Geology Reference
In-Depth Information
6000
280
[1] dark nilas
[2] snow-covered white ice
[3] gray ice
[4] light nilas
[5] frost flowers
[6] small floes and brash ice
AVHRR channel 2
May 7
OW + New ice
4000
275
Light nilas
+ young ice
2000
[3,5]
[3,4,5]
[1,3]
270
[3,4]
[1]
[1]
0
0.0
0.5
1. 0
1. 5
2.0
2.5
3.0
Radiance AVHRR channel 2
265
5000
AVHRR channel 4
[3,5,6]
Light nilas
+ young ice
FY ice
4000
[2]
260
Open water
3000
[2]
2000
New ice
255
1000
0
2
4
Distance (km)
6
8
10
0
75
70
65
60
55
Figure 10.2
Variation of surface temperature, derived from
AVHRR, along a lead at the SHEBA site in the Beaufort Sea on
7 May 1998. Ice types as inferred from video tape are indicated
for selected temperature ranges [
Massom and Comiso,
1994,
Figure 3, with permission from AGU].
Radiance AVHRR channel 4
Figure 10.1
Histograms of AVHRR channel 2 and channel
4 radiances from a region in the Bering Sea during the spring
of 1988, showing the separation of the four surfaces in the
channel 4 data [
Massom and Comiso
, 1994, Figure 3, with
permission from AGU].
from this study is presented in Figure 10.2. It shows the
variation of IST from different thin ice types in a lead.
No unique temperature can be linked to a particular ice
type. Atmospheric temperature contributes to temporal
variations of IST, and the distribution of water in open-
ings within the sea ice cover contributes to its spatial vari-
ations. As for FY ice types it suffices to mention that they
cannot be discriminated based on IST. Although FY ice
temperature is generally colder than young ice tempera-
ture and warmer than MY ice during winter, it varies over
a wide range depending on ice thickness and atmospheric
temperature. While it is possible to use surface tempera-
ture to discriminate between MY ice and seasonal ice
types during fall and winter, it is not possible to apply this
concept to summer ice.
Most of the methods that rely on albedo can discrimi-
nate between sea ice and OW or (to a limited extent)
between ice types based on the narrowband albedo avail-
able from optical sensors such as AVHRR, MODIS, or
the European Medium‐Resolution Imaging Spectrometer
(MERIS). Optical observations from AVHRR have been
used operationally in the European Ocean and Sea Ice
Satellite Application Facility (O&SISAF) to classify sea
ice and OW [
Tonboe and Haarpaintner,
2003]. The cloud
“contamination” in optical sensors' observations has
been addressed in a few early studies to facilitate sea
ice discrimination from the surrounding OW in the
range of emissivity from these surfaces in the TIR region
as presented in section 7.3.3). The four surface types are
associated with distinguishable modes in the distribution
of the TIR data. The algorithm by
Massom and Comiso
[1994] uses channel 2 and 4 radiances to identify clusters
of the data from the four surface types and set appropri-
ate thresholds to separate them. Training data were used
to assist the selection of the threshold. The authors stated
that unambiguous distinction of more ice types may be
very difficult because the albedo and emissivity of the dif-
ferent classes have overlapping values. A well‐known
challenge of using TIR data is the difficulty of cloud
detection during polar nights. For that reason, the afore-
mentioned study was limited to daylight conditions. In
later studies methods were developed for nighttime cloud
detection [
Spangenberg et al.,
2002;
Salomon et al.,
2007].
Haggerty et al.
[2003] explored the spatial variations in
ice surface temperature (IST) of young sea ice types in the
vicinity of the Surface Heat Budget of the Arctic Ocean
(SHEBA) site in the spring of 1998 (SHEBA field experi-
ment was conducted over a year‐long period from
October 1997 to October 1998 in the Beaufort and
Chukchi Seas [
Perovich et al.,
1999]). They calculated the
IST using the split‐window technique [equation (7.55)]
with input from AVHRR TIR channels 4 and 5. A graph
