6Terrestrial Hydrology : Soil Moisture and Snow‫ٳ‬ឮȁ֚჊ࢩോఱ‫ڠ‬௙ࣣ‫ڠش‬໐઀౻ȁੇဎ൐‫ނ‬ఱ‫ڠ‬ఱ‫شݪࡄࠏڠࢥ֭ڠ‬Ichirow Kaihotsu
Faculty of IntegratedArts and Sciences,Hiroshima University
Toshio Koike
School of Engineering,
The University of Tokyo

6.1 ‫࢓ܨ‬ΏΑΞθ̤̫ͥͅၘ֖କ੏۪͈࿨‫ڰۼ૽͂ڬ‬൲͈͒‫ޣג‬
Terrestrial Hydrology : Its Important Role in the Climate System and its Effects on Human Activitiesၘ֖କ໲ً೾͂ఱ‫ܨ‬ȝၘ֖ȝ٬ဢͬ੏۪̳ͥକ͉౷‫଼̳ͥࠁͬ࢓ܨ͈ݩ‬ਹါ̈́ါள̳́ȃୟୱ͞ാિକ໦̱͂̀ၘ֖ͅಡၣ̯̞ͦ̀ͥ
କ͈‫́ޣג‬Ȃޫਫ਼എ̜̞͉ͥ౷֖എ̈́କ੏۪་൲̦ఈ͈ႀ֖͞‫ܬ‬୯͂௖ࡽ‫۾‬Ⴒ଻ͬ঵̞̳̽̀͘ȃ̢̹͂ޫਫ਼എ̈́କ࿚ఴͬࣉ̢̧ͥ͂͜ͅȂ౷‫ݩ‬କ੏۪ͬ೒̱̀ఈ͈౷֖͈͂Ξτ
΋Υ·ΏοϋȪ‫ैڞ׿‬ဥȫ͈࢘‫̢̫ͦ̈́ࣉͬض‬̵͊̈́ͤͭ͘ȃၘ֖କ໲ً೾̷͈͂঵௽എ̈́κΣΗςϋΈෝႁͬ٨஝̱̀Ȃ౷‫ݩ‬କ੏۪͈ထ௶ෝႁͬ࢜ષ̵̯̭͉ͥ͂Ȃକͅ‫ͥͩ۾‬ब‫૽͞ࡘࠚ͈ٺ‬႒͈঵௽എ‫ٳ‬อ͈͒ਹါ࣓̈́ࡃ̳͂̈́ͤ͘ȃAMSR/AMSR-E͉ഐশ͈Ȃ຦ৗ͈̞ࣞȂಿ‫֖ၘ͈ܢ‬କ໲ૂ༭ͬ೹‫̳̱͘ރ‬ȃHydrologic processes on land, and water
circulation among atmosphere, land, and ocean are
key features of the Earth's climate. Due to variationsof water storage as snow and soil moisture, local
and regional variations in the water cycle correlatewith different areas and seasons. Even when weaddress a more localized water-related event, wemust consider its tele-connectivity with other areas
or regions under the global water cycle variation.
Enhanced prediction of variations in the global
water cycle based on improved understanding ofhydrological processes on land and sustained
monitoring capability, greatly mitigate water-related
damage and contribute to sustainable human଎ġġġ6.1.1 କ੏۪͈‫ٽ‬ැ଎
Fig. 6.1.1 Hydrological circulation

development. AMSR and AMSR-E provide timely,long-term, quality information on land hydrology.

35

6.2 ാિକ໦͂ୟୱ͈࿨‫ڬ‬
Role of Soil Moisture and Snow
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5-2.0-2.5-3.0૊อ८ņŵȪEvapotranspirationȫ

ࣛକPȪPrecipitationȫ
౷ນ࿂ນ࿂ၠ੄ SRȪSoil surfaceȫȪSurface runoffȫआ߲֖ȪRoot zoneȫ౷‫ئ‬କᙐူGȪGroundwater rechargeȫࡐ෎ HȪSensible heatȫஆ෎ LEȪLatent heatȫ౷ນ࿂ȪSoil surfaceȫະգ౷‫ئ‬କȪUnconfined groundwaterȫP = Et + SR + ǍS + G଎ġġġ6.2.1 ౷ນ࿂͈କਓ঑
Fig. 6.2.1 Water balance at the soil surface

