Surface Emissivity of the Arctic Sea Ice at AMSR-E Frequencies

Mathew, Nizy; Heygster, Georg; Melsheimer, Christian

doi:10.1109/tgrs.2009.2023667

Cited by 48 publications

(42 citation statements)

References 15 publications

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“…The penetration depths for the above‐mentioned microwave frequencies derived by for first‐year sea ice, multiyear sea ice, and snow with liquid water contents of 1% and 2% can be seen in Table 2. Similar penetration depths for first‐ and multiyear ice have been found by Mathew et al [2009].…”

Section: Theorysupporting

confidence: 92%

Shortwave broadband black-sky surface albedo estimation for Arctic sea ice using passive microwave radiometer data

Laine¹,

Manninen²,

Riihelä³

et al. 2011

J. Geophys. Res.

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[1] A new remote sensing method to estimate the optical broadband black-sky surface albedo using passive microwave radiometer data has been developed. In this research, the Advanced Very High Resolution Radiometer (AVHRR) based shortwave broadband black-sky albedos processed using the algorithms of the new surface broadband albedo CM-SAF product SAL, and the Advanced Microwave Scanning Radiometer (AMSR-E) microwave data from the National Snow and Ice Data Center, Boulder, Colorado, have been employed. To analyze the correspondence between AVHRR-based optical albedo and microwave brightness temperature, AMSR-E frequencies of 6.9, 18.7, and 36.5 GHz have been tested by fitting a third-degree polynomial curve to the SAL albedo and the AMSR-E data points. The best correlation occurs in 6.9 GHz vertical polarization brightness temperature (R 2 = 0.92). To illustrate and compare the spatial variabilities of the SAL and AMSR-E albedos of the sea ice during the melting and refreezing period, maps of 13 weeks have been prepared of both albedos from June to August 2007. The albedo time series from AMSR-E and AVHRR data are calculated for the combined sea ice and open water cover for the Northern Hemisphere as a whole, and for six specific sea ice regions: the Arctic Ocean, the Kara and Barents Seas, the Greenland Sea, the Labrador Sea, Hudson Bay, and the Canadian Archipelago. The standard errors between the optical and passive microwave estimates varied from about 0.001 (Greenland Sea) to 0.04 (Canadian Archipelago) being 0.013 on the average in absolute units. The relative standard errors are then smaller than 5% in most of the regions.Citation: Laine, V., T. Manninen, A. Riihelä, and K. Andersson (2011), Shortwave broadband black-sky surface albedo estimation for Arctic sea ice using passive microwave radiometer data,

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Section: Theorysupporting

confidence: 92%

Shortwave broadband black-sky surface albedo estimation for Arctic sea ice using passive microwave radiometer data

Laine¹,

Manninen²,

Riihelä³

et al. 2011

J. Geophys. Res.

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“…[]. Results for the extreme snow cases showed that depletions are not much discernible from results in Table , confirming that the snow depth over the Arctic sea ice is generally thin enough to ignore the snow impact at lower MW frequencies [ Andersen et al , ; Mathew et al , ].…”

Section: Emissivity Retrieval From Lower Frequency Microwave Measuremsupporting

confidence: 78%

“…The obtained emissivities were used for simulating AMSR‐E brightness temperatures. Mathew et al [] retrieved the sea ice emissivities from AMSR‐E measurements with the use of ice temperature regressed from near surface temperature of ERA‐Interim data and atmospheric correction, for the seasonal ice at one region and for multiyear ice at another region in January 2005. These values are used for generating interchannel emissivity correlations which are important factor in data assimilation.…”

Section: Resultsmentioning

confidence: 99%

Differentiating between first‐year and multiyear sea ice in the Arctic using microwave‐retrieved ice emissivities

2017

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Polarized emissivities of the sea ice over the Arctic were retrieved at Advanced Microwave Scanning Radiometer–EOS 10.65, 18.7, 23.8, and 36.5 GHz channel frequencies. Results indicate that retrieved emissivities are consistent with other emissivity estimates. However, errors in the retrieved emissivity for multiyear sea ice at 23.8 and 36.5 GHz can be large up to 8% and 20%, respectively, because of ignoring the freeboard ice scattering and the use of the same emission layer as in 6.925 GHz. It is shown that the emissivity slope for first‐year ice between 10.65 and 18.7 GHz is opposite to that for multiyear sea ice, enabling a distinction between first‐year ice and multiyear ice. Using these differences in spectral features with ice types, an emissivity difference (vertically polarized emissivity difference between 10.65 and 18.7 GHz) was devised to differentiate between first‐year sea ice and multiyear sea ice. A comparison with the ice status information obtained from Cold Regions Research and Engineering Laboratory buoy measurements demonstrates that the method can separate first‐year ice from multiyear ice, implying that this technique enables us to obtain instantaneous and pixel‐level ice‐type information from space‐based passive microwave measurements.

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“…The study by [30] shows that, at 36 GHz and higher frequencies, the emissivity of the snow/ice pack increases to values above 0.9, compared with values around 0.8 for dry snow and 0.6 for frozen crust. A relevant finding was concluded by [31], who estimated emissivity of Arctic sea ice using AMSR-E data. The authors reported an increase in MYI emissivity and a drop of FYI emissivity with high variability during summer months when ice surface or snow cover starts to melt.…”

Section: Misclassification Of Myi In Autumnmentioning

confidence: 85%

Improving Multiyear Ice Concentration Estimates With Reanalysis Air Temperatures

Heygster

Shokr

2016

IEEE Trans. Geosci. Remote Sensing

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Multiyear ice (MYI) characteristics can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combine both observations to identify partial concentrations of ice types (including MYI) is the Environment Canada Ice Concentration Extractor (ECICE). However, cycles of warm-cold air temperature trigger wet-dry cycles of the snow cover on MYI surface. Under wet snow conditions, anomalous brightness temperature and backscatter, similar to those of first-year ice (FYI), are observed. This leads to misidentification of MYI as being FYI, hence decreasing the estimated MYI concentration suddenly. The purpose of this paper is to introduce a correction scheme to restore the MYI concentration under this condition. The correction is based on air temperature records. It utilizes the fact that the warm spell in autumn lasts for a short period of time (a few days). The correction is applied to MYI concentration retrievals from ECICE using an input of combined QuikSCAT and AMSR-E data, acquired over the Arctic region in a series of autumn seasons from 2003 to 2008. The correction works well by replacing anomalous MYI concentrations with interpolated ones. For September of the six years, it introduces over0.1 × 10 6 km 2 MYI area, except for 2005. Due to the regional effect of warm air spells, the correction could be important in the operational applications where ice concentrations are crucial on small scale and mesoscale. Index Terms-Arctic sea ice, Environment Canada Ice Concentration Extractor (ECICE), ice concentration, microwave remote sensing, multiyear ice (MYI), surface air temperature.

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Surface Emissivity of the Arctic Sea Ice at AMSR-E Frequencies

Cited by 48 publications

References 15 publications

Shortwave broadband black-sky surface albedo estimation for Arctic sea ice using passive microwave radiometer data

Shortwave broadband black-sky surface albedo estimation for Arctic sea ice using passive microwave radiometer data

Differentiating between first‐year and multiyear sea ice in the Arctic using microwave‐retrieved ice emissivities

Improving Multiyear Ice Concentration Estimates With Reanalysis Air Temperatures

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