Intercomparisons of Antarctic sea ice types from visual ship, RADARSAT-1 SAR, Envisat ASAR, QuikSCAT, and AMSR-E satellite observations in the Bellingshausen Sea

Özsoy, Burcu; Kern, Stefan; Ackley, Stephen F.; Xie, Hongjie; Tekeli, Ahmet Emre

doi:10.1016/j.dsr2.2010.10.031

Cited by 28 publications

(49 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two different approaches, buoyancy equation and empirical equation, are used to compute sea ice thicknesses based on ICESat freeboard and other parameters, at two different horizontal scales: high resolution at the ICESat footprint scale (i.e., 70 m) and coarse resolution at the AMSR‐E snow depth data scale (i.e., 12.5 km). The ICESat ON07 campaign period is chosen to compare these computations with extensive surface validation measurements made in October 2007 during the ship‐based experiment in the Bellingshausen Sea, Sea Ice Mass Balance in the Antarctic (SIMBA) [ Xie et al ., ; Lewis et al ., ; Weissling et al ., ; Weissling and Ackley , ; Ozsoy‐Cicek et al ., ].…”

Section: Sea Ice Thickness Estimation Methodsmentioning

confidence: 99%

Sea ice thickness estimations from ICESat Altimetry over the Bellingshausen and Amundsen Seas, 2003–2009

et al. 2013

Self Cite

View full text Add to dashboard Cite

[1] Sea ice thicknesses derived from NASA's Ice, Cloud, and Land Elevation Satellite (ICESat) altimetry data are examined using two different approaches, buoyancy and empirical equations, and at two spatial scales-ICESat footprint size (70 m diameter spot) and Advanced Microwave Scanning Radiometer (AMSR-E) pixel size (12.5 km by 12.5 km) for the Bellingshausen and Amundsen Seas of west Antarctica. Ice thickness from the empirical equation shows reasonable spatial and temporal distribution of ice thickness from 2003 to 2009. Ice thickness from the buoyancy equation, however, additionally needing snow depth information derived from the AMSR-E, shows an overestimation in terms of maximum, mean (+63% to 75%), and standard deviation while underestimation in modal thickness (À20%) as compared with those from the empirical equation approach. When ICESat snow freeboard is used as the snow depth in the buoyancy equation, i.e., the zero ice freeboard assumption, the derived ice thicknesses match well with those from the empirical equation approach, within 5% overall. The AMSR-E, therefore, may underestimate snow depth and accounts for~95% of the ice thickness overestimation as compared with the buoyancy approach. Starting from autumn, a general picture of seasonal mean, modal, and median ice thickness increases progressively from autumn to spring and decreases from spring to the following autumn, when new thin ice dominates the ice thickness distribution. The asymmetric shape of the thickness distribution reflects the key role of ice deformation processes in the evolution of the thickness distribution. The statistical properties of the thickness distribution interannually (high range of mean thickness and standard deviation) indicate the variability of deformation processes. However, spring ice volume, the product of ice mean thickness and areal extent computed for the spring maximum, shows variability year to year but is primarily dominated by ice extent variability, with no increasing or decreasing trend over this record length. The dependence of the volume on the ice extent primarily suggests that ice thickness changes have also not covaried with the ice extent losses seen over the satellite record in this region, unlike the Arctic. These properties reflect the interactive processes of ice advection, thermodynamic growth and ice deformation that all substantially influence ice mass balance in the Bellingshausen-Amundsen Seas region.

show abstract

Section: Sea Ice Thickness Estimation Methodsmentioning

confidence: 99%

Sea ice thickness estimations from ICESat Altimetry over the Bellingshausen and Amundsen Seas, 2003–2009

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…AMSR-E snow depth is known to be relatively accurate for undeformed sea ice while snow depths are underestimated by up to a factor of 2 over deformed (rough) sea ice [27][28][29][30]. It was shown by [30] that combining AMSR-E brightness temperature observations with surface roughness information can lead to a substantial reduction in the AMSR-E snow depth bias over rough sea ice.…”

Section: Consistency Check: Winter To Spring Snow Depth Evolutionmentioning

confidence: 99%

“…On the other hand, ASPeCt observations carried out during SIMBA in the Bellingshausen Sea revealed snow depths of 20 cm to 30 cm on pancake ice, which has formed within 10-20 days. In the same area, AMSR-E snow depths are 5 cm to 10 cm [28], which corresponds to a substantial underestimation of the actual snow depth. Pancake ice is the usual type of sea ice formed seaward of the Antarctic pack ice cover.…”

