Sea ice concentration products are used to understand the spatial characterization of sea ice cover and to calculate sea ice extent and area for time series analyses and process studies in the Arctic and Antarctic. Passive microwave data are particularly useful for sea ice studies because of the relatively high contrast in emissivities between open water and sea ice. This contrast is frequency-dependent; contrast increases with decreasing channel frequency. In most algorithms, atmospheric effects are assumed constant contributing to uncertainties in the sea ice estimates. The satellite-received radiation, expressed as a Brightness Temperature (T_b), is as follows (Cavalieri and Comiso 2000):

T_b = T_bwC_w + T_biC_i  (Equation 1)

Where:

T_bw = brightness temperature of open water
T_bi = brightness temperature of sea ice
C_w = fraction of open water within instrument field-of-view
C_i = fraction of sea ice concentration within the instrument field-of-view

Sea ice concentration (C_I), corresponding to an observed T_b over a sea-ice-covered region (T_b), is derived as follows:

C_i = (T_b - T_bw) / (T_bi - T_bw)  (Equation 2)

The AMSR-E sea ice concentration products use this equation as a basis for their algorithms, but the channels and methods to derive sea ice concentration are different for each algorithm. Values of brightness temperature of open water, brightness temperature of sea ice include contributions from the intervening atmosphere. Brightness temperature of sea ice varies spatially because of spatial changes in emissivity and temperature of ice, while brightness temperature of open water is constant for open water within the ice pack.

Sea Ice Concentration

The 25 km sea ice concentration product is generated using the Enhanced NASA Team (NT2) algorithm described by Markus and Cavalieri (2000) for both the Arctic and the Antarctic. NT2 sea ice concentrations are calculated using the individual Level 2-A swaths rather than using gridded averaged brightness temperatures in order to make the atmospheric corrections on an orbit-by-orbit basis before obtaining daily average ice concentrations. However, previous AE_SI25 versions employed the AMSR Bootstrap Algorithm (ABA) described by Comiso, Cavalieri, and Markus (2003) for the Antarctic. Now the ABA is only used in the sea ice concentration difference field between the ABA and the NT2 (ABA-NT2) for both hemispheres.

The original NASA Team algorithm is based on techniques described in Cavalieri et al. (1984) and Gloersen and Cavalieri (1986). The NT2 algorithm (Markus and Cavalieri 2000) identifies two ice types for both the Arctic and the Antarctic:

In this algorithm, the primary source of error is attributed to conditions in the surface layer such as surface glaze and layering (Comiso et al. 1997), which can significantly affect the horizontally polarized 18.7 GHz brightness temperature (Matzler et al. 1984) leading to increased PR(18) values and thus is an underestimation of sea ice concentration. The use of the horizontally polarized channels makes it imperative to resolve a third ice type to overcome the difficulty of surface effects on the emissivity of the horizontally polarized component.

Thus, one advantage of using the NT2 algorithm is that it uses the 89 GHz channels because the horizontally polarized 89 GHz data are less affected by surface conditions than the horizontally polarized 18.7 GHz data (Matzler et al. 1984), and the 89 GHz channels have successfully been used in sea ice concentration retrievals under clear atmospheric conditions (Lubin et al. 1998). As a result, this algorithm can identify more complex ice types resulting from surface glaze and layering in the snow cover. The thin-ice bias is also reduced through the use of the third ice type. The third ice type consists of surface glaze and layering of sea ice with snow cover. This type of ice primarily occurs in the Antarctic.

The Spectral Gradient Ratios (GR) and Polarization Ratios (PRs) are calculated from the observed and modeled brightness temperatures using the following two equations:

The Polarization Equation:

The Spectral Gradient Ratio Equation:

Where:

T_b is the brightness temperature at frequency ν for the polarized component p (vertical (V) or horizontal (H)).

The actual radiance ratios used in the NT2 algorithm are PR_R(18), PR_R(89), GR (89, 18, H-pol), and GR (89, 18, V-pol) where the subscript R refers to a rotation of axes. This rotation is done in the NT PR(18)-GR(37, 18, V-pol) domain, with the axes rotated through an angle, φ, until the ice-type lines (the FY-MY line for the Arctic and the A–B line for the Antarctic) are parallel to the GR axis. The rotated PR is defined by:

and is independent of the two ice types. Next, we make use of the difference between two gradient ratios

to resolve the ambiguity between pixels with true low sea ice concentration and pixels with significant scattering (defined as Ice Type C). Therefore, ΔGR serves as an indicator of the presence of Ice Type C. The higher the value of ΔGR the higher the amount of layering within a pixel. The retrieval of Ice Type C is applied to both, the Arctic and the Antarctic, oceans. Finally, a third parameter is defined to avoid the ambiguity between changes in sea ice concentration and changes in atmospheric conditions, because of the higher sensitivity of the 89-GHz channels to atmospheric variability compared to the lower frequency channels. This third parameter is the rotated PR(89), PR_R(89), computed from the PR(89)-GR(37V18V) domain analogous to the calculation of PR_R(18) but with a different angle.

