thin ice area extraction in the seasonal sea ice zones …

THIN ICE AREA EXTRACTION IN THE SEASONAL SEA ICE ZONES OF

ABSTRACT: Sea ice has an important role of reflecting the solar radiation back into space. In addition, the heatstrongly affected by the ice thickness difference. Therefore, iceprevious study, the authors haveSea of Okhotsk. The basic idea of the algorithm is to use the brightness temperature scatter plots ofdifference (V-ice areas. However, two problemswas that some of the consolidated ice were misextraction algorithm to solve these problems. By adjusting thescatter plots of AMSR2 19GHzextracted in the Bering Sea. The authors also introduced an equation using the brightnessand horizontal polarization to reject the thin ice area misextractedAMSR2 data, most of theLawrence with good result. The thin ice area extracted data are planed to be approved by JAXA

Since 1978, passive microwave radiometers, including AMSR2 onboard GCOMthe earth for 40 years. The longthe passive microwave observation showed clear decline trend of the Arctic sea ice cover2018 etc.). The warming in the Fifth Assessment Report of IPCC (2014).Usually, sea ice extent is calculated from sea ice concentration derived from brightness temperatures measured by passive microwave radiometers. fundamental parameter of sea ice which can be calculated from brightness temperradiometers. There are number of sea ice concentration algorithms including NASA Team Algorithm (Cavarieli et al., 1984), Bootstrap Algorithm (Comiso, 1995)(Svendsen et al.Since the heat thickness (Maykut, 1978)parameter of sea ice. However, the sea ice thickness information cannot be estimated from the sea ice concentration data. on estimating ice thiacquired from passive microwave radiometers onboard satellites have been done in the past inc(2002), Martin et al. (2005), and Tamura et al.the detailed validatiothickness is still on the way. Estimating ice thickness from passive microwave radiometer is not easy. been developbrightness temperature scatter plots opolarization difference (VSea of Okhotsk“thin ice” is defined as the ice which thickness is around less than 30cm. Cho et al

*Kohei Cho, Tokai University Research & Information Center, 2-28Japan

THIN ICE AREA EXTRACTION IN THE SEASONAL SEA ICE ZONES OFTHE NORTHERN HEMISPHERE USING ASMR2 DATA

1Tokai University, 2

KEY WORDS:

ABSTRACT:

Sea ice has an important role of reflecting the solar radiation back into space. In addition, the heatstrongly affected by the ice thickness difference. Therefore, iceprevious study, the authors haveSea of Okhotsk. The basic idea of the algorithm is to use the brightness temperature scatter plots of

-H) vs 19GHz V polarization. The algorithm was alsoice areas. However, two problems

that some of the consolidated ice were misextraction algorithm to solve these problems. By adjusting thescatter plots of AMSR2 19GHzextracted in the Bering Sea. The authors also introduced an equation using the brightnessand horizontal polarization to reject the thin ice area misextractedAMSR2 data, most of the thin ice areas in the Bering Sea were well extracted. The algorithm was also applied to the Gulf ofLawrence with good result. The thin ice area extracted data are planed to be approved by JAXA

1. INTRODUCTION

passive microwave radiometers, including AMSR2 onboard GCOM-W satellite, have been continuously observing the earth for 40 years. The longthe passive microwave observation showed clear decline trend of the Arctic sea ice cover (Comiso, 2012, JAXA, 2012

The result is referredwarming in the Fifth Assessment Report of IPCC (2014).Usually, sea ice extent is calculated from sea ice concentration derived from brightness temperatures measured by passive microwave radiometers. Ice concentration is the most fundamental parameter of sea ice which can be calculated from brightness temperatures measured by passive microwave radiometers. There are number of sea ice concentration algorithms including NASA Team Algorithm (Cavarieli et al.,

Bootstrap Algorithm (Comiso, 1995)vendsen et al. .1987).

Since the heat flux of ice is strongly affected by the ice (Maykut, 1978), i

parameter of sea ice. However, the sea ice thickness information cannot be estimated from the sea ice concentration data. on estimating ice thickness from the acquired from passive microwave radiometers onboard satellites

done in the past inc(2002), Martin et al. (2005), and Tamura et al.the detailed validation of the accuracy of the estimated sea ice thickness is still on the way. Estimating ice thickness from passive microwave radiometer is not easy.

developing a method tobrightness temperature scatter plots opolarization difference (V-H) vs 19GHz V polarizationSea of Okhotsk (Cho et. Al, 2012, “thin ice” is defined as the ice which thickness is around less

Cho et al. (2011, 2012) has done detailed stud

Kohei Cho, Tokai University Research & Information 28-4, Tomigaya, Shibuya


Tokai University, 2-

KEY WORDS: sea ice, passive microwave radiometer, global warming, GCOM

Sea ice has an important role of reflecting the solar radiation back into space. In addition, the heatstrongly affected by the ice thickness difference. Therefore, iceprevious study, the authors have developed a thin ice area extraction algorithm using passive microwave radiometer AMSR2Sea of Okhotsk. The basic idea of the algorithm is to use the brightness temperature scatter plots of

H) vs 19GHz V polarization. The algorithm was alsoice areas. However, two problems have become clear. One was that some the thin ice areas were not well extracted, and the other

that some of the consolidated ice were misextraction algorithm to solve these problems. By adjusting thescatter plots of AMSR2 19GHz polarization difference (Vextracted in the Bering Sea. The authors also introduced an equation using the brightnessand horizontal polarization to reject the thin ice area misextracted

thin ice areas in the Bering Sea were well extracted. The algorithm was also applied to the Gulf ofLawrence with good result. The thin ice area extracted data are planed to be approved by JAXA

INTRODUCTION

passive microwave radiometers, including AMSR2 W satellite, have been continuously observing

the earth for 40 years. The long-term sea ice ethe passive microwave observation showed clear decline trend

(Comiso, 2012, JAXA, 2012referred as an evidence of global

warming in the Fifth Assessment Report of IPCC (2014).Usually, sea ice extent is calculated from sea ice concentration derived from brightness temperatures measured by passive

Ice concentration is the most fundamental parameter of sea ice which can be calculated from

atures measured by passive microwave radiometers. There are number of sea ice concentration algorithms including NASA Team Algorithm (Cavarieli et al.,

Bootstrap Algorithm (Comiso, 1995)

flux of ice is strongly affected by the ice ice thickness is another important

parameter of sea ice. However, the sea ice thickness information cannot be estimated from the sea ice concentration data.

