scholarly journals Waveform analysis of airborne synthetic aperture radar altimeter over Arctic sea ice

2013 ◽  
Vol 7 (2) ◽  
pp. 1215-1242
Author(s):  
M. Zygmuntowska ◽  
K. Khvorostovsky ◽  
V. Helm ◽  
S. Sandven
Keyword(s):  
Sea Ice ◽  
Synthetic Aperture Radar ◽  
Waveform Analysis ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Ku Band ◽  
Aperture Radar

Abstract. Sea ice thickness is one of the most sensitive variables in the Arctic climate system. In order to quantify changes in sea ice thickness, CryoSat was launched in 2010 carrying a Ku-band Radar Altimeter (SIRAL) designed to measure sea ice freeboard with a few centimeters accuracy. The instrument uses the synthetic aperture radar technique providing signals with a resolution of about 300 m along track. In this study, airborne Ku-band radar altimeter data over different sea ice types has been analyzed. A set of parameters has been defined to characterize the difference in strength and width of the returned power waveforms. With a Bayesian based method it is possible to classify about 80% of the waveforms by three parameters: maximum of the returned power echo, the trailing edge width and pulse peakiness. Furthermore, the radar power echo maximum can be used to minimize the rate of false detection of leads compared to the widely used Pulse Peakiness parameter. The possibility to distinguish between different ice types and open water allows to improve the freeboard retrieval and the conversion into sea ice thickness where surface type dependent values for the sea ice density and snow load can be used.

scholarly journals Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice

2013 ◽  
Vol 7 (4) ◽  
pp. 1315-1324 ◽  
Author(s):  
M. Zygmuntowska ◽  
K. Khvorostovsky ◽  
V. Helm ◽  
S. Sandven
Keyword(s):  
Sea Ice ◽  
Synthetic Aperture Radar ◽  
Arctic Sea Ice ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Ku Band ◽  
Aperture Radar

Abstract. Sea ice thickness is one of the most sensitive variables in the Arctic climate system. In order to quantify changes in sea ice thickness, CryoSat-2 was launched in 2010 carrying a Ku-band radar altimeter (SIRAL) designed to measure sea ice freeboard with a few centimeters accuracy. The instrument uses the synthetic aperture radar technique providing signals with a resolution of about 300 m along track. In this study, airborne Ku-band radar altimeter data over different sea ice types have been analyzed. A set of parameters has been defined to characterize the differences in strength and width of the returned power waveforms. With a Bayesian-based method, it is possible to classify about 80% of the waveforms from three parameters: maximum of the returned power waveform, the trailing edge width and pulse peakiness. Furthermore, the maximum of the power waveform can be used to reduce the number of false detections of leads, compared to the widely used pulse peakiness parameter. For the pulse peakiness the false classification rate is 12.6% while for the power maximum it is reduced to 6.5%. The ability to distinguish between different ice types and leads allows us to improve the freeboard retrieval and the conversion from freeboard into sea ice thickness, where surface type dependent values for the sea ice density and snow load can be used.

scholarly journals Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice

2021 ◽  
Vol 15 (4) ◽  
pp. 1811-1822
Author(s):  
Rasmus T. Tonboe ◽  
Vishnu Nandan ◽  
John Yackel ◽  
Stefan Kern ◽  
Leif Toudal Pedersen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Scattering Model ◽  
Radar Scattering ◽  
The Impact ◽  
Ku Band

Abstract. Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and the impact on the Ka- and Ku-band radar scattering horizon or the “track point” (i.e. the scattering layer depth detected by the radar re-tracker) are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and the Archimedes principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round-robin data package, in which NASA's Operation IceBridge (OIB) data and climatology are included, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth; however, the simulated track point difference is much smaller than what is reported in the literature from the Ku-band CryoSat-2 and Ka-band SARAL/AltiKa satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences is sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track point difference when the snow is thin and then decreasing the difference when the snow is thick (>0.1 m). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for (1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both (1) and (2), the impact of using a snow climatology versus the actual snow depth is relatively small on the radar freeboard, only raising the radar freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. The radar freeboard is a function of both radar scattering and floe buoyancy. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals via the combination of the currently operational and ESA's forthcoming Ka- and Ku-band dual-frequency CRISTAL radar altimeter missions.

