Data Sets of Radiative Measurements and Properties - Sea Ice: Physics and Remote Sensing - page 380

remote sensing observations and establish databases that
can be used as tools to retrieve surface parameters (as
shown in the surface temperature retrieval in section 7.5).
However, weather and climate models need only climatic
seasonal or monthly averages of emissivity in given
regions. A complete set of monthly average emissivity of
FYI and MYI in the Arctic was generated by Mathew
et al. [2008] for the microwave frequencies of the Advanced
Microwave Sounding Unit (AMSU) radiometer onboard
NOAA polar orbiting satellites. The AMSU‐A has 15
channels in the frequency range 23-89 GHz, and the
AMSU‐B has 5 channels in the frequency range 89-183
GHz. The observations scan the scene in a range of local
zenith angles that vary between ±57°. Another study by
Mathew et al. , [2009] established the same data for the
frequencies of AMSR‐E. In addition to the satellite
observations, simulated brightness temperatures were
used to calculate the emissivity using equation (8.25).
The brightness temperatures were based on atmospheric
model profiles of temperature and humidity from the
European Centre for Medium‐Range Weather Forecasts
(ECMWF). The above two references show tables and
graphs of monthly averaged emissivity of Arctic sea ice.
For example, Mathew et al. (2008] include graphs of
angular variation of the monthly averaged emissivity in
2005 for the MY ice and FY ice in the north of Greenland.
The variation of the emissivity (represented by the stand-
ard deviation) is high during the summer months when
the ice surface melts. For this period a slight increase in
MY ice emissivity and a considerable drop in the FY ice
emissivity that brings the values close to those of OW are
observed. For all frequencies and months, the variation
of emissivity with the local zenith angle is negligible up to
45° and then decreases as the angle increases. Mathew
et al. [2008] concluded also that both the FYI and MYI
emissivity at frequencies up to 50 GHz show the least
variability for the winter months of November to April.

1

Before snowfall
After snowfall

V pol.

0.9

V pol.

V pol.

H pol.

0.8

V pol.

H pol.

0.7

H pol.

Before snow

H pol.

0.6

After snow

0.5

10 GHz 18 GHz 37 GHz 90 GHz

0.4 30

60 30 60 30 60 30 60

Angle Angle Angle Angle

Figure 8.38 Comparative spectra before and after a 4.5 cm
snowfall. V‐pol. and H‐pol. are indicated by the solid and
dashed lines, respectively [ Grenfell and Comiso , 1986, Figure 5,
with permission from IEEE].

1. 0

0.8

Wet snow (RN)
Wet snow (MS)
Dry snow (RN)
Dry snow (MS)

0.6

Frozen crust (RN)

Frozen crust (MS)

0.4

4.9 10.4 21.0 35.0 94.0

Frequency (GHz)

Figure 8.39 Variation of emissivity at vertical polarization with
frequency for three different snow types. The data were derived
from measurements of brightness temperature measurements
at an incidence angle of 50° by Rott and Nagler (RN) from
snow on land and M. Shokr (MS) from snow on ice [adapted
from Rott and Nagler , 1994].

8.5. MicRowave penetRation depth

This section includes data on penetration depth δ p in
snow‐covered sea ice obtained for the microwave bands
of operational sensors. Penetration depth depends on the
frequency of the radiation, the incidence angle it makes
at the surface, and most importantly the composition and
the degree of heterogeneity of the material. The factors
that affect δ p in the case of snow‐covered sea ice include
the salinity and conductivity of the snow cover ( vant
et al. , 1978; Hallikainen and Winebrenner , 1992], brine
volume [ Arcone et al. , 1986; Onstott and Shuchman , 2004],
brine salinity [ Stogryn and Desargant , 1985; Shokr and
Barber , 1994], air bubble contents of MY ice [ Fung
and Eom , 1982], and ice density. As δ p increases, the pos-
sibility of the EM wave to encounter more dielectric

interpret observed anomalies of high brightness tempera-
ture from wet snow on Arctic ice (shown in Figure 7.46).
Spectral emissivity of wet, dry snow and refrozen crusts
are shown in Figure 8.39 using two sets of data. The first
was obtained from snow over land in the Swiss Alps [ Rott
and Nagler , 1994] (denoted RN) and the second from
snow on sea ice in the Arctic [ M. Shokr , unpublished
data] (denoted MS). All data points are from measure-
ments of brightness temperature in vertical polarization.
The figure shows that the emissivity of wet snow is higher
than that of dry snow at all frequencies.
The above data demonstrate the effects of ice and snow
parameters on emissivity. These are required to model