ckness from the brightness temperature acquired from passive microwave radiometers onboard satellites

done in the past including those of Tateyama et al. (2002), Martin et al. (2005), and Tamura et al.

n of the accuracy of the estimated sea ice thickness is still on the way. Estimating ice thickness from passive microwave radiometer is not easy.

a method to extract thin ice area using brightness temperature scatter plots of AMSR2 19GHz

H) vs 19GHz V polarization(Cho et. Al, 2012, 2014, 2015

“thin ice” is defined as the ice which thickness is around less 2011, 2012) has done detailed stud

Kohei Cho, Tokai University Research & Information 4, Tomigaya, Shibuya-ku, Tokyo151


K. Cho 1*, K. Miyao

-28-4, Tomigaya, Shibuya

Commission III, WG III/2

sea ice, passive microwave radiometer, global warming, GCOM

Sea ice has an important role of reflecting the solar radiation back into space. In addition, the heatstrongly affected by the ice thickness difference. Therefore, ice

developed a thin ice area extraction algorithm using passive microwave radiometer AMSR2Sea of Okhotsk. The basic idea of the algorithm is to use the brightness temperature scatter plots of

H) vs 19GHz V polarization. The algorithm was alsohave become clear. One was that some the thin ice areas were not well extracted, and the other

that some of the consolidated ice were mis-extracted as thin ice areas. In this study, the authorsextraction algorithm to solve these problems. By adjusting the

polarization difference (Vextracted in the Bering Sea. The authors also introduced an equation using the brightnessand horizontal polarization to reject the thin ice area misextracted


INTRODUCTION


term sea ice extent derived from the passive microwave observation showed clear decline trend

(Comiso, 2012, JAXA, 2012, NSIDC, as an evidence of global

warming in the Fifth Assessment Report of IPCC (2014).Usually, sea ice extent is calculated from sea ice concentration derived from brightness temperatures measured by passive



Bootstrap Algorithm (Comiso, 1995) and ASI Algorithm

flux of ice is strongly affected by the ice ce thickness is another important

parameter of sea ice. However, the sea ice thickness information cannot be estimated from the sea ice concentration data. Studies

brightness temperature acquired from passive microwave radiometers onboard satellites

uding those of Tateyama et al. (2002), Martin et al. (2005), and Tamura et al. (2007). However,

n of the accuracy of the estimated sea ice thickness is still on the way. Estimating ice thickness from passive microwave radiometer is not easy. The authors have

thin ice area using f AMSR2 19GHz

H) vs 19GHz V polarization for the , 2015). In this study,

“thin ice” is defined as the ice which thickness is around less 2011, 2012) has done detailed studies

Kohei Cho, Tokai University Research & Information ku, Tokyo151-0063,


*, K. Miyao 1, K. Naoki

4, Tomigaya, Shibuya-ku, Tokyo, Japan,



Sea ice has an important role of reflecting the solar radiation back into space. In addition, the heatstrongly affected by the ice thickness difference. Therefore, ice thickness is one of the most important parameters of sea ice. In our


H) vs 19GHz V polarization. The algorithm was also applicable to the Bering Sea, and could extract most ofhave become clear. One was that some the thin ice areas were not well extracted, and the other

extracted as thin ice areas. In this study, the authorsextraction algorithm to solve these problems. By adjusting the parameters of the algorithm applied to the brightness temperature

polarization difference (V-H) vs 19GHz V polarization, most of the thin ice areasextracted in the Bering Sea. The authors also introduced an equation using the brightnessand horizontal polarization to reject the thin ice area misextracted over consolidated ice. By applying the



xtent derived from the passive microwave observation showed clear decline trend

, NSIDC, as an evidence of global

warming in the Fifth Assessment Report of IPCC (2014). Usually, sea ice extent is calculated from sea ice concentration derived from brightness temperatures measured by passive



and ASI Algorithm

flux of ice is strongly affected by the ice ce thickness is another important

parameter of sea ice. However, the sea ice thickness information Studies

brightness temperature data acquired from passive microwave radiometers onboard satellites

uding those of Tateyama et al. However,

n of the accuracy of the estimated sea ice thickness is still on the way. Estimating ice thickness from

authors have thin ice area using f AMSR2 19GHz

for the In this study,

“thin ice” is defined as the ice which thickness is around less ies on

comparing the data observed by and MODIS thickness is less than the ice such as used of extract thin icecomparing with the MODIS images. The result of applying the thin ice area extraction algorithmThin Ice Algorithm,zones of the northern hemisphere including the Sea of Okhotsk, Bering Sea, and Gulf

Figure 1 show the test sites study whiOkhotskthe Gulf of Saint LawrenceAll three are seasonal sezones of the northern hemisphere. Okhotsk north side of Hokkaido, Japan, surrounded by the Island of Saeastern Siberian coast, Kamchatka Peninsula and Kuril Islands. The sea of the most southern seasonal sea ice zones in the northernhemisphere

Kohei Cho, Tokai University Research & Information 0063,


, K. Naoki 1

ku, Tokyo, Japan,



Sea ice has an important role of reflecting the solar radiation back into space. In addition, the heatthickness is one of the most important parameters of sea ice. In our


applicable to the Bering Sea, and could extract most ofhave become clear. One was that some the thin ice areas were not well extracted, and the other

extracted as thin ice areas. In this study, the authorsparameters of the algorithm applied to the brightness temperature

H) vs 19GHz V polarization, most of the thin ice areasextracted in the Bering Sea. The authors also introduced an equation using the brightness

over consolidated ice. By applying the thin ice areas in the Bering Sea were well extracted. The algorithm was also applied to the Gulf of

Lawrence with good result. The thin ice area extracted data are planed to be approved by JAXA

paring the in situ data observed by optical sensorand MODIS on Aqua/Terrathickness is less than the ice thickness difference can be detected with optical sensors such as RSI and MODIS.used as the reference of identifying thin ice areas, of extract thin icecomparing with the MODIS images. The result of applying the thin ice area extraction algorithmThin Ice Algorithm,zones of the northern hemisphere including the Sea of Okhotsk, Bering Sea, and Gulf

2. TEST SITE

Figure 1 show the map of the test sites analyzedstudy which are the Sea of Okhotsk, the Bering sea and

Gulf of Saint LawrenceAll three are seasonal sezones of the northern hemisphere. The Sea of Okhotsk is located north side of Hokkaido, Japan, surrounded by the Island of Sakhalin and eastern Siberian coast, Kamchatka Peninsula and Kuril Islands. The sea of the most southern seasonal sea ice zones in the northernhemisphere, and many thin


kohei.cho@tokai


Sea ice has an important role of reflecting the solar radiation back into space. In addition, the heat flux of ice in thin ice areas isthickness is one of the most important parameters of sea ice. In our



extracted as thin ice areas. In this study, the authors have improved tparameters of the algorithm applied to the brightness temperature