scholarly journals Simulated Ka- and Ku-band radar altimeter scattering horizon on snow-covered Arctic sea ice

2020 ◽  
Author(s):  
Rasmus T. Tonboe ◽  
Vishnu Nandan ◽  
John Yackel ◽  
Stefan Kern ◽  
Leif Toudal Pedersen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Scattering Model ◽  
Radar Scattering ◽  
The Impact ◽  
Ku Band

Abstract. Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and its impact on the Ka- and Ku-band radar scattering horizon or the "track point" (i.e. the scattering layer depth detected by the radar re-tracker), are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and Archimedes’ principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round robin data package, NASA’s Operation Ice Bridge (OIB) data and climatology, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth, however, the simulated track point difference is much smaller than what is reported in the literature from the CryoSat-2 Ku-band and SARAL/AltiKa Ka-band satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences are sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track-point difference when the snow is thin and then decreasing the difference when the snow is thick (> 10 cm). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for 1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and 2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small on the measured freeboard, by only raising the measured freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals from combining currently operational and upcoming Ka- and Ku-band dual-frequency radar altimeter missions, such as ESA’s Copernicus High Priority Candidate Mission CRISTAL.

scholarly journals High spatial and temporal resolution L- and C-band Synthetic Aperture Radar data analysis from the yearlong MOSAiC expedition

2021 ◽  
Author(s):  
Malin Johansson ◽  
Suman Singha ◽  
Gunnar Spreen ◽  
Stephen Howell ◽  
Shin-ichi Sobue ◽  
...  
Keyword(s):  
Sea Ice ◽  
Synthetic Aperture Radar ◽  
Temporal Resolution ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Significant Shift ◽  
Synthetic Aperture Radar Data ◽  
The Mean ◽  
L Band ◽  
Aperture Radar

<p>In the yearlong MOSAIC expedition (2019-2020) R/V Polarstern drifted with sea ice through the Arctic Ocean, with the goal to continually monitor changes in the coupled ocean-ice-atmosphere system throughout the seasons. A substantial amount of synthetic aperture radar (SAR) satellite images overlapping the campaign was collected. Here, we investigate the change in polarimetric features over sea ice from the freeze up to the advanced melt season using fully polarimetric L-band images from the ALOS-2 PALSAR-2 and fully polarimetric C-band images from the RADARSAT-2 satellite SAR sensors.</p><p>Three different sea ice types are investigated, young ice, level first year ice and deformed first and second-year ice. Areas of deformed and level sea ice were observed in the vicinity of R/V Polarstern and these areas are included whenever possible in the yearlong time series.</p><p>Comparing the different sea ice types, we observe that during the freezing season there is a larger difference in the co-polarization channels between smooth and deformed ice in L-band compared to C-band. Similar to earlier findings we observe larger differences between young ice and deformed ice backscatter values in the L-band data compared to the C-band data. Moreover, throughout the year the HV-backscatter values show larger differences between level and deformed sea ice in L-band than C-band. The L-band data variability is significantly smaller for the level sea ice compared to the deformed sea ice, and this variability was also smaller than that observed for the overlapping C-band data. Thus L-band data could be more suitable to reliable separate deformed from level sea ice areas.   </p><p>Within the L-band images a noticeable shift towards higher backscatter values in early melt season compared to the freezing season for all polarimetric channels is observed, though no such strong trend is found in the C-band data. The change in backscatter values is first noticeable in the C-band images and later followed by a change in the L-band images, probably caused by their different penetration depth and volume scattering sensitivities. This change also results in a smaller backscatter variability.</p><p>The polarization difference (PD; VV-HH on a linear scale) show a seasonal dependency for the smooth and deformed sea ice within the L-band data, whereas for the C-band data no such trend is observed. For the L-band data were the PD variability for all ice classes reasonably small for the freezing season, with a significant shift towards larger variability during the early melt season, though during this time period the mean PD values remained similar. However, once the temperatures reached above 0°C both the variability and the mean values increased significantly.</p><p>Overall, our results demonstrate that the C- and L-band data are complementary to one another and that through their slightly different dependencies on season and sea ice types, a combination of the two frequencies can aid improved sea ice classification. The availability of a high spatial and temporal resolution dataset combined with in-situ information ensures that seasonal changes can be fully explored.</p>

