scholarly journals Sea‐ice detection from near‐nadir Ku‐band echoes from CFOSAT/SWIM scatterometer

2022 ◽  
Author(s):  
Charles Peureux ◽  
Nicolas Longépé ◽  
Alexis Mouche ◽  
Céline Tison ◽  
Cédric Tourain ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ice Detection ◽  
Ku Band

scholarly journals Large-scale inverse Ku-band backscatter modeling of sea ice

2003 ◽  
Vol 41 (8) ◽  
pp. 1821-1833 ◽  
Author(s):  
Q.P. Remund ◽  
D.G. Long
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Ku Band

Intercomparison of Arctic Sea Ice Backscatter and Ice Type Classification Using Ku-Band and C-Band Scatterometers

2021 ◽  
pp. 1-18
Author(s):  
Zhilun Zhang ◽  
Yining Yu ◽  
Mohammed Shokr ◽  
Xinqing Li ◽  
Yufang Ye ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Type Classification ◽  
Ku Band

NOC GNSS-R Global Ocean Wind Speed and Sea-Ice Products

2020 ◽  
Author(s):  
Matthew Hammond ◽  
Giuseppe Foti ◽  
Christine Gommenginger ◽  
Meric Srokosz ◽  
Martin Unwin ◽  
...  
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Global Ocean ◽  
Retrieval Algorithm ◽  
Navigation Satellite ◽  
Validation Data ◽  
Global Navigation ◽  
Ice Detection ◽  
Global Navigation Satellite ◽  
Ocean Wind

<p>Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from Global Navigation Satellite Systems, which have been reflected off the Earth’s surface. Using GNSS-R data collected by the UK TechDemoSat-1 (TDS-1) between 2014 and 2018, the National Oceanography Centre (NOC) has developed a GNSS-R wind speed retrieval algorithm called the Calibrated Bistatic Radar Equation (C-BRE), which now features updated data quality control mechanisms including flagging of radio frequency interference (RFI) and sea-ice detection based on the GNSS-R waveform. Here we present an assessment of the performance of the latest NOC GNSS-R ocean wind speed and sea-ice products (NOC C-BRE v1.0) using validation data from the ECMWF ERA-5 re-analysis model output. Results show the capability of spaceborne GNSS-R sensors for accurate wind speed retrieval and sea-ice detection. Additionally, ground-processed Galileo returns collected by TDS-1 are examined and the geophysical sensitivity of reflected Galileo data to surface parameters is investigated. Preliminary results demonstrate the feasibility of spaceborne GNSS-R instruments receiving a combination of GNSS signals transmitted by multiple navigation systems, which offers the opportunity for frequent, high-quality ocean wind and sea-ice retrievals at low relative cost. Other advancements in GNSS-R technology are represented by future mission concepts such as HydroGNSS, a proposed ESA Scout mission opportunity by SSTL offering support for enhanced retrieval capabilities exploiting dual polarisation, dual frequency, and coherent reflected signal reception.</p>

scholarly journals An intercomparison between AMSR-E snow-depth and satellite C- and Ku-band radar backscatter data for Antarctic sea ice

2011 ◽  
Vol 52 (57) ◽  
pp. 279-290 ◽  
Author(s):  
Stefan Kern ◽  
Burcu Ozsoy-Cicek ◽  
Sascha Willmes ◽  
Marcel Nicolaus ◽  
Christian Haas ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Weddell Sea ◽  
Late Winter ◽  
First Year ◽  
Radar Backscatter ◽  
Antarctic Sea Ice ◽  
Ridged Ice ◽  
Ice Fraction ◽  
Ku Band

AbstractAdvanced Microwave Scanning Radiometer (AMSR-E) snow-depth data for Antarctic sea ice are compared with ship-based visual observations of snow depth, ice type and ridged-ice fraction, and with satellite C-band and Ku-band radar backscatter observations for two ship cruises into the Weddell Sea (ISPOL 2004–05,WWOS 2006) and one cruise into the Bellingshausen Sea (SIMBA 2007) during late winter/spring. Most (>75%) AMSR-E and ship-based snow-depth observations agree within 0.2 m during WWOS and SIMBA. Remaining observations indicate substantial underestimations of snow depths by AMSR-E data. These underestimations tend to increase with the ridged-ice fraction for WWOS and SIMBA. In areas with large snow depths, a combination of relatively stable low C-band radar backscatter and variable Ku-band radar backscatter is associated with undeformed first-year ice and may indicate snow metamorphism at this time of year during SIMBA. In areas with small snow depths, a combination of relatively stable low Ku-band radar backscatter, high C-band radar backscatter and low C-band radar backscatter standard deviations is associated with rough first-year ice during SIMBA. This information can help to better understand causes of the observed AMSR-E snow-depth bias during late-winter/spring conditions with decreasing average snow depth and to delineate areas where this bias occurs.

Sea Ice Detection in the Sea of Okhotsk Using PALSAR and MODIS Data

2013 ◽  
Vol 6 (3) ◽  
pp. 1516-1523 ◽  
Author(s):  
Hiroyuki Wakabayashi ◽  
Yuta Mori ◽  
Kazuki Nakamura
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Modis Data ◽  
The Sea Of Okhotsk ◽  
Ice Detection
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