H) vs 19GHz V polarization, most of the thin ice areasextracted in the Bering Sea. The authors also introduced an equation using the brightness temperatures difference of 89GHz vertical

over consolidated ice. By applying the thin ice areas in the Bering Sea were well extracted. The algorithm was also applied to the Gulf of

Lawrence with good result. The thin ice area extracted data are planed to be approved by JAXA as a AMSR2 re

in situ ice thickness measurement optical sensors such as

on Aqua/Terra. The result suggested that if the ice thickness is less than 30cm, under the less snow cover condition,

thickness difference can be detected with optical sensors MODIS. In this study,

as the reference of identifying thin ice areas, of extract thin ice area with AMSR2 data are validated by comparing with the MODIS images. The result of applying the thin ice area extraction algorithmThin Ice Algorithm, to AMSR2 data in the seasonal sea ice zones of the northern hemisphere including the Sea of Okhotsk, Bering Sea, and Gulf of St. Lawrence are presented in this paper.

SITES

map of the ed in this the Sea of

Bering sea and Gulf of Saint Lawrence.

All three are seasonal sea ice zones of the northern

he Sea of located at the

north side of Hokkaido, Japan, surrounded by the

khalin and eastern Siberian coast, Kamchatka Peninsula and Kuril Islands. The sea is one of the most southern seasonal sea ice zones in the northern

many thin Figure. 1


[email protected]

sea ice, passive microwave radiometer, global warming, GCOM-W

flux of ice in thin ice areas isthickness is one of the most important parameters of sea ice. In our

developed a thin ice area extraction algorithm using passive microwave radiometer AMSR2Sea of Okhotsk. The basic idea of the algorithm is to use the brightness temperature scatter plots of AMSR2 19GHz polarization


have improved the thin ice area parameters of the algorithm applied to the brightness temperature

H) vs 19GHz V polarization, most of the thin ice areas temperatures difference of 89GHz vertical

over consolidated ice. By applying the above two methods to thin ice areas in the Bering Sea were well extracted. The algorithm was also applied to the Gulf of

as a AMSR2 research product.

ckness measurement s such as RSI on FORMOSAT

. The result suggested that if the ice under the less snow cover condition,

thickness difference can be detected with optical sensors In this study, the MODIS images

as the reference of identifying thin ice areas, area with AMSR2 data are validated by

comparing with the MODIS images. The result of applying the thin ice area extraction algorithm, hereafter refe

to AMSR2 data in the seasonal sea ice zones of the northern hemisphere including the Sea of Okhotsk,

of St. Lawrence are presented in this paper.

Figure. 1 Map of the t

THIN ICE AREA EXTRACTION IN THE SEASONAL SEA ICE ZONES OF

flux of ice in thin ice areas isthickness is one of the most important parameters of sea ice. In our

developed a thin ice area extraction algorithm using passive microwave radiometer AMSR2 for the AMSR2 19GHz polarization

applicable to the Bering Sea, and could extract most of the thin have become clear. One was that some the thin ice areas were not well extracted, and the other

he thin ice area parameters of the algorithm applied to the brightness temperature

were also well temperatures difference of 89GHz vertical

above two methods to thin ice areas in the Bering Sea were well extracted. The algorithm was also applied to the Gulf of St.

search product.

ckness measurement result with the on FORMOSAT-2


thickness difference can be detected with optical sensors MODIS images are

as the reference of identifying thin ice areas, the possibility area with AMSR2 data are validated by

comparing with the MODIS images. The result of applying the ferred to as the



Map of the test sites

flux of ice in thin ice areas is thickness is one of the most important parameters of sea ice. In our

AMSR2 19GHz polarization

the thin have become clear. One was that some the thin ice areas were not well extracted, and the other

he thin ice area parameters of the algorithm applied to the brightness temperature

l temperatures difference of 89GHz vertical

above two methods to St.

the 2


thickness difference can be detected with optical sensors are

possibility area with AMSR2 data are validated by

comparing with the MODIS images. The result of applying the the



The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W7, 2019 TC III WG III/2,10 Joint Workshop “Multidisciplinary Remote Sensing for Environmental Monitoring”, 12–14 March 2019, Kyoto, Japan

This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W7-5-2019 | © Authors 2019. CC BY 4.0 License.

5

ice areas can be found in the the northernmost by the Siberia, the Kamchatka Peninsula, the Alaska Peninsula and the Aleutian Islands. Ocean by the Bering Strait. The Gulf of St. Lawrence is a kind of inland sea locAmerica's Great Lakes via theAtlantic Ocean.

The brightness temperature data microwave radiometer were used in this study2012 and AMSR2 has been observing the earth for over 6 yeTable 1 showconcentration data derived from AMSR2 data using Bootstrap Algorithm (Comiso, 2009)order to identify thin iMODIS onboard Aqua satellite were usedshow the specifications of MODIS. As for MODIS, only the Band 1 and 2 which have the were used in this study.distribution of sea ice can be observed from MODIS images. Since Aqua and GCOMthe frame work of constellation of same area four minuonboard GCOMeffective validation data for AMSR2 data.

Table 1Frequency

(polarization)

7GHzV,H）

11GHz (V,H)19GHz(V,H24GHz(V,H36GHz(V,H89GHz(V,H

Table 2Band

1 0.6202 0.841

4.1 Sample Area selection

Figure 2 show the comparison of siAMSR2 ice concentration image and of Okhotsk taken on of sea ice can be identified from the AMSR2 ice concentration image as shown on Figure 2(a).However, it is difficult to identify ice thickness differences or thin ice areas from the image. On the other hand, sea ice distributions can be observedimage of MODISshown on Figure 2(b)sample areas of thin sea ice, big icesea ice are selected

can be found in the the northernmost part of the Pacific Ocean, which is surrounded by the Siberia, the Kamchatka Peninsula, the Alaska Peninsula and the Aleutian Islands. The Bering Sea is connected to Arctic Ocean by the Bering Strait. The Gulf of St. Lawrence is a kind of inland sea located in eastern Canada. It is the outlet of North America's Great Lakes via theAtlantic Ocean.