౷ಎ෎ SHȪSoil heat storageȫ
Rn = H + LE + SH଎ġġġ6.2.2 ౷ນ࿂͈෎ਓ঑
Fig. 6.2.2 Heat balance at the soil surfaceാિକ໦͉౷ນ࿂͈́କਓ঑Ȫ଎6.2.1ȫͬࢹ଼̳ͥਹါ̈́ါள̜́ͥ͂͂͜ͅȂ෎ਓ঑എ͉ͅ૊อ͂૊८̤͍͢౷ಎ෎ͅਹါ̈́࿨‫̳̞̲̀͘׵ͬڬ‬ȃஆ෎Ȫ૊อ८ȫ͉ാિକ໦Ȫ౷ນ࿂͈କ૊‫ܨ‬ၾȫ͂ఱ‫͈ܨ‬କ૊‫ܨ‬ၾ͈͂΀ΥσΆȜओ‫͍ݞ‬౷ນ໓௸ͅջం̱̳͘ȃ̭͈ஆ෎͈་‫̱̀͂ض͉ࠫا‬෎ਓ঑͈෻໦ͅ‫͈̳̱́͘ޣג‬Ȫ଎6.2.2ȫȂၘ֖କ੏۪Ȃ̾ͤ͘‫࢓ܨ‬ΏΑΞθͅ‫ͬޣג‬ဓ̢̭̳ͥ͂̈́ͤ͘ͅȃാિକ໦͈་൲͉౷֖̥ͣͺΐͺȂ̞͉͌̀౷‫ܰݩ‬࿅͈କ੏۪་൲ͅఱ̧̈́‫ͬޣג‬ဓ̢ͥਹါ̈́ါ֦̳͈́́Ȃാિକ໦ͬশߗ‫ۼ‬എͅ௴̢̭͉ͥ͂ఱ་փ̦̜̳݅ͤ͘ȃୟୱ͉Ȃအș̈́ब‫͞ٺ‬କ঩࡙͈ಡၣ̱͂̀૽‫ڰۼ‬൲ͅఱ̧̈́‫ͬޣג‬ဓ̢ͥ͂͂͜ͅȂ̷͈̞ࣞఊု༶ৣ͈฽ৣၚ͞ဏୱࢃ͈ാિକ໦ᙐူͬ೒̱̀Ȃ‫̷͈͞࢓ܨ‬་൲଻ͅਹါ̈́࿨‫̳̞̲̀͘׵ͬڬ‬ȃ଎6.2.3bȪհ଼ঙ೹‫ރ‬ȫ̦া̳̠͢ͅȂξȜρΏͺఱၘ͈ୟୱ་൲̷͉͈ා͈‫ͼ͈ذ‬ϋΡ͈ࣛକၾ͂໅͈௖‫̳̜̦ͤ͘߸۾۾‬ȃ଎6.2.3aȪ‫ؗ‬ঙ೹‫ރ‬ȫ͉΀σΣȜΣο͂Ηͼ͈‫͈ࣛذ‬କၾ་൲ͅ‫۾̞ޑ‬Ⴒ̦̜̭ͥ͂ͬা̱̞̳̦̀͘Ȃୟୱ͈‫΀͉ޣג‬σΣȜΣο̦ࣛକ་൲ͅဓ̢ͥ‫͂ޣג‬൳အ͈ഽࣣ̞ͬা̱̞̳̀͘ȃ

36

1950

1955

1960

1965

1970

1975

1980

1985

1990

ୃྙ༶ৣ RnȪNet radiationȫ(b) Negative Correlation between the Monsoon Activities and the Eurasian Snow Cover40

Snow Cover

30Anomaly [%]ാિକ໦ಡၣϚSȪSoil moisture storageȫ

Rain : May - July
SOI : 11

Normalized Index૊८૊อȪTranspirationȫ
ȪEvaporationȫ

(a) Interannual Variation of Rainfall in Thailamd and SOI

Indian Summer Rainfall (June to September)

20
100-10-20-30

1965

1970

1975

1980

1985

1990

଎ȁ6.2.3 ȪaȫΗͼ͈‫ࣛܬذ‬କၾ་൲͂΀σΣȜΣο͈‫߸۾‬ȂȪbȫξȜρΏͺఱၘ͈ୟୱ་൲͂ͼϋΡ͈‫ࣛܬذ‬କၾ͈‫߸۾‬
Fig. 6.2.3 (a) Effect of El Niño on the summer rainfall in Thailand and (b) negative correlation between snow
cover area in the Eurasian continent and the Indian summer monsoon rainfall