scholarly journals Sensitivity of Radar Altimeter Waveform to Changes in Sea Ice Type at Resolution of Synthetic Aperture Radar

2019 ◽  
Vol 11 (22) ◽  
pp. 2602 ◽  
Author(s):  
Wiebke Aldenhoff ◽  
Céline Heuzé ◽  
Leif E. B. Eriksson
Keyword(s):  
Sea Ice ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Altimeter Data ◽  
Small Scale ◽  
High Peak Power ◽  
Radar Altimeter ◽  
Basin Scale ◽  
Sar Imagery ◽  
Aperture Radar

Radar altimetry in the context of sea ice has mostly been exploited to retrieve basin-scale information about sea ice thickness. In this paper, we investigate the sensitivity of altimetric waveforms to small-scale changes (a few hundred meters to about 10 km) of the sea ice surface. Near-coincidental synthetic aperture radar (SAR) imagery and CryoSat-2 altimetric data in the Beaufort Sea are used to identify and study the spatial evolution of altimeter waveforms over these features. Open water and thin ice features are easily identified because of their high peak power waveforms. Thicker ice features such as ridges and multiyear ice floes of a few hundred meters cause a response in the waveform. However, these changes are not reflected in freeboard estimates. Retrieval of robust freeboard estimates requires homogeneous floes in the order of 10 km along-track and a few kilometers to both sides across-track. We conclude that the combination of SAR imagery and altimeter data could improve the local sea ice picture by extending spatially scarce freeboard estimates to regions of similar SAR signature.

scholarly journals The relation between sea ice thickness and freeboard in the Arctic

2010 ◽  
Vol 4 (2) ◽  
pp. 641-661 ◽  
Author(s):  
V. Alexandrov ◽  
S. Sandven ◽  
J. Wahlin ◽  
O. M. Johannessen
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Empirical Relation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Retrieval of Arctic sea ice thickness from radar altimeter freeboard data, to be provided by CryoSat-2, requires observational data to verify the relation between the two variables. In this study in-situ ice and snow data from 689 observation sites obtained during the Sever expeditions in the 1980s have been used to establish an empirical relation between ice thickness and freeboard. Estimates of mean and variability of snow depth, snow density and ice density were produced based on many field observations, and have been used in the isostatic equilibrium equation to estimate ice thickness as a function of ice freeboard, snow depth and snow/ice density. The accuracy of the ice thickness retrieval has been calculated from the estimated variability in ice and snow parameters and error of ice freeboard measurements. It is found that uncertainties of ice density and freeboard are the major sources of error in ice thickness calculation. For FY ice, retrieval of ≈1.0 m (2.0 m) thickness has an uncertainty of 60% (41%). For MY ice the main uncertainty is ice density error, since the freeboard error is relatively smaller than for FY ice. Retrieval of 2.4 m (3.0 m) thick MY ice has an error of 24% (21%). The freeboard error is ±0.05 m for both the FY and MY ice. If the freeboard error can be reduced to 0.01 m by averaging a large number of measurements from CryoSat, the error in thickness retrieval is reduced to about 32% for a 1.0 m thick FY floe and to about 18% for a 2.3 m thick MY floe. The remaining error is dominated by uncertainty in ice density. Provision of improved ice density data is therefore important for accurate retrieval of ice thickness from CryoSat data.