3. ANALY

The brightness temperature data radiometer AMSR2

in this study. GCOM2012 and AMSR2 has been observing the earth for over 6 yeTable 1 shows the specifications of AMSR2. concentration data derived from AMSR2 data using Bootstrap

(Comiso, 2009) order to identify thin ice areaMODIS onboard Aqua satellite were usedshow the specifications of MODIS. As for MODIS, only the Band 1 and 2 which have the were used in this study. Under the cloud free condidistribution of sea ice can be observed from MODIS images.

Aqua and GCOM-W are in the same orbital “track” under the frame work of the NASA’s A

of satellites, MODIS onboard Aqua observesame area four minutes after the observation of AMSR2 onboard GCOM-W. Therefore, MODIeffective validation data for AMSR2 data.

Table 1. Specifications of AMSR2Frequency

(polarization) IFOV

V,H） 35×62

(V,H) 24×42 V,H） 14×22 V,H） 15×26 V,H） 7×12 kmV,H） 3×5 km

Table 2. Specifications of MODISWavelength

0.620-0.670 μm0.841-0.876 μm

4. RESEARCH METHOD

Sample Area selection

Figure 2 show the comparison of siAMSR2 ice concentration image and

taken on February 27, 2013can be identified from the AMSR2 ice concentration

as shown on Figure 2(a).However, it is difficult to identify ice thickness differences or thin ice areas from the image. On the other hand,

ributions can be observedMODIS(Band 1 to blue and red, Band 2 to green) Figure 2(b). In ou

of thin sea ice, big iceare selected in this study as sown on Figure

can be found in the sea. The Bering Sea is part of the Pacific Ocean, which is surrounded

by the Siberia, the Kamchatka Peninsula, the Alaska Peninsula The Bering Sea is connected to Arctic

Ocean by the Bering Strait. The Gulf of St. Lawrence is a kind d in eastern Canada. It is the outlet of North

America's Great Lakes via the Saint Lawrence

ANALY ZED DATA

The brightness temperature data acquired frAMSR2 onboard GCOM-W was launched by JAXA in

2012 and AMSR2 has been observing the earth for over 6 yethe specifications of AMSR2.

concentration data derived from AMSR2 data using Bootstrap were also used in this study. I

ce areas, data collected by optical sensor MODIS onboard Aqua satellite were used as referenceshow the specifications of MODIS. As for MODIS, only the Band 1 and 2 which have the highest spatial

Under the cloud free condidistribution of sea ice can be observed from MODIS images.

are in the same orbital “track” under the NASA’s A-Train

, MODIS onboard Aqua observetes after the observation of AMSR2

W. Therefore, MODIS data is one of the most effective validation data for AMSR2 data.

Specifications of AMSR2

IFOV Swath

km

1450km

km km km km km

pecifications of MODIS

IFOV μm

250 m μm

RESEARCH METHOD

Sample Area selection

Figure 2 show the comparison of simultaneously collected AMSR2 ice concentration image and MODIS image

February 27, 2013. Tcan be identified from the AMSR2 ice concentration

as shown on Figure 2(a). However, it is difficult to identify ice thickness differences or thin ice areas from the image. On the other hand,

ributions can be observed in the (Band 1 to blue and red, Band 2 to green)

our thin ice algorithm, we first of thin sea ice, big ice floe, open water and mixed

in this study as sown on Figure

The Bering Sea is located in part of the Pacific Ocean, which is surrounded



Saint Lawrence River into the

ED DATA

acquired from passive onboard GCOM-W satellite

W was launched by JAXA in 2012 and AMSR2 has been observing the earth for over 6 ye

the specifications of AMSR2. The ice concentration data derived from AMSR2 data using Bootstrap

also used in this study. I, data collected by optical sensor

as reference. Table 3 show the specifications of MODIS. As for MODIS, only the

spatial resolution of 250m Under the cloud free condition, detailed

distribution of sea ice can be observed from MODIS images. are in the same orbital “track” under

(NASA, 2012), MODIS onboard Aqua observe

tes after the observation of AMSR2 data is one of the most

Specifications of AMSR2

Swath Incident angle

1450 55 deg

pecifications of MODIS Swath

2330 km

RESEARCH METHOD

multaneously collected MODIS image of the

The clear distribution can be identified from the AMSR2 ice concentration

However, it is difficult to identify ice thickness differences or thin ice areas from the image. On the other hand, more detailed

in the color composite (Band 1 to blue and red, Band 2 to green)

thin ice algorithm, we first select floe, open water and mixed

in this study as sown on Figure 2 and 3.

located in part of the Pacific Ocean, which is surrounded



River into the

passive W satellite

W was launched by JAXA in 2012 and AMSR2 has been observing the earth for over 6 years.

The ice concentration data derived from AMSR2 data using Bootstrap

also used in this study. In , data collected by optical sensor

Table 3 show the specifications of MODIS. As for MODIS, only the

of 250m tion, detailed

distribution of sea ice can be observed from MODIS images. are in the same orbital “track” under

(NASA, 2012), the , MODIS onboard Aqua observed the

tes after the observation of AMSR2 data is one of the most

Incident angle

deg

km

multaneously collected of the Sea

distribution can be identified from the AMSR2 ice concentration

However, it is difficult to identify ice thickness differences or more detailed

color composite (Band 1 to blue and red, Band 2 to green) as

thin ice algorithm, we first select floe, open water and mixed

.

(a) MODIS imageFigur

(a)Big ice Figure

4.2

Figure 19GHz (V2013. In this scatter plot, representsis to extract the thiequations to Vertical (Tb19GHzV)>(Tb19GHzVwhere

Figure

(a) MODIS imageFigure 2. Comparison of AMSR2 and MODIS images.

(Sea of Okhotsk,

(a)Big ice floe (b)Thin iceFigure 3. Sample area

MODIS image

Thin Ice Algorithm

Figure 4 shows the scatter plot of AMSR2 19GHz V versus 19GHz (V-H) of the Sea of Okhotsk observed on 2013. In this scatter plot, represents big ice floe. is to extract the thiequations to the brightness temperatures

ertical(V) and Hor

(Tb19GHzV)>T1 (Tb19GHzV－Tb19GHzH) where Tb19GHzH:

Tb19GHzV T1, T2: parameter

Figure 4. Scatter plots of (19GHzV Polarization

(a) MODIS image (b) AMSR2 ice concentration

. Comparison of AMSR2 and MODIS images.(Sea of Okhotsk,

(b)Thin ice (c)Mixed ice . Sample area of different ice types extracted from

MODIS image (Sea of Okhotsk, Feb

Thin Ice Algorithm

shows the scatter plot of AMSR2 19GHz V versus H) of the Sea of Okhotsk observed on

2013. In this scatter plot, ▲ ice floe. The basic idea of

is to extract the thin ice area by applying the following two the brightness temperatures

rizontal(H) polarization of

Tb19GHzH) ＞ -Tb19GHzV

Tb19GHzH: Tb of AMSR2 19GV: Tb of AMSR2 19G

T1, T2: parameters adjusted to

Scatter plots of (19GHzV olarization (Sea of Okhotsk, Feb

(b) AMSR2 ice concentration

. Comparison of AMSR2 and MODIS images.(Sea of Okhotsk, February 27,2013)

(c)Mixed ice (d)Open waterof different ice types extracted from

(Sea of Okhotsk, Feb

shows the scatter plot of AMSR2 19GHz V versus H) of the Sea of Okhotsk observed on

represents thin ice basic idea of our Thin Ice A

by applying the following two the brightness temperatures (T

larization of AMSR2.