Section: Differences Between Icesat and Amsr-e Snow Depthmentioning

confidence: 99%

“…More details about the algorithm and its limitations are given in [19,24]. It needs to be stated clearly, that it has been illustrated in a number of studies that AMSR-E, or in general satellite passive microwave, snow depth on sea ice retrieval works best for level sea ice [27][28][29]37,38].…”

Section: Methodsmentioning

confidence: 99%

“…A comparison with ASPeCt observations carried out during the expedition SIMBA (24 September until 27 October 2007) [28,29] revealed AMSR-E snow depths of 25 cm to 35 cm south of 70˝S, while ASPeCt snow depths ranged between 45 cm and 55 cm in the same area. For the ICESat period with temporal overlap: ON07 (see Table 1), our ICESat snow depths are 40 cm to 50 cm in the SIMBA area.…”

Section: Inter-comparison With Aspect Visual Ship-based Snow Depth Obmentioning

confidence: 99%

See 2 more Smart Citations

Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches

Kern

Özsoy

2016

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Snow on Antarctic sea ice plays a key role for sea ice physical processes and complicates retrieval of sea ice thickness using altimetry. Current methods of snow depth retrieval are based on satellite microwave radiometry, which perform best for dry, homogeneous snow packs on level sea ice. We introduce an alternative approach based on in-situ measurements of total (sea ice plus snow) freeboard and snow depth, which we use to compute snow depth on sea ice from Ice, Cloud, and land Elevation Satellite (ICESat) total freeboard observations. We compare ICESat snow depth for early winter and spring of the years 2004 through 2006 with the Advanced Scanning Microwave Radiometer aboard EOS (AMSR-E) snow depth product. We find ICESat snow depths agree more closely with ship-based visual and air-borne snow radar observations than AMSR-E snow depths. We obtain average modal and mean ICESat snow depths, which exceed AMSR-E snow depths by 5-10 cm in winter and 10-15 cm in spring. We observe an increase in ICESat snow depth from winter to spring for most Antarctic regions in accordance with ground-based observations, in contrast to AMSR-E snow depths, which we find to stay constant or to decrease. We suggest satellite laser altimetry as an alternative method to derive snow depth on Antarctic sea ice, which is independent of snow physical properties.

show abstract

Sea ice thickness retrieval algorithms based on in situ surface elevation and thickness values for application to altimetry

et al. 2013

Self Cite

View full text Add to dashboard Cite

[1] In situ measurements of sea ice thickness (I), snow depth (S), and snow freeboard (F sn ) from drilling profile lines from 15 cruises into the Southern Ocean, Antarctica, were analyzed. I was calculated from in situ F sn and S using an isostatic approach. I was also directly estimated from F sn as can be obtained from laser altimetry. The root-mean-square difference (RMSD) between observed and calculated I reduces, and the correlation between F sn and I increases substantially, when (1) using values averaged over the survey lines ($50 m) instead of single drill hole measurements ($1 m) and (2) treating positive and negative sea ice freeboard (F i ) separately. For small F i , however, S approximates F sn pointing toward an isostatic balance also between S and I. Our linear regression analysis between the in situ measurements suggests a direct conversion of F sn into I using a region-specific set of equations. RMSD values are similar to those obtained employing isostatic balance models and treating positive and negative F i separately. However, more data would have been needed to obtain significant differences between most of the various models suggested. Still our new approach gives a viable alternative for Antarctic I retrieval from altimetric measurements of F sn alone. Correlation between in situ observations of F sn and S is high. RMSD between observed and calculated S is small. This suggests estimation of S from altimetric F sn measurements. Such S has an estimated precision of $5 cm, and is neither affected by snow wetness or grain size nor limited to S < 50 cm.Citation: Ozsoy-Cicek, B., S. Ackley, H. Xie, D. Yi, and J. Zwally (2013), Sea ice thickness retrieval algorithms based on in situ surface elevation and thickness values for application to altimetry,

show abstract

Intercomparisons of Antarctic sea ice types from visual ship, RADARSAT-1 SAR, Envisat ASAR, QuikSCAT, and AMSR-E satellite observations in the Bellingshausen Sea

Cited by 28 publications

References 33 publications

Sea ice thickness estimations from ICESat Altimetry over the Bellingshausen and Amundsen Seas, 2003–2009

Sea ice thickness estimations from ICESat Altimetry over the Bellingshausen and Amundsen Seas, 2003–2009

Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches

Sea ice thickness retrieval algorithms based on in situ surface elevation and thickness values for application to altimetry

Contact Info

Product

Resources

About