Soil moisture is one element of water balance (Fig. 6.2.1) and plays an important role inevapotranspiration. Latent heat depends on the energy balance between soil moisture andatmospheric water vapor content as well as surface wind speed. As change of latent heat yields tothe change in partition of the energy balance (Fig. 6.2.2), it influences the hydrological cycle andclimatic system. We must, therefore, observe soil moisture in time and space.
Snow plays an important role in climate and its variability through its high reflectance and soil
moisture supply after snowmelt. It also affects human activities through snow disasters and as afunction of water resources storage. Yasunari (personal communication) suggests a negative
correlation between the snow cover area in the Eurasian continent and the Indian summer monsoon
rainfall, as depicted in Fig. 6.2.3b. It is comparable to El Niño in its effect on the summer rainfall inThailand (Fig. 6.2.3a), as pointed out by Oki (personal communication).

6.3 ാિକ໦͈౷ષ۷௶͂‫ିמ‬۷௶͈಺გͬ࿒ঐ̱̀
Coordination of In-Situ and Satellite Observations of Soil Moisture౷ນ࿂ȪSoil surfaceȫΟȜΗυ΄ȪData loggerȫฒ߄೷ࢯ‫أ‬ഽࠗȪSoil temperature sensorȫAerovane

Infraredthermometer

Thermometer,
HygrometerThree cup windanemometer

Data logger,Barometer

TDR probes,
ST sensors
in the soil

TDR probesΨΛΞςȜȪBatteryȫ

ST sensorsാિȪSoilȫ

Raingauge

TDRίυȜήȪTDR probeȫ଎ġġġ6.3.1 TDR༹ͥ͢ͅാિକ໦κΣΗςϋΈΏΑΞθ
Fig. 6.3.1 Monitoring system of soil moisture using the TDR method

Net radiometer

Data logger, Battery

AWS in Bayantsagaan

ASSH଎ġġġ6.3.2 AWSȆASSHͥ͢ͅκϋΌσ͈́ാિକ໦۷௶Fig. 6.3.2 Soil moisture observations by AWS and ASSH౷ષാિକ໦͈۷௶༹࿤‫ٸ‬ാિକ໦ၾ௶೰̱͂̀डഐ̈́TDRȪশ‫ۼ‬ႀ֖฽ৣၚȫ༹͉ȂTDRίυȜή͈υΛΡ̥ͣാિಎͅอ̱̹ഩঽ෨͈௢࣐শ‫ͤ͢ͅۼ‬ാિକ໦ၾͬ௶೰̱̳͘ȃ଎6.3.1͉໹࣐2஌ഩ
ޭ߿TDRίυȜῄ࡛ͥ͢ા௶೰͈‫ٽ‬ැ଎̳́ȃ౷ಎ͈ਫ਼೰͈૬ഽͅକ໹ͅίυȜήͬொව̱̀ಿ‫͈ܢ‬κΣΗςϋΈ࣐̞̳ͬ͘ȃAWSȪু൲‫ܨ‬યΑΞȜΏοϋȫ͂ASSHȪু൲ാિକ໲ΑΞȜΏοϋȫͅ଎6.3.1͈ാિକ໦κΣΗςϋΈΏΑΞθͬழ͙ࣺͭ́Ȃ‫ܖ‬யഎ̈́‫ܨ‬યȆକ໲ါளͬ௶೰̱̦̈́ͣȂാિକ໦͈ಿ‫ܢ‬κΣΗςϋΈ
Ȫ૬ഽ3cm̥ͣ100cmȇે‫ͅޙ‬؊̲̀ȫͬκϋΌσদࡑ౷́৘ঔ̱̞̳̀͘ȃ଎6.3.2͉৘ष͈AWS͂ASSHͬা̱̞̳̀͘ȃͺͼ΂χ͈́ζͼ·υ෨༶ৣ͈ࠗ౷ષ۷௶
2002ා6ȡ7࠮ͅༀ࣭ͺͼ΂χਗ̀ͅȂༀ࣭࣎ߗֳಐޫȪNASAȫȂ෠ྩજȪUSDAȫ̽̀͢ͅ৘ঔ̯̹ͦാિକ໦࠿બ৘ࡑȪSMEX02ȫͅ౷ષ୭౾߿ζͼ·υ෨༶ৣ̦ࠗ൵ව̯ͦȂျ౷Ȃ̠̭̱͂ͧ͜ศȂఱ൨ศ͈۷௶Ȫ଎6.3.3ȫ࣐̞ͬȂ૒୆͈࢘‫̺ͭ܄ͬض‬༶ৣഥో༷೾৆̦೰৆‫̹̱̯ͦ͘ا‬ȃ̷͈ࠫ‫ͬض‬਋̫̀Ȃ2ਔ෨Ȃ2༊෨ͬঀ̹̽ാિକ໦Ȃ૒୆କ໦ॳ೰ͺσΌςΒθ̦‫ٳ‬อ̯̱̹ͦ͘ȃ଎ġġġ6.3.3 ζͼ·υ෨༶ৣ͈ࠗ౷ષ۷௶Ȫͺͼ΂χȫFig. 6.3.3 Ground-based microwave radiometer experiments in Iowa