Tb19GHzV + T2

of AMSR2 19GHz H polof AMSR2 19GHz V-pol

s adjusted to particular

Scatter plots of (19GHzV – 19GHzH)(Sea of Okhotsk, Feb.

(b) AMSR2 ice concentration. Comparison of AMSR2 and MODIS images.

February 27,2013)

(d)Open waterof different ice types extracted from

(Sea of Okhotsk, Feb. 27, 2013)

shows the scatter plot of AMSR2 19GHz V versus H) of the Sea of Okhotsk observed on February27,

thin ice and ■our Thin Ice Algorithm

by applying the following two Tb) of 19GHz

AMSR2.

(1) T2 (2) polarization polarization

particular sea ice zone.

19GHzH) Vs 19GHzV

27, 2013)

(b) AMSR2 ice concentration . Comparison of AMSR2 and MODIS images.

(d)Open water of different ice types extracted from

shows the scatter plot of AMSR2 19GHz V versus February27,

■ m

by applying the following two 19GHz



6

It is impossible to identify ice concentration areas. ice area with 80% or higher sea ice concentration.words, our target is thin ice area which concentrathan 80%/ The microwave brightness temperature of water is much lower in H polarization than in that of V polarization. Since thin ice areas are rather wettemperature of thin ice areapolarization than in that of V polarization. the microwave brightness temperature of consolidated ice does not show big difference between V and H polariConsidering these characteristics, the authors have introduced equation (2) for extracting thin ice area.

5.1 Sea of Okhotsk

Firstly, the authors have applied the AMSR2 data of the Sea of Okhotsk. Figure AMSR2 sea ice concentration image of February 27, 2013. The cyan areas in the image show the “thin ice areas” extracted using AMSR2 data using equations (1) and (2). T2=300K are areas were overlaid on the simultaneously collected MODIS image for evaluation as shown on Figure all but most of the thin ice areas which are appearing in dark purple in the MODIS image are extracted with the proposed method.

(a)AMSR2 image Figure 5. Thin ice area ext

area) 5.2 Bering Sea

The authors have applied the scenes of AMSR2 data 19, 2016. Figure versus 19GHz (V2016. The blue as thin ice area with equation (1) and (2).extracted sea ice areas(

It is impossible to identify ice thickness differenceice concentration areas. The equation (1) is used ice area with 80% or higher sea ice concentration.

rget is thin ice area which concentraThe microwave brightness temperature of water is

much lower in H polarization than in that of V polarization. thin ice areas are rather wet

of thin ice areapolarization than in that of V polarization. the microwave brightness temperature of consolidated ice does not show big difference between V and H polariConsidering these characteristics, the authors have introduced

for extracting thin ice area.

5. EXTRACTED RESULT

Sea of Okhotsk

he authors have applied the AMSR2 data of the Sea of Okhotsk. Figure AMSR2 sea ice concentration image of February 27, 2013. The cyan areas in the image show the “thin ice areas” extracted sing AMSR2 data using equations (1) and (2).

are specified for the Sea of Okhotsk. areas were overlaid on the simultaneously collected MODIS image for evaluation as shown on Figure all but most of the thin ice areas which are appearing in dark

e MODIS image are extracted with the proposed

(a)AMSR2 image . Thin ice area extraction result (Cyan: extracted

area) (Sea of Okhotsk, Feb

Bering Sea

The authors have applied the AMSR2 data for the Figure 6 shows the scatter plot of AMSR2 19GHz

versus 19GHz (V-H) of the Bering Sea observed on blue meshed area represents the

thin ice area with equation (1) and (2).extracted sea ice areas(■) are distributed outside of the blue

ice thickness differenceThe equation (1) is used

ice area with 80% or higher sea ice concentration.rget is thin ice area which concentra

The microwave brightness temperature of water is much lower in H polarization than in that of V polarization.

thin ice areas are rather wet, the microwave brightness of thin ice areas become much lower in H

polarization than in that of V polarization. the microwave brightness temperature of consolidated ice does not show big difference between V and H polariConsidering these characteristics, the authors have introduced

for extracting thin ice area.

EXTRACTED RESULT

he authors have applied the Thin Ice AlgorithmAMSR2 data of the Sea of Okhotsk. Figure AMSR2 sea ice concentration image of February 27, 2013. The cyan areas in the image show the “thin ice areas” extracted sing AMSR2 data using equations (1) and (2).

specified for the Sea of Okhotsk. areas were overlaid on the simultaneously collected MODIS image for evaluation as shown on Figure 5 (b). It shows that not all but most of the thin ice areas which are appearing in dark


(b) MODIS image

raction result (Cyan: extracted(Sea of Okhotsk, Feb. 27, 2013)

The authors have applied the Thin Ice Algorithmthe Bering Sea

shows the scatter plot of AMSR2 19GHzBering Sea observed on

meshed area represents the thin ice area with equation (1) and (2). It is clear that the un

are distributed outside of the blue

ice thickness difference in the low The equation (1) is used to extract

ice area with 80% or higher sea ice concentration. In other rget is thin ice area which concentration is higher


the microwave brightness become much lower in H

polarization than in that of V polarization. On the other hathe microwave brightness temperature of consolidated ice does not show big difference between V and H polarization. Considering these characteristics, the authors have introduced

EXTRACTED RESULT

Thin Ice AlgorithmAMSR2 data of the Sea of Okhotsk. Figure 5(a) shows the AMSR2 sea ice concentration image of February 27, 2013. The cyan areas in the image show the “thin ice areas” extracted sing AMSR2 data using equations (1) and (2). T1=245K and

specified for the Sea of Okhotsk. The extracted areas were overlaid on the simultaneously collected MODIS

