6. COMPARISON OF RSI DATA WITH MDOIS DATA
In this study, authors were considering to use optical sensor
data as the “truth data” for evaluating thin ice area extracted
with AMSR-E data. Now, we have got good relationship
between the ice thickness and RSI data when the ice thicknesses
were less than 20cm. However, since one pixel size of AMSR-E
36.5GHz band is about the swath of RSI. Direct comparison of
RSI data with AMSR-E data is nonsense. So, we decided to
compare RSI data with MODIS data. The IFOV size of MODIS
is 250m, which means that one pixel size of AMSR-E 36.5GHz
band is about 100 x 100 pixels of MODIS image. This is
appropriate size for using MODIS image as “truth data” of
AMSR-E (see Figure 8).
On February 19, 2011, RSI and MODIS observed Saroma
Lake as shown on Figure 6. The box area in the RSI and
MODIS images of the Saroma Lake was compared. Figure 7
shows the scatter plots of RSI band 3 and MODIS band 1.
(a) RSI image
CREER
: (C) MODIS image
Figure 6. Comparison of RAI image with MODIS image
(Saroma Lake, February 19, 2011)
MODIS Band1 vs RSI Band3
160 ys0:66x* 203897
R? = 0.94
A
Oo
io
e
=
Q
e
RSI Band3 Radiance (W/m2/umésr)
©
o
0 20 40 60 80 100 120 140 160 180 200 220
MODIS Band1 Radiance (W/m2/um/sr)
Figure 7. Scatter plots of RSI band 3 versus MODIS band 1
(Saroma Lake, February 19, 2011)
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
It is clear that both data have very high correlation(R2=0.94).
This result suggests the possibility of estimating ice thickness
with MODIS data under the less snow and cloud free condition.
At least, it is fare to say that thin ice area may be estimated
using MODIS image.
7. EXTRACTION OF THIN ICE AREA WITH AMSR-E
DATA
The main purpose of this study was to extract thin sea ice
area using passive microwave AMSR-E data. However,
considering the difficulty of discriminating thin sea ice from
thick sea ice in the low ice concentration areas, we decided to
extract only the thin sea ice area with 80% or higher sea ice
concentration. The target will be focused only to seasonal sea
ice zones to reject the influence of multi-year ice. Thus, the
data will only be calculated before the melting season of the Sea
of Okhotsk to reduce the effects of flooding.
Figure 8 show MODIS image and sea ice concentration
image derived from AMSR-E data using Bootstrap Algorithm
(Comiso, 2009). Since both sensors are on the same Aqua
satellite, the data are taken at exactly the same time. In the
MODIS images, blue and red are assigned to band 1(visible)
and green to band 2(near infrared). In Figure 8(a), dark purple
area can be seen along the coast of Russia. As explained in the
previous chapters, dark ice area in MODIS image can be
estimated as thin ice area. Especially, since the reflectance of
ice reduces in band 2 when the ice is covered or surrounded by
water, the thin ice areas are likely to appear in purple in the
MODIS image.
In order to examine the brightness temperature
characteristics of big ice floe, thin ice, mixed ice, and open
water, the sample area of each item was selected in the MODIS
image as shown on Figure 9. Then the sample areas are overlaid
on the AMSR-E image, and the AMSR-E data of the sample
areas were extracted.
(a) MODIS image (b) AMSR-E ice concentration
Figure 8. Comparison of MODIS and AMSR-E images.
(Sea of Okhotsk, Feb. 7, 2009)
(a)Big ice fioc: (b) Thin ice (c) Mixed ice (d) Mixed ice
Figure 9. Sample area of different ice types extracted from
MODIS image (Sea of Okhotsk, Feb. 7, 2009)
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