scholarly journals Simulation of the satellite radar altimeter sea ice thickness retrieval uncertainty

2009 ◽  
Vol 3 (2) ◽  
pp. 513-559 ◽  
Author(s):  
R. T. Tonboe ◽  
L. T. Pedersen ◽  
C. Haas
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
The Arctic ◽  
Ice Thickness ◽  
Spatial And Temporal Variability ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Scattering Model ◽  
Satellite Radar ◽  
Effective Scattering Surface

Abstract. Although it is well known that radar waves penetrate into snow and sea ice, the exact mechanisms for radar-altimeter scattering and its link to the depth of the effective scattering surface from sea ice are still unknown. Previously proposed mechanisms linked the snow ice interface, i.e. the dominating scattering horizon, directly with the depth of the effective scattering surface. However, simulations using a multilayer radar scattering model show that the effective scattering surface is affected by snow-cover and ice properties. With the coming Cryosat-2 (planned launch 2009) satellite radar altimeter it is proposed that sea ice thickness can be derived by measuring its freeboard. In this study we evaluate the radar altimeter sea ice thickness retrieval uncertainty in terms of floe buoyancy, radar penetration and ice type distribution using both a scattering model and ''Archimedes' principle''. The effect of the snow cover on the floe buoyancy and the radar penetration and on the ice cover spatial and temporal variability is assessed from field campaign measurements in the Arctic and Antarctic. In addition to these well known uncertainties we use high resolution RADARSAT SAR data to simulate errors due to the variability of the effective scattering surface as a result of the sub-footprint spatial backscatter and elevation distribution sometimes called preferential sampling. In particular in areas where ridges represent a significant part of the ice volume (e.g. the Lincoln Sea) the simulated altimeter thickness estimate is lower than the real average footprint thickness. This means that the errors are large, yet manageable if the relevant quantities are known a priori. A discussion of the radar altimeter ice thickness retrieval uncertainties concludes the paper.

scholarly journals The relation between sea ice thickness and freeboard in the Arctic

2010 ◽  
Vol 4 (3) ◽  
pp. 373-380 ◽  
Author(s):  
V. Alexandrov ◽  
S. Sandven ◽  
J. Wahlin ◽  
O. M. Johannessen
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Empirical Relation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Late Winter ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Arctic Sea Ice Thickness

Abstract. Retrieval of Arctic sea ice thickness from CryoSat-2 radar altimeter freeboard data requires observational data to verify the relation between these two variables. In this study in-situ ice and snow data from 689 observation sites, obtained during the Sever expeditions in the 1980s, have been used to establish an empirical relation between thickness and freeboard of FY ice in late winter. Estimates of mean and variability of snow depth, snow density and ice density were produced on the basis of many field observations. These estimates have been used in the hydrostatic equilibrium equation to retrieve ice thickness as a function of ice freeboard, snow depth and snow/ice density. The accuracy of the ice thickness retrieval has been calculated from the estimated variability in ice and snow parameters and error of ice freeboard measurements. It is found that uncertainties of ice density and freeboard are the major sources of error in ice thickness calculation. For FY ice, retrieval of ≈ 1.0 m (2.0 m) thickness has an uncertainty of 46% (37%), and for MY ice, retrieval of 2.4 m (3.0 m) thickness has an uncertainty of 20% (18%), assuming that the freeboard error is ± 0.03 m for both ice types. For MY ice the main uncertainty is ice density error, since the freeboard error is relatively smaller than that for FY ice. If the freeboard error can be reduced to 0.01 m by averaging measurements from CryoSat-2, the error in thickness retrieval is reduced to about 32% for a 1.0 m thick FY floe and to about 18% for a 2.4 m thick MY floe. The remaining error is dominated by uncertainty in ice density. Provision of improved ice density data is therefore important for accurate retrieval of ice thickness from CryoSat-2 data.

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