(b). It shows that not all but most of the thin ice areas which are appearing in dark


(b) MODIS image raction result (Cyan: extracted

27, 2013)

Algorithm to several observed on March

shows the scatter plot of AMSR2 19GHzBering Sea observed on March 19,

meshed area represents the area to be extracted It is clear that the un


in the low to extract sea

In other tion is higher


the microwave brightness become much lower in H

other hand, the microwave brightness temperature of consolidated ice does

zation. Considering these characteristics, the authors have introduced

Thin Ice Algorithm to (a) shows the

AMSR2 sea ice concentration image of February 27, 2013. The cyan areas in the image show the “thin ice areas” extracted

T1=245K and The extracted

areas were overlaid on the simultaneously collected MODIS (b). It shows that not

all but most of the thin ice areas which are appearing in dark e MODIS image are extracted with the proposed

(b) MODIS image

raction result (Cyan: extracted

several observed on March

shows the scatter plot of AMSR2 19GHz V March 19,

area to be extracted It is clear that the un-


meshed areaT1 of equationparameter T2extractinghave added the following equation to the algorithmet al, 2

(Tb where Figure AMSR2 Bering Sea Figure

.

Figure

meshed area．Therefore, of equation (1)

arameter T2=300K. Iextracting some of the big ice floe as thin ice areahave added the following equation to the algorithmet al, 2018)

(Tb89GHzV－Tbwhere Tb89GHzV Tb89GHzH:

Figure 7 show the AMSR2 sea ice concentration image Bering Sea observed on

Figure 6. Scatter plots of Polarization

Figure 7. Thin ice area extraction result (Cyan: extracted area) (

Therefore, the authors(1) from 245K to235K.

=300K. In order to reject the effect of missome of the big ice floe as thin ice area

have added the following equation to the algorithm

Tb89GHzH) ＞20V: Tb of AMSR2

GHzH: Tb of AMSR2

show the extracted thin ice sea ice concentration image

observed on March 19,

Scatter plots of (19GHzV olarization (Bering Sea, Mar

(a)AMSR2 image

(b) MODIS image. Thin ice area extraction result (Cyan: extracted

(Bering Sea, Feb

the authors have changesm 245K to235K. No change to the

n order to reject the effect of missome of the big ice floe as thin ice area

have added the following equation to the algorithm

20K of AMSR2 89GHz V polarizationof AMSR2 89GHz H polarization

thin ice areas overlaid on the sea ice concentration image and MODIS image

March 19, 2016.

19GHzV – 19GHzH)Bering Sea, Mar. 19, 2016

AMSR2 image

MODIS image . Thin ice area extraction result (Cyan: extracted

Bering Sea, Feb. 10, 2014)

changes parameter No change to the

n order to reject the effect of mis-some of the big ice floe as thin ice areas, the authors

have added the following equation to the algorithm( see .Miyao

(3) polarization

9GHz H polarization

overlaid on the and MODIS image of the

) Vs 19GHzV 19, 2016)

. Thin ice area extraction result (Cyan: extracted

parameter No change to the

-s, the authors

Miyao

overlaid on the the



7

5.3 Gulf of St. Lawrence

Figure 8 show 19GHz (V-H) of the Gulf of St. Lawrence observed on March 1, 2015. The distribution of the data also suggests the possibility of applying the same algorithm to the Gulf of St. Lawrencesame with Bering Sea, the parameter changed from 245K to235K.T2=300K. Figure the AMSR2 sea ice concentration image the Bering Sea

Figure 8. Scatter plo

Polarization

Figure 9. Thin ice area extraction result (Cyan: extracted area)

In this study, authors have applied the AMSR2 Algorithm which was Okhotsk also The extracted thin sea ice areas were validated by comparing

Gulf of St. Lawrence

show the scatter plot of AMSR2 19GHz V versus H) of the Gulf of St. Lawrence observed on March 1,

2015. The distribution of the data also suggests the possibility of applying the same algorithm to the Gulf of St. Lawrencesame with Bering Sea, the parameter

m 245K to235K.Figure 9 show the

sea ice concentration image the Bering Sea observed on March 1

Scatter plots of (19GHzV olarization (Gulf of St. Lawrence, Mar

(a)AMSR2 image

(b) MODIS image. Thin ice area extraction result (Cyan: extracted

area) (Gulf of St. Lawrence, Mar

6. CONCLUSION

In this study, authors have applied the AMSR2 which was originally

also to the Bering Sea and the Gulf of St. Laurence. The extracted thin sea ice areas were validated by comparing

the scatter plot of AMSR2 19GHz V versus H) of the Gulf of St. Lawrence observed on March 1,

2015. The distribution of the data also suggests the possibility of applying the same algorithm to the Gulf of St. Lawrencesame with Bering Sea, the parameter T1

m 245K to235K. No change to the pshow the extracted thin ice

sea ice concentration image and MODIS image March 1, 2015.

ts of (19GHzV – 19GHzH)Gulf of St. Lawrence, Mar

(a)AMSR2 image

(b) MODIS image . Thin ice area extraction result (Cyan: extracted

Gulf of St. Lawrence, Mar

CONCLUSION

In this study, authors have applied the AMSR2 originally developed for the Sea of

to the Bering Sea and the Gulf of St. Laurence. The extracted thin sea ice areas were validated by comparing


2015. The distribution of the data also suggests the possibility of applying the same algorithm to the Gulf of St. Lawrence

T1 of equation (1)No change to the parameter

thin ice areas overlaid on and MODIS image

19GHzH) Vs 19GHzV Gulf of St. Lawrence, Mar. 1, 2015)


Gulf of St. Lawrence, Mar. 1, 2015)

In this study, authors have applied the AMSR2 Thin Icedeveloped for the Sea of



2015. The distribution of the data also suggests the possibility of applying the same algorithm to the Gulf of St. Lawrence. Just

(1) was arameter

overlaid on and MODIS image of

Vs 19GHzV 1, 2015)


Thin Ice developed for the Sea of


with have analyzed Bering Sea and ice areas identified in MODIS images were well extracted from AMSR2 data by applyingsome tuning of the parameters algorithm when applying zonesproduce the Ice Algorit

This study was supported by JAXA under the framGCOMtheir kind support.

