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the volume increases as the radius of the snow grains
increases. The difference between results from the multi-
ple scattering models and the other two models (which
are shown in the figure) increases also with radius of the
snow grains. The co‐polarized snow volume backscatter-
ing simulated from three models has comparable values
from each model. The cross‐polarized backscattering
coefficients, on the other hand, are underestimated by the
first‐order scattering model. Regardless of the model
used to produce these results, the underlying point is the
increase of both co‐ and cross‐polarization with the
radius of snow grains before they saturate around a radius
of 1.5 mm. The increase in the cross‐polarization scatter-
ing is a manifestation of multiple scattering which trig-
gers depolarization of the radar signal (section 7.6.2.3).
An implication of these results is that as snow on FY ice
acquires wetness and then refreezes, snow grains grow
bigger and therefore the backscatter from this medium
increases. If this happens to the snow on FY ice, it will
likely make the backscatter signature as high as that from
MY ice and thus confuses the ice classification. This sce-
nario can be used to explain anomalies in Arctic FY ice
signature in the spring as presented later.
Information about snow grain size is needed in order to
simulate the microwave observations from snow‐covered
sea ice and assess the retrieval algorithms. There have
been only a few field measurements of grain size and shape
such as those conducted in the 1990s during the SIMMS
program [
LeDrew and Barber
, 1994]. Measurements are
usually hampered by sampling constraints because of the
high spatial and temporal variability of snow grain
dimensions, especially at small scales.
Colbeck
[1983] and
Langlois et al
., [2008] suggest that the snow morphology
is extremely variable and can change within a few hours.
Snow wetness and salinity also affect both passive micro-
wave emission and radar backscatter. As explained in sec-
tion 2.3.3.2, the snowpack may pick up brine from the
underlying ice surface through the brine expulsion mecha-
nism. Additionally, snow can acquire high salinity from
frost flowers that can possibly exist on a young ice surface
(section 9.4). These processes create a vertical salinity pro-
file within the snowpack that has a maximum value at the
base. The effect of an average 4‰ salinity increase of the
snowpack is to double the value of the dielectric loss. Recall
that the loss is a strong function of salinity while the per-
mittivity is a weak function of salinity but increases with
snow density (section 3.6.2). Snow wetness is expected to
decrease the emissivity in the microwave region and eventu-
ally bring it to values close to those of OW (according to
data in Table 8.10 the emissivity of OW is significantly
lower than that of ice and it is less from the horizontal
polarization than the vertical).
Markus et al.,
[2009] found
the emissivity from wet snow to be very close to values
from OW even before the development of melt ponds. This
is a decrease with respect to the dry snow emissivity. On the
other hand, snow wetness is associated with a rise in the
temperature of the radiating layer. Therefore, the net effect
of the snow wetness on the microwave brightness tem-
perature can be an increase or a decrease.
Lohanick
[1993]
reported a dramatic decrease in brightness temperature at
10 GHz due to the presence of a slush layer at the snow-ice
interface immediately after the snowfall.
0
5
-10
-15
-20
-25
-30
-35
Snow depth = 1.0 m
Snow density = 0.25 g/cm
3
-40
0
1
2
3
4
Snow wetness (%)
5
6
7
8
9
10
Figure 7.45
Modeled backscattering from surface and volume of snow over land as a function of snow wetness.
Volume scattering decreases as wetness increases, allowing more contribution of surface scattering. The input
parameters to the model are shown [
Koskinen et al.
, 2000, Figure 1, with permission from IEEE].