The algorithm quantifies atmospheric effects by calculating brightness temperatures for each channel using a forward atmospheric radiative transfer model (Kummerow 1993) that incorporates four surface types: first-year ice, multiyear ice, Type C, and open water. Beginning with version 3, the Southern Hemisphere sea ice concentration algorithm no longer uses the Level-2A land flag and only uses the updated land mask for surface type classification. Thus, the 89 GHz channels together with a forward radiative transfer model provide ice concentrations under all atmospheric conditions. Figure 3 shows the general flow of the algorithm. First brightness temperatures are calculated for the four surface types and all weather conditions, currently 12. The response of the brightness temperatures to different weather conditions is calculated using the atmospheric radiative transfer model. The input data for the model consists of several things such as: the emissivities of the different surface types taken from Table 4-1 in Eppler et al. (1992) with modifications to achieve agreement between modeled and observed ratios, different cloud properties, specifically cloud base, cloud top, and cloud liquid water, taken from Fraser et al. (1975), and average atmospheric temperature and humidity profiles for summer and winter conditions taken from Antarctic research stations. Brightness temperatures are calculated for all possible ice concentration combinations in one percent increments, and the following ratios were calculated for each increment: PR_R(18), PR_R(89), and ΔGR. This creates a prism in which each element within this space contains a vector with the three ratios: PR_R(18), PR_R(89), and ΔGR. The subscript R refers to a rotation of axes in PR-GR space by the angle so that PR_R(18) and PR_R(89) are independent of ice types A and B in the Antarctic, and first-year and multiyear ice for the Arctic. For each pixel, the observed brightness temperatures are used to create a vector with the same ratios. The ice concentration for a pixel is determined where the difference between an observed and a modeled ratio is smallest.

The NT2 algorithm also corrects for the presence of thin ice by using GR(37V19V) and only two ice types. The algorithm eliminates spurious sea ice concentrations over open ocean by employing weather filters previously used for SSM/I (Cavalieri et al. 1995). Ice concentrations for image pixels with GR(37V19V) values greater than 0.05 and GR(2219) values greater than 0.045 are set to zero.

Sea Ice Concentration Difference

The AMSR Bootstrap Algorithm (ABA) (Comiso, Cavalieriand, and Markus 2003) is used in the calculation of sea ice concentration differences between the ABA and the NT2 (ABA-NT2) for both hemispheres. The ABA makes use of the 6.9 GHz data to reduce temperature effects on the 18.7 GHz and 36.5 GHz brightness temperatures, both vertically polarized (called V1937) data provides very similar results to those of the Bootstrap Basic Algorithm (BBA) used in the 12.5 km sea ice product, indicating that errors associated with temperature effects on the latter are relatively minor; however, ABA allows for the calculation of ice temperature, which in itself is an important climate parameter.

Processing Steps

The AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures are binned into 25 km grid cells using a drop-in-the-bucket method where the grid cell that contains the center of the observation footprint is given the whole weight of the observation. With this procedure, the number of observations is always in whole numbers. All valid brightness temperature observations within the extent of the polar grids are binned into grid cells, including land observations. Figure 2 summarizes the input products and algorithms used to create the gridded 25 km AMSR-E sea ice products.

After input Level-2A brightness temperatures are binned into 25 km grid cells, the ascending, descending, and daily data are averaged. Refer to the Data Source section of this guide document for more information. The daily average is not simply an average of ascending and descending orbits, because a given pixel could have, for example, three measurements from ascending orbits and two from descending orbits. Instead, the daily average is of all the observations for that grid cell. For example, if A = ascending and B = descending:

((A1 + A2) / 2 + (B1 + B2 + B3) / 3 ) / 2

(Equation 7)

is not equal to:

(A1 + A2 + B1 + B2 + B3) / 5

(Equation 8)

However, this biases daytime (ascending) orbits over nighttime (descending).

Land Masks

The AMSR-E sea ice product utilizes a 25 km Northern Hemisphere land mask (amsr_gsfc_25n.hdf) and a 25 km Southern Hemisphere land mask (amsr_nic_25s.hdf). The North land mask is identical to gsfc_25n.msk and was last updated in 1997. The South land mask includes an updated ice shelf definition created by the National Ice Center Science Department in June 2011 and an updated shoreline developed from ENVISAT and RADARSAT imagery from October 2009 to April 2010.

A 1-byte integer array is included in each HDF file.

amsr_gsfc_25n.hdf: 304 columns x 448 rows
Values are 0 (water) and 1 (land).
amsr_nic_25s.hdf: 316 columns x 332 rows
Values are 0 (water), 1 (land) and 2 (coast).

Version History

Changes to the Version 3 (V15-stage1) algorithm for these data include: 

• Use of the most recent version (Version 3) of the AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures data as input
• An improved Antarctic land mask
• No longer uses the Level-2A land flag and only uses the updated land mask for surface type classification in the Southern Hemisphere sea ice concentration algorithm
• Inclusion of ISO lineage metadata

Refer to the AMSR-E Data Versions Web page for a summary of changes since the start of mission.

Error Sources

In the sea ice concentration algorithm, the primary source of error is attributed to conditions in the surface layer such as surface glaze and layering (Comiso et al. 1997), which can significantly affect the horizontally polarized 18.7 GHz brightness temperature (Matzler et al. 1984) leading to increased PR(18) values and thus underestimates in ice concentration.

Ashcroft and Wentz (2000) discuss errors in the source Level-2A brightness temperatures that were binned into 25 km grid cells for this sea ice product.