Comiso, J., 2012, Large Decadal Decline of the Arctic Multiyear Ice Cover, JoJAXA, 2012, Arctic Sea Ice Observation Data Analysis Results, Press Release, http://global.jaxa.jp/press/2012/08/20120825_ arctic_sea_e.html.NSIDC, 2018, Arctic sea ice maximum at second lowest in the satellite recIPCC. 2014. “Summary for Policymakers.” In Climate Change 2013: The Physical Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T.F. Stocet al., Cambridge: Cambridge University Press.Cavalieri, D. J. and P. Gloersen, 1984, Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res., Vol.89, pp.5355Comiso, J. C., 1995 ,SSM/I Sea Ice ConcenBootstrap Algorithm”, NASA Reference Publication 1380, Maryland, NASA Center for AeroSpace Information.Svendsenretrieving total sea ice concentration from a spaceborne dualpolarized pInternational Journal of Remote Sensing, Vol.8, No.10, pp.1479Maykut, G. A., 1978, Energy exchange over young sea ice in the central arctic, JGR, Vol.83, pp.3646Tateyama K., H. Enomoto,, T. Toyice thickness estimated from passive microwave radiometers, Polar Meteorol. Glaciol., National Institute of Polar Research, Vol. 16, pp.15Martinof ice thickness and pderived from AMSRTamura T., K. I. Ohshima, T. Markus, D. J. Cavalieri, S. Nihashi, N. Hirasawa, 2007, Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the AntarctiOcean, Journal of Atmospheric and Oceanic Technology, Vol. 24, pp.1757Cho, K., Y. Mochizuki, Y. Yoshida, 2012, Thin ice area extraction using AMSRProceedings of the 33rd Asian Conference on Remote SensingTS-E6

simultaneously collected MODIS images. have analyzed around 10Bering Sea and the Gulf of St. Laurence. ice areas identified in MODIS images were well extracted from AMSR2 data by applyingsome tuning of the parameters algorithm when applying zones of the Northern Hemisphere. produce the thin ice product

Algorithm as the

ACKNOWLEDGEMENT

This study was supported by JAXA under the framGCOM-W Project. The authors would like to thank JAXA for their kind support.

Comiso, J., 2012, Large Decadal Decline of the Arctic Multiyear Ice Cover, JoJAXA, 2012, Arctic Sea Ice Observation Data Analysis Results, Press Release, http://global.jaxa.jp/press/2012/08/20120825_ arctic_sea_e.html. NSIDC, 2018, Arctic sea ice maximum at second lowest in the satellite record, http://nsidc.org/arcticseaicenews/IPCC. 2014. “Summary for Policymakers.” In Climate Change 2013: The Physical Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T.F. Stocet al., Cambridge: Cambridge University Press.Cavalieri, D. J. and P. Gloersen, 1984, Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res., Vol.89, pp.5355-5369.Comiso, J. C., 1995 ,SSM/I Sea Ice ConcenBootstrap Algorithm”, NASA Reference Publication 1380, Maryland, NASA Center for AeroSpace Information.Svendsen, E., C. Matzler & T. C. Grenfell, 1987, A model for retrieving total sea ice concentration from a spaceborne dualpolarized passive microwave instrument operating near 90 GHz, International Journal of Remote Sensing, Vol.8, No.10, pp.1479-1487 Maykut, G. A., 1978, Energy exchange over young sea ice in the central arctic, JGR, Vol.83, pp.3646Tateyama K., H. Enomoto,, T. Toyice thickness estimated from passive microwave radiometers, Polar Meteorol. Glaciol., National Institute of Polar Research, Vol. 16, pp.15-31. Martin, S. and R. Drucker, 2005, Improvements in the estimates of ice thickness and pderived from AMSRTamura T., K. I. Ohshima, T. Markus, D. J. Cavalieri, S. Nihashi, N. Hirasawa, 2007, Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the AntarctiOcean, Journal of Atmospheric and Oceanic Technology, Vol. 24, pp.1757-1772. Cho, K., Y. Mochizuki, Y. Yoshida, 2012, Thin ice area extraction using AMSRProceedings of the 33rd Asian Conference on Remote Sensing

E6-3, pp.1-6.

simultaneously collected MODIS images. around 10 scenes for the Gulf of St. Laurence.

ice areas identified in MODIS images were well extracted from AMSR2 data by applying the algorithmsome tuning of the parameters mayalgorithm when applying the algorithm

of the Northern Hemisphere. thin ice product from AMSR2 data

the research product of AMSR2.

ACKNOWLEDGEMENT

This study was supported by JAXA under the framW Project. The authors would like to thank JAXA for

REFERENCES

Comiso, J., 2012, Large Decadal Decline of the Arctic Multiyear Ice Cover, Journal of Climate, Vol. 25, pp.1176JAXA, 2012, Arctic Sea Ice Observation Data Analysis Results, Press Release, http://global.jaxa.jp/press/2012/08/20120825_

NSIDC, 2018, Arctic sea ice maximum at second lowest in the

ord, http://nsidc.org/arcticseaicenews/IPCC. 2014. “Summary for Policymakers.” In Climate Change 2013: The Physical Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T.F. Stocet al., Cambridge: Cambridge University Press.Cavalieri, D. J. and P. Gloersen, 1984, Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res.,

5369. Comiso, J. C., 1995 ,SSM/I Sea Ice ConcenBootstrap Algorithm”, NASA Reference Publication 1380, Maryland, NASA Center for AeroSpace Information.

E., C. Matzler & T. C. Grenfell, 1987, A model for retrieving total sea ice concentration from a spaceborne dual

assive microwave instrument operating near 90 GHz, International Journal of Remote Sensing, Vol.8, No.10,

Maykut, G. A., 1978, Energy exchange over young sea ice in the central arctic, JGR, Vol.83, pp.3646Tateyama K., H. Enomoto,, T. Toyice thickness estimated from passive microwave radiometers, Polar Meteorol. Glaciol., National Institute of Polar Research,

S. and R. Drucker, 2005, Improvements in the estimates

of ice thickness and production in the Chukchi Sea polynyas derived from AMSR-E, GRL, Vol. 32, L05505Tamura T., K. I. Ohshima, T. Markus, D. J. Cavalieri, S. Nihashi, N. Hirasawa, 2007, Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the AntarctiOcean, Journal of Atmospheric and Oceanic Technology, Vol.

Cho, K., Y. Mochizuki, Y. Yoshida, 2012, Thin ice area extraction using AMSR-E data in the Sea of Okhotsk, Proceedings of the 33rd Asian Conference on Remote Sensing

simultaneously collected MODIS images. scenes for the Sea of Okhotsk,

the Gulf of St. Laurence. The most of the thin ice areas identified in MODIS images were well extracted from

the algorithm. The result suggests may improve the accuracy

the algorithm to the of the Northern Hemisphere. JAXA has decided to

from AMSR2 data h product of AMSR2.