Soil moisture observation method
Time domain reflectometry (TDR) is well known as the best method to measure soil water content
in the field. Fig. 6.3.1 illustrates a monitoring system for soil moisture measurement by TDR sensors
with two parallel wires. In practice, several TDR probes are inserted horizontally into the vertical soil
profile of a trench in the field. Ground-based long-term monitoring by Automatic Weather Stations(AWSs) and Automatic Stations for Soil Hydrology (ASSHs) including the TDR soil moisturemeasurement system is continuing successfully in Mongolia. Fig. 6.3.2 presents the conditions ofAWS and ASSH in the study area.
Ground-based microwave radiometer experiments in Iowa
From June to July 2002, University of Tokyo (UT) and JAXA introduced a ground-based microwave
radiometer in the intensive validation experiment for soil moisture in Iowa, the SMEX02 organized byNASA and USDA (Fig. 6.3.3). UT and JAXA measured bare soil and land surfaces covered with cornand soybeans at different stages of growth. Using the resulting data, we modeled the effects ofvegetation and developed a dual-frequency and dual-polarization algorithm for soil moisture andvegetation water content.

37

Mongolia

AWS

Russia

ASSH

China

Bayantsagaan Site

20

Areal soil moisture120

Areal soil moisture change[%]
25

Ulaanbaatar

90

15

10

560

in the study area in June, 2004

3060

90

120

2004 / 03 / 06

2003 / 12 / 06

2003 / 09 / 06

2003 / 06 / 06

2003 / 03 / 06

2002 / 12 / 06

2002 / 09 / 06

2002 / 06 / 06

2002 / 03 / 06

ASSH (Automatic Station for Soil Hydrology)

2001 / 12 / 06

Location of AWS (Automatic Weather Station),

2001 / 09 / 06

2001 / 06 / 06

030

Year / Month / Day

160 [km]

Mandalgobi Site଎ġġġ6.3.4 ADEOS-㸈ȆAquaκϋΌσদࡑ౷
Fig. 6.3.4 Study area for ADEOS-㸈 and Aqua validation experimentκϋΌσ౷ષদࡑ౷
ADEOS-㸈͂Aqua͈࠿બΟȜΗͬං̹ͥ͛ͅȂκϋΌσ࣭κϋΌσࣞࡔ͈ζϋΘσΌΫ
ঌ͂Ιοͼσঌ͈‫ͅۼ‬൐ୌ160kmȂධཤ120km͈౷ષ۷௶ဥ͈দࡑ౷ͬ୭̫̱̹͘
Ȫ଎6.3.4ȫȃদࡑ౷͈ධ௰͉͊ͣ̈́͘௜౷ȪͼΥ‫͈ش‬ཪ௜̦৽ఘȫ̜́ͤȂཤ̥ͣ൐̥̫̀ͅᚇ࿐࢐̲
͈ͤࣞྟഽ͈௜౷̦ജ‫̳̞̱̀͘ٳ‬ȃ̭͈দࡑ౷ͅ5ర͈AWS͂12ర͈ASSHͬ୭౾̱Ȃໝତ͈ࣞഽ͞૬ഽ̤̫ͥͅ‫ܨུܖ‬યါள͂ാિକ໦͈κΣΗςϋΈͬȂAWS̞͉̾̀ͅ
2000ාȂASSH̞͉̾̀ͅ2001ා̥࣐̞̳ͣ̽̀͘ȃ଎ġġġ6.3.5 দࡑ౷͈඾໹޳࿂ୟକ໦ၾ͈་‫ا‬Fig. 6.3.5 Time change of the daily mean areal soil moisture

