The physical basis for sea ice remote sensing

Hallikainen, M.; Winebrenner, D. P.

doi:10.1029/gm068p0029

Cited by 143 publications

(100 citation statements)

References 30 publications

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“…During the winter months, moisture levels in the snow cover are negligible. As a result, the snow layer on the ice is essentially transparent at Ku-band [32]. However, as the surface temperature increases and the water content rises during early spring, forward scattering from the wet snow surface increases, thereby decreasing the values [21].…”

Section: B Melt Onset Mappingmentioning

confidence: 99%

Cryosphere applications of NSCAT data

Long

Drinkwater

1999

IEEE Trans. Geosci. Remote Sensing

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Abstract-Though designed to measure vector winds over the ocean, new imaging techniques facilitate the use of NASA scatterometer data (NSCAT) in cryosphere studies. NSCAT provides data of unprecedented coverage, resolution, and quality which, when coupled with the scatterometer image reconstruction with filtering (SIRF) algorithm, enables images of at resolutions approaching 8 km over stationary targets. Such images are useful in ice mapping and classification, and multidecadal studies are possible by comparison with Seasat Scatterometer (SASS) data. The utility of NSCAT data in polar ice studies is illustrated through a review of two cryosphere applications of NSCAT data: 1) sea-ice mapping and tracking and 2) ice-sheet change in Greenland and Antarctica. The wide swath and frequent revisit, coupled with incidence and azimuth angle diversity makes NSCAT data very effective in mapping the extent of sea-ice. In Greenland, snow and ice "facies" are clearly delineated on the basis of the seasonally dependent radar backscattering cross section, due to sensitivity of radar backscatter to diagenetic changes occurring at and beneath the surface. Comparison of NSCAT and SASS data enables study of change in Greenland between 1978 and 1996.

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Section: B Melt Onset Mappingmentioning

confidence: 99%

Cryosphere applications of NSCAT data

Long

Drinkwater

1999

IEEE Trans. Geosci. Remote Sensing

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“…Use of satellite radar altimetry (RA) for measuring ice freeboard is based on experiments showing that the radar signal reflects from the snowice, rather than the air-snow interface (Beaven et al, 1995). Changes in temperature could cause changes in the snow pack, affecting the radar signal penetration to the snow/ice interface (Hallikainen and Winebrenner, 1992;Giles and Hvidegaard, 2006). Ice thickness has been determined in winter period i.e.…”

Section: Introductionmentioning

confidence: 99%

The relation between sea ice thickness and freeboard in the Arctic

Alexandrov

Sandven²,

Wå̊hlin³

et al. 2010

The Cryosphere

140

164

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Abstract. Retrieval of Arctic sea ice thickness fromCryoSat-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.Correspondence to: V. Alexandrov

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“…The value of the dielectric constant e ' for freshwater ice has been measured as 3.17 at 10 GHz and varies little with frequency (Hallikainen and Winebrenner 1992). For saline first-year ice, e ' is higher and strongly dependent on both temperature and salinity of the ice.…”

Section: Microwave Electrical Properties Of Icementioning

confidence: 99%

Sea Ice Monitoring by Remote Sensing

Sandven¹,

Johannessen²,

Kloster³

2000

Encyclopedia of Analytical Chemistry

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Sea ice is defined as ice which is formed as a result of freezing of seawater. Sea ice occurs at the surface of the ocean in areas where the surface temperature is cooled to the freezing point, which is about −1.8°C for sea water with a salinity of about 35 parts per thousand. Ice formed in lakes and rivers and icebergs coming from glaciers and ice sheets are not defined as sea ice. Monitoring by remote sensing is defined as any measurement technique which can be used to observe sea ice repeatedly by instruments on board earth observation satellites. Monitoring also includes the process of making observational data available for users a short time after the observations have been obtained (i.e. a few hours). Remote sensing of ice can also be done with aircraft, submarines and other vehicles, but these techniques are not discussed in this article.

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The physical basis for sea ice remote sensing

Cited by 143 publications

References 30 publications

Cryosphere applications of NSCAT data

Cryosphere applications of NSCAT data

The relation between sea ice thickness and freeboard in the Arctic

Sea Ice Monitoring by Remote Sensing

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