ACKNOWLEDGEMENT S

This study was supported by JAXA under the framW Project. The authors would like to thank JAXA for

REFERENCES

Comiso, J., 2012, Large Decadal Decline of the Arctic urnal of Climate, Vol. 25, pp.1176

JAXA, 2012, Arctic Sea Ice Observation Data Analysis Results, Press Release, http://global.jaxa.jp/press/2012/08/20120825_

NSIDC, 2018, Arctic sea ice maximum at second lowest in the ord, http://nsidc.org/arcticseaicenews/

IPCC. 2014. “Summary for Policymakers.” In Climate Change 2013: The Physical Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T.F. Stocker, D. Qin, G.K. Plattner, et al., Cambridge: Cambridge University Press. Cavalieri, D. J. and P. Gloersen, 1984, Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res.,

Comiso, J. C., 1995 ,SSM/I Sea Ice Concentrations Using the Bootstrap Algorithm”, NASA Reference Publication 1380, Maryland, NASA Center for AeroSpace Information.

E., C. Matzler & T. C. Grenfell, 1987, A model for retrieving total sea ice concentration from a spaceborne dual


Maykut, G. A., 1978, Energy exchange over young sea ice in the central arctic, JGR, Vol.83, pp.3646-3658 Tateyama K., H. Enomoto,, T. Toyota. and S. Uto, 2002, Sea ice thickness estimated from passive microwave radiometers, Polar Meteorol. Glaciol., National Institute of Polar Research,

S. and R. Drucker, 2005, Improvements in the estimates roduction in the Chukchi Sea polynyas

E, GRL, Vol. 32, L05505 Tamura T., K. I. Ohshima, T. Markus, D. J. Cavalieri, S. Nihashi, N. Hirasawa, 2007, Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the AntarctiOcean, Journal of Atmospheric and Oceanic Technology, Vol.

Cho, K., Y. Mochizuki, Y. Yoshida, 2012, Thin ice area E data in the Sea of Okhotsk,

Proceedings of the 33rd Asian Conference on Remote Sensing

simultaneously collected MODIS images. The authors the Sea of Okhotsk, the

he most of the thin ice areas identified in MODIS images were well extracted from

. The result suggests that accuracy of the

to the other sea ice JAXA has decided to

from AMSR2 data using the Thin h product of AMSR2.

This study was supported by JAXA under the framework of W Project. The authors would like to thank JAXA for

Comiso, J., 2012, Large Decadal Decline of the Arctic urnal of Climate, Vol. 25, pp.1176-1193.


NSIDC, 2018, Arctic sea ice maximum at second lowest in the ord, http://nsidc.org/arcticseaicenews/

IPCC. 2014. “Summary for Policymakers.” In Climate Change 2013: The Physical Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on

ker, D. Qin, G.K. Plattner,

Cavalieri, D. J. and P. Gloersen, 1984, Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res.,

trations Using the Bootstrap Algorithm”, NASA Reference Publication 1380, Maryland, NASA Center for AeroSpace Information.

E., C. Matzler & T. C. Grenfell, 1987, A model for retrieving total sea ice concentration from a spaceborne dual-


Maykut, G. A., 1978, Energy exchange over young sea ice in

ota. and S. Uto, 2002, Sea ice thickness estimated from passive microwave radiometers, Polar Meteorol. Glaciol., National Institute of Polar Research,


Tamura T., K. I. Ohshima, T. Markus, D. J. Cavalieri, S. Nihashi, N. Hirasawa, 2007, Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the Antarctic Ocean, Journal of Atmospheric and Oceanic Technology, Vol.


Proceedings of the 33rd Asian Conference on Remote Sensing

The authors he

he most of the thin ice areas identified in MODIS images were well extracted from

that of the

sea ice JAXA has decided to

the Thin

ework of W Project. The authors would like to thank JAXA for

Comiso, J., 2012, Large Decadal Decline of the Arctic 1193.


NSIDC, 2018, Arctic sea ice maximum at second lowest in the

IPCC. 2014. “Summary for Policymakers.” In Climate Change 2013: The Physical Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on

ker, D. Qin, G.K. Plattner,

Cavalieri, D. J. and P. Gloersen, 1984, Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res.,

trations Using the Bootstrap Algorithm”, NASA Reference Publication 1380,

E., C. Matzler & T. C. Grenfell, 1987, A model for -


Maykut, G. A., 1978, Energy exchange over young sea ice in

ota. and S. Uto, 2002, Sea ice thickness estimated from passive microwave radiometers, Polar Meteorol. Glaciol., National Institute of Polar Research,


Tamura T., K. I. Ohshima, T. Markus, D. J. Cavalieri, S. Nihashi, N. Hirasawa, 2007, Estimation of Thin Ice Thickness

c Ocean, Journal of Atmospheric and Oceanic Technology, Vol.


Proceedings of the 33rd Asian Conference on Remote Sensing，



8

Tokutsu Y., K. Cho, 2014, Thin Ice Area Extraction Algorithm Using AMSR2 Data for the Sea of Okhotsk, Proceedings of the 35th Asian Conference on Remote Sensing，OS-101, pp.1-6. Sato, Y., K. Naoki, K. Cho, A Study on Extracting the Trend of Then Ice Distribution in the Sea of Okhotsk Using ASMR-E and AMSR2 Data, Proceedings of the 36th Asian Conference on Remote Sensing, TUP1.49, pp.1-6. October 2015. Cho K., Y. Mochiduki, Y. Yoshida, M. Nakayama, K. Naoki, C. F. Chen, 2011, Thin ice thickness monitoring with FORMOSAT-2 RSI data, Proceedings of the 32nd Asian Conference on Remote Sensing，TS1-2, pp.1-8 Cho K., Y. Mochizuki, Y. Yoshida, H. Shimoda and C. F. CHEN, 2012, A study on extracting thin sea ice area from space, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXIX-B8, pp.561-566. Comiso, J. C., 2009, Enhanced Sea Ice Concentrations and Ice Extent from AMSR-E Data, Journal of the Remote Sensing Society of Japan, Vol.29, No.1, pp.199-215. Miyao, K., K. Naoki, K. Cho, Upgrading thin ice area extraction algorithm using AMSR2 data, Proceedings of the 39th Asian Conference on Remote Sensing，ABA469, pp.1-10. October 2018. NASA, 2012, https://atrain.nasa.gov/



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thin ice area extraction in the seasonal sea ice zones …

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