Study area for the ADEOS-Τ / Aqua validation experiment
To obtain ground-based data for ADEOS-Τ and Aqua algorithm validation of soil moisturemeasurement and physical parameters, a study area of 160km by 120km was established in theMongolian plateau between Mandalgobi and Choyr (Fig. 6.3.4). The soil surface in the southern partis mostly covered with sparse pasture and that in the northern and eastern part is covered with dense
pasture with sporadic shrubs. Five AWSs and twelve ASSHs were installed in this study area.
Monitoring fundamental elements of meteorology and hydrology was initiated using AWS from 2000
and ASSH from 2001.

দࡑ౷͈࿂ୟକ໦ၾ2001ා6࠮̥ͣ2004ා5࠮͈́͘૬ഽ3cm͈඾໹޳࿂ୟକ໦ၾȪκϋΌσদࡑ౷͈஠௶೰താિକ໦ၾ͈ॳ੅໹޳౵ȫ་‫ͬا‬଎6.3.5ͅা̱̳͘ȃ଎ಎ͈඾໹޳࿂ୟକ໦ၾ̦1.8%̥
ͣ19.6ɓ͈ํս́Ⴒ௽ͅ་‫̱ا‬ȂAMSR͞AMSR-E͈ാિକ໦ͺσΌςΒθ͈೩Ȇಎാિକ໦֖͈࠿બဥΟȜΗ̱͂̀ခဥ̜̭̦́ͥ͂໦̥̳ͤ͘ȃ̹͘Ȃ‫̈́ࠣݢ͈ذ‬କ໦ષઌַ͉͈̳ࣛͥ́͢͜ͅȃ10࠮̥ͣ2࠮̥̫͉̀ͅാિ̦ൄാ‫̱ا‬Ȃࡉ̥̫ષȂఱ་‫̹̞ۋ‬ാિକ໦ેఠ̞̳͂̈́̽̀͘ȃ

38Areal soil moistureFig. 6.3.5 presents the time change of the areal soil moisture calculated arithmetically using all theASSH data in the experimental field from June 2001 to May 2004. The wide range of differences(1.8% to 19.6%) in the areal soil moisture implies it will be useful in validating the satellite soil
moisture algorithm as shown in the figure. Many spikes can be seen in spring and summer due torainfall. From October to February, the soil is apparently drier because of freezing conditions.

6.4 AMSR-E ̦௴̢̹κϋΌσ͂ΙαΛΠࣞࡔ̤̫ͥͅാિକ໦໦ືSoil Moisture Distribution in Mongolia and the Tibetan Plateau Derived from AMSR-E
53N
52N
51N
50N
49N
48N
47N
46N
45N
44N
43N
42N
41N
40N

40N
39N
38N
37N
87E

90E

93E

96E

99E

102E 105E

108E 111E 114E

117E 120E
36N

53N
52N
51N
50N
49N
48N
47N
46N
45N
44N
43N
42N
41N
40N
53N
52N
51N
50N
49N
48N
47N
46N
45N
44N
43N
42N
41N
40N

35N
34N
33N
32N
31N
87E

90E

93E

96E

99E

102E 105E

108E 111E 114E

Ϊ30N

117E 120E

ζ

29N
28N

ρμ५ྤ
ȁHimalaya Mountains

27N
26N
25N
75E
87E

90E

93E

96E

99E

102E 105E

108E 111E 114E

78E

81E

84E

87E

90E

93E

96E

99E

102E

105E

117E 120E[%]

