledge of the SRF is important [87], [88], as well as the availability of full-resolution (nonaccumulated) samples. It is essential that the fore, mid, and aft beams view the same area, i.e., view the same wind vector or land cell.…”

Section: Discussionmentioning

confidence: 99%

Scientific Developments and the EPS-SG Scatterometer

Stoffelen

Aaboe

Calvet

et al. 2017

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

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The second-generation exploitation of meteorological satellite polar system (EPS-SG) C-band-wavelength scatterometer instrument (called SCA), planned for launch in 2022, has a direct heritage from the successful advanced scatterometer (ASCAT) flown on the current EPS satellites. In addition, SCA will represent three major innovations with respect to ASCAT, namely: 1) Cross polarization and horizontal copolarization; 2) a nominal spatial resolution of 25 km; and 3) 20% greater spatial coverage than AS-CAT. The associated expected science and application benefits that led the SCA design are discussed with respect to ocean, land, and sea ice applications for near-real time, climate monitoring, and research purposes. Moreover, an option to implement an ocean Doppler capability to retrieve the ocean motion vector is briefly discussed as well. In conclusion, the SCA instrument innovations are well set to provide timely benefits in all the main application areas of the scatterometer (winds, soil moisture, sea ice) and can be expected to contribute to new and more sophisticated meteorological, oceanographic, land, sea ice, and climate services in the forthcoming SCA era.

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Section: Discussionmentioning

confidence: 99%

Scientific Developments and the EPS-SG Scatterometer

Stoffelen

Aaboe

Calvet

et al. 2017

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

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“…A transfer of the QuikScat approach has been nevertheless tested in order to investigate the potential for the production if a longer data record. The so called 'egg' product has been selected (4.4km nominal resolution, Long (2017)). The Oceansat-2 (OSCAT BYU) product starts 5th November 2010 and ends 20th February 2013.…”

Section: Ku-band Scatterometermentioning

confidence: 99%

Towards long-term records of rain-on-snow events across the Arctic from satellite data

Bartsch¹,

Bergstedt²,

Pointner³

et al. 2022

Preprint

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Abstract. Rain-on-Snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snow pack properties are changing and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. Specifically, satellite microwave observations have been shown to provide insight into known events. Only Ku-band radar (scatterometer) has been applied so far across the entire Arctic. Data availability at this frequency is limited, however. The utility of other frequencies from passive and active systems need to be explored to develop a concept for long-term monitoring. Active (radar) records have been shown to capture the associated snow structure change based on time series analyses. This approach is also applicable when data gaps exist and bears capabilities to evaluate the impact severity of events. Active as well as passive microwave sensors can also detect wet snow at the timing of a ROS, if an acquisition is available. Wet snow retrieval methodology is, however, rather mature, compared to identification of snow structure change which needs consideration of ambiguous scattering behaviour. C-band radar is of special interest due to good data availability including a range of nominal spatial resolution (10 m–12.5 km) A combined approach is therefore considered and tested for C-band (active, snow structure change) and L-band (passive, wet snow). Results were compared to in situ observations (snow pit records, caribou migration data) and Ku-band products. Ice crusts were found in the snow pack after detected events. The more crusts (events) the higher the winter season backscatter increase at C-band. ROS events captured on the Yamal and Seward peninsulas have had severe impacts on reindeer and caribou, respectively, due to crust formation. Temperature dependence of C-band backscatter observable down to -40 °C is identified as a major issue for ROS retrieval, but can be addressed by combination with passive microwave wet snow indicators (demonstrated for Metop ASCAT and SMOS). Synthetic Aperture Radar (SAR) from specifically Sentinel-1 (C-band) is promising regarding ice layer identification at better spatial details for all available polarizations. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record, but the consideration of performance differences due to spatial and temporal cover as well as microwave frequency is crucial. Retrieval is most robust north of 65° N, in the tundra biome, where records can be used to identify extremes and to apply the results for impact studies at regional scale.

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“…We used σ 0 data from enhanced resolution images processed with the Scatterometer Image Reconstruction (SIR) technique, which combines multiple σ 0 measurements from a single beam, numerous azimuth angles, and several orbit-passes across the imaging period (Early and Long, 2001;Long and Hicks, 2010). The Spatial Response Function of the measurements allows for higher resolution (Long, 2017). The resulting images represent a nonlinear weighted average of the measurements (Early and Long, 2001), based on an implicit assumption that the surface properties remain constant throughout the imaging period.…”

Section: Scatterometer Datamentioning

confidence: 99%

Spatiotemporal change analysis for snowmelt over the Antarctic ice shelves using scatterometers

Luis¹,

Alam²,

Jawak³

2022

Front. Remote Sens.

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Using Scatterometer-based backscatter data, the spatial and temporal melt dynamics of Antarctic ice shelves were tracked from 2000 to 2018. We constructed melt onset and duration maps for the whole Antarctic ice shelves using a pixel-based, adaptive threshold approach based on backscatter during the transition period between winter and summer. We explore the climatic influences on the spatial extent and timing of snowmelt using meteorological data from automatic weather stations and investigate the climatic controls on the spatial extent and timing of snowmelt. Melt extent usually starts in the latter week of November, peaks in the end of December/January, and vanishes in the first/second week of February on most ice shelves. On the Antarctic Peninsula (AP), the average melt was 70 days, with the melt onset on 20 November for almost 50% of the region. In comparison to the AP, the Eastern Antarctic experienced less melt, with melt lasting 40–50 days. For the Larsen-C, Shackleton, Amery, and Fimbul ice shelf, there was a substantial link between melt area and air temperature. A significant correlation is found between increased temperature advection and high melt area for the Amery, Shackleton, and Larsen-C ice shelves. The time series of total melt area showed a decreasing trend of −196 km2/yr which was statistical significant at 97% interval. The teleconnections discovered between melt area and the combined anomalies of Southern Annular Mode and Southern Oscillation Index point to the high southern latitudes being coupled to the global climate system. The most persistent and intensive melt occurred on the AP, West Ice Shelf, Shackleton Ice Shelf, and Amery Ice Shelf, which should be actively monitored for future stability.

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Comparison of SeaWinds Backscatter Imaging Algorithms

Cited by 20 publications

References 43 publications

Scientific Developments and the EPS-SG Scatterometer

Scientific Developments and the EPS-SG Scatterometer

Towards long-term records of rain-on-snow events across the Arctic from satellite data

Spatiotemporal change analysis for snowmelt over the Antarctic ice shelves using scatterometers

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