Polar Applications of Spaceborne Scatterometers

Long, D.G.

doi:10.1109/jstars.2016.2629418

Cited by 42 publications

(34 citation statements)

References 127 publications

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“…This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ With respect to the cryosphere, scatterometer data are used in the retrieval of several sea ice parameters, such as sea ice coverage, type, and drift [17], as illustrated in Fig. 2.…”

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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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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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“…Within single chart pairs, the equivalence between the pixels varies from 84.7% to 100%, but it is mostly above 98%. (12,100,530) This high agreement between the two charts gives further confidence on the reliability of our algorithms and procedures for the MODIS SIT and OWSI chart processing. It is noted that the two charts are fully independent in the estimation of the sea ice properties.…”

Section: Comparison Of Modis Sit and Owsi Chartsmentioning

confidence: 71%

“…It has been also used for the estimation of snow thickness on sea ice and ice surface temperature, e.g., [6], mapping of thin ice areas [7][8][9] or estimation of thin ice thickness, e.g., [10,11]. Scatterometer data has been used for mapping of sea ice extent and ice edge location, and for sea ice typing [12]. All of the sensors have been used for estimation of beginnings and endings of different sea ice thermodynamic regimes, e.g., [12][13][14], and sea ice drift based on tracking of temporal changes in the imagery, e.g., [12,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Scatterometer data has been used for mapping of sea ice extent and ice edge location, and for sea ice typing [12]. All of the sensors have been used for estimation of beginnings and endings of different sea ice thermodynamic regimes, e.g., [12][13][14], and sea ice drift based on tracking of temporal changes in the imagery, e.g., [12,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data

Mäkynen

Karvonen

2017

Remote Sensing

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Abstract:We have developed algorithms and procedures for calculating daily sea ice thickness (SIT) and open water-sea ice (OWSI) charts, based on the Moderate Resolution Imaging Spectroradiometer (MODIS), ice surface temperature (IST) (night-time only), and reflectance (R) swath data, respectively. The resolution of the SIT chart is 1 km and that of the OWSI chart is 250 m. The charts are targeted to be used in development and validation of sea ice products from microwave sensor data. We improve the original MODIS cloud masks for the IST and R data, with a focus on identifying larger cloud-free areas in the data. The SIT estimation from the MODIS IST swath data follows previous studies. The daily SIT chart is composed from available swath charts by assigning daily median SIT to a pixel. The OWSI classification is simply conducted by a fixed threshold for the MODIS band 1 R. This was based on manually selected R data for various ice types in late winter, early melt, and advanced melt conditions. The composition procedures for the daily SIT and OWSI charts somewhat compensates for errors due to the undetected clouds. The SIT and OWSI charts were compared against manual ice charts by Arctic and Antarctic Research Institute in Russia and by Norwegian Meteorological Institute, respectively, and on average, a good relationship between the charts was found. Pixel-wise comparison of the SIT and OWSI charts showed very good agreement in open water vs. sea ice classification, which gives further confidence on the reliability of our algorithms. We also demonstrate usage of the MODIS OWSI and SIT charts for validation of sea ice concentration charts based on the SENTINEL-1 SAR and AMSR2 radiometer data and two different algorithms.

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“…Satellite radar instruments called wind scatterometers illuminate the Earth's surface with microwaves and measure the normalized radar cross-section (σ 0 ) of the surface [1]. While the σ 0 measurements prove useful for many applications such as iceberg tracking, ice classification, vegetation classification, and soil moisture estimation [1][2][3][4], the scatterometer's primary function is to estimate the near-surface vector wind from the ocean σ 0 measurements.…”

Section: Introductionmentioning

confidence: 99%

Ultrahigh Resolution Scatterometer Winds near Hawaii

2020

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Hawaii regional climate model (HRCM), QuikSCAT, and ASCAT wind estimates are compared in the lee of Hawaii's Big Island with the goal of understanding ultrahigh resolution (UHR) scatterometer wind retrieval capabilities in this area, which includes a reverse-flow toward the island in the lee of the predominate flow. A comparison of scatterometer measured σ 0 and model predicted σ 0 suggests that scatterometers can detect the reverse flow in the lee of the island; however, neither QuikSCAT-nor ASCAT-estimated winds consistently report this flow. Furthermore, the scatterometer UHR winds do not resolve the wind direction features predicted by the HRCM. Differences between scatterometer measured σ 0 and HRCM predicted σ 0 indicate possible error in the placement of key reverse flow features predicted by the HRCM. We find that coarse initialization fields and a large size median filter windows used in ambiguity selection can impede the accuracy of the UHR wind direction retrieval in this area, suggesting the need for further development of improved near-coastal ambiguity selection algorithms.

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Polar Applications of Spaceborne Scatterometers

Cited by 42 publications

References 127 publications

Scientific Developments and the EPS-SG Scatterometer

Scientific Developments and the EPS-SG Scatterometer

MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data

Ultrahigh Resolution Scatterometer Winds near Hawaii

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