଎ġġġ6.4.1 AMSR-Eͥ͢ͅκϋΌσ͈‫ܬذ‬ാિକ໦໦ື଎Ȫષ଎ȇ7࠮Ȃಎ଎ȇ8࠮Ȃ‫ئ‬଎ȇ9࠮ȫ
Fig. 6.4.1 Soil moisture distribution in Mongolia by AMSR-E (top, July; middle, August; bottom, September)2ਔ෨Ȃ2༊෨ാિକ໦ͺσΌςΒθͬAMSR-EΟȜῌഐဥ̱̀ॳ೰̯̹ͦκϋΌσ͈‫ذ‬Ȫ2002ා7Ȃ8Ȃ9࠮͈ड੝͈10඾໹޳ȫ͈ാિକ໦໦ືͬ଎6.4.1ͅা̱̞̳̀͘ȃୌ໐͈‫ۋ‬௓‫ڐ͈ا‬ఱ̦௴̢̞̳ͣͦ̀͘ȃ଎6.4.2͉Ȃ2ਔ෨Ȃ2༊෨ാિକ໦ͺσΌςΒθͬAMSR-EΟȜῌഐဥ̱̀ॳ೰̯̹ͦ
ΙαΛΠࣞࡔષ͈2003ා9࠮‫ئ‬੐͈10඾໹޳͈ാિକ໦໦ື଎̳́ȃཤୌ໐͈‫ۋ‬௓֖͂ධ൐໐͈৔੕֖͈΋ϋΠρΑΠ̦අಭഎ̳́ȃ̹͘ȂΪζρμධ০࿂ͅ৔੕֖̦Ȃཤ০࿂ͅ‫ۋ‬௓֖̦చ̱ͬ̈́̀ఝેͅႲ̞̳̈́̽̀͘ȃ

051015202530

3540455055 [%]଎ġġġ6.4.2 AMSR-Eͥ͢ͅΙαΛΠࣞࡔ͈ാિକ໦໦ື଎
Fig. 6.4.2 Soil moisture distribution in the Tibetan Plateau by AMSR-E

UT and JAXA derived the first ten-day soil moisture average in July (top panel, Fig. 6.4.1), August(middle) and September (bottom) in Mongolia based on the AMSR-E data. They observed anexpansion of the dry area in the west.
By applying the dual-frequency, dual-polarization algorithm to the AMSR-E data, UT and JAXA
produced a ten-day average soil moisture distribution map (Fig. 6.4.2) of the Tibetan Plateau for theend of September 2003. They identified a very clear dry-wet contrast between the northwest andsoutheast parts of the Tibetan Plateau. There are wet bands on the south and dry bands on the northslopes of the Himalayas.

396.5 ୟୱ͈౷ષ۷௶͂‫ିמ‬۷௶͈͂಺გͬ࿒ঐ̱̀
Coordination of In-Situ and Satellite Observations of Snow

Molot
Kenkeme

Pine

Larch

Tulagino
Larch (Larch Forest)

Viluy

Molot (Mixed Forest)

Khatassy଎ġġġ6.5.2 υΛ΅Ȝ५ྤ͈ζͼ·υ෨༶ৣࠗୟୱ۷௶
Fig. 6.5.2 Ground-based microwave radiometer observation in the Rocky Mountains

10km
10km

Pine (Pine Forest)଎ġġġ6.5.1 Ώαςͺ͈দࡑ΍ͼΠ
Fig. 6.5.1 Observation site in Siberia
Viluy (Pine Forest)‫ିמ‬ζͼ·υ෨ςκȜΠΓϋΏϋΈͥ͢ͅୟୱ۷௶਀༹͈‫ٳ‬อ͈̹͉͛ͅȂୟୱಎ͈༶ৣഥోඅ଻͈ࣉၪ͂Ȃအș̈́ാ౷ૄ࠯‫࠿͈́ئ‬બ̦ຈါ̳́ȃ‫ێ‬႖౷ً೾ίυΐͿ·ΠȪCLPXȫͅAMSR/AMSR-E͂൳̲ਔ෨ତͬ̾͜౷ષ୭౾߿ζͼ·υ෨༶ৣ̦ࠗ൵ව̯ͦȪ଎6.5.2ȫȂୟୱಎ͈ζͼ·υ෨༶ৣഥోً೾͈ၑٜ͈࢜ષ͂κΟσ‫ٳ‬อ̦ૺ̱̹͛ͣͦ͘ȃΏαςͺ͉ͅȂ૒୆ેఠ̦։̈́ͥ˓΍ͼΠͅୟୱ૬̯̦ࠗ2001ාͅ୭౾̯ͦȪ଎6.5.1ȫȂAMSR/AMSR-EୟୱίυΘ·Μ͈٨ၻ͂࠿બ͈̹͈͛ܲਹ̈́۷௶ΟȜΗͬ೹‫̳̞̱̀͘ރ‬ȃ
Microwave remote sensing of snow from space should be developed based on radiativetransfer characteristics of the snow pack, and validated under various land cover conditions. A
ground-based microwave radiometer (Fig. 6.5.2) having observational frequencies consistent
with AMSR/AMSR-E was introduced in the Cold Land Processes Experiment (CLPX)
conducted in the Rocky Mountains in 2003 to facilitate understanding and modeling microwave
radiative transfer processes in snowpack. In 2001, seven snow depth sensors were installed in
Siberia under different vegetation covers (Fig. 6.5.1) and are providing valuable data sets forKenkeme (Forest & Open Field)

40Khatassy (Open Field)

Tulagino (Open Field)

improving and validating AMSR and AMSR-E snow products.

6.6 AMSR-E ̦௴̢̹Ώαςͺ͈ୟୱ໦ືSnow Distribution in Siberia Derived from AMSR-E
74N
72N
70N
68N
66N
64N
62N
60N
58N
56N
54N
52N
50N
30E

40E

50E

60E

70E

80E

90E

100E

110E

120E

130E

140E

150E

160E

170E

74N
72N
70N
68N
66N
64N
62N
60N
58N
56N
54N
52N
50N
30E

40E

50E

60E

70E

80E

90E

100E

110E

120E

130E

140E

150E

160E

170E

74N
72N
70N
68N
66N
64N
62N
60N
58N
56N
54N
52N50N
30E

2003
180E

2004

‫ͥ͢ͅିמ‬ୟୱ۷௶ͺσΌςΒθͬࢩ̩ଲ‫ێ͈ٮ‬႖౷֖ͅഐဥ̳̹͉ͥ͛ͅȂͺσΌςΒθͅழ͙ࣺ̞ͦ̀ͥ͘‫ུͥ̈́͂ܖ‬༶ৣഥోκΟσ͈ഐဥ଻̦Ȃୟୱ૬Ȃୟୱ໤ၑඅ଻Ȃୟୱ‫͈ئ‬ാિૄ࠯͈ࢩ̞τϋΐͅმ̽̀‫ږ‬༗̵̯̞̫ͦ̀̈́ͦ͊̈́ͤͭ͘ȃ̹͘ȂͺσΌςΒθ͉‫͍̤͢ିמ‬ఈ͈ΟȜΗΕȜΑ̥ͣ၌ဥ‫خ‬ෝ̈́ΟȜΗͬडఱࡠͅ၌ဥ̳ͥࢹ௮̞̭͂̈́̽̀ͥ͂͜ਹါ̳́ȃ̷̭́Ȃ4ΑΠςȜθέ͹ȜΑΠκΟσ͂ᠯྟ̈́໤ৗඤ͈́༶ৣഥోκΟσͬဥ̞̹ͺσΌςΒθ̦‫ٳ‬อ̯̱̹ͦ͘ȃ̭͈ͺσΌςΒθͬဥ̞̩̹̀̾ͣͦΏαςͺ֖͈ୟୱ໦ື଎Ȫ଎6.6.1ȫ̤̞̀ͅȂΏαςͺ̤̫ͥͅ൐ୌ͈ୟୱ໦ື̦ාș་൲̱̞̭̦̀ͥ͂া̯̞̳ͦ̀͘ȃ180E

To apply an algorithm for snow to cold regions
worldwide, we must improve the applicability of thebasic radiative transfer embedded in the algorithm towide ranges of snow depth, physical characteristics,
and soil conditions below the snow. The algorithm

2005
40E

050E60E

1070E

2080E

3090E

40100E

110E

50120E

60130E

70140E

80150E

160E

90170E

100 [cm]

180E

must also maximize use of available data fromsatellites and other data sources. UT and JAXA
developed an algorithm by introducing the fourstream fast radiative transfer model and the dense
media radiative transfer model using the four଎ġġġ6.6.1 AMSR-Eͥ͢ͅΏαςͺ֖͈1࠮͈࠮໹޳ୟୱ໦ື଎
Fig. 6.6.1 Monthly distribution of snow of Siberia during January

frequency channels of AMSR and AMSR-E. The
derived products present the interannual variation ofsnow distribution in Siberia from 2003 to 2005 (Fig.
6.6.1).

41