J. S. PariharJ. S. Parihar

Agricultural Resources GroupAgricultural Resources Group

Space Applications Centre (ISRO)Space Applications Centre (ISRO)

Ahmedabad, IndiaAhmedabad, IndiaE-mail: jsparihar@sac.isro.gov.inE-mail: jsparihar@sac.isro.gov.in

Land applications from Land applications from Megha Tropiques dataMegha Tropiques data

3rd ISRO-CNES Workshop on Megha-Tropiques, Ahmedabad, October 17-20, 2005

NET PRIMARY PRODUCTION: 2.18 Pg C

NET PRIMARY PRODUCTION: 2.18 Pg C

• Water and energy fluxes of land surface coupled Water and energy fluxes of land surface coupled with atmospheric parameters with atmospheric parameters have direct impact have direct impact on Earth’s productivity and food security. on Earth’s productivity and food security.

• Understanding and modeling of land surface Understanding and modeling of land surface processes help in improved use of natural processes help in improved use of natural resources, mitigation of environmental hazards, resources, mitigation of environmental hazards, and knowledge of climate change scenariosand knowledge of climate change scenarios..

• Global observation from various space missions Global observation from various space missions operating in different EM regions are important operating in different EM regions are important source of data to retrieve many critical land source of data to retrieve many critical land surface parameters.surface parameters.

• Megha Tropiques (MT) is unique satellite, which Megha Tropiques (MT) is unique satellite, which will have sensors operating in optical, thermal and will have sensors operating in optical, thermal and microwave regions for characterizing land-ocean-microwave regions for characterizing land-ocean-atmospheric parameters and earth radiation atmospheric parameters and earth radiation budgetbudget. .

BackgroundBackground

Soil Vegetation Atmosphere Interaction

Radiation components: Net radiation Rn(short, long wave) & Albedo ; Temperature, SM are very important

Parameters Needed in Process Modeling

(Retrievable from satellite)

•Soil Moisture • Radiation

•Albedo •Rainfall

•Surface Temperature

SMi = SMi-1 + P - RO – AET- DP

MT SENSORS AND THEIR UNIQUENESS

ScaRaBMADRAS

INDIA AS VIEWED FROM DMSP-SSM/I AND NOAA-AVHRR RADIOMETER

(JUNE, 1999)

FCC

RED: 19 GHz MPDI

GREEN: NDVI

BLUE: 85 GHz MPDI

C h a n n e l F r e q u e n c y ( G H z )

P o l a r i s a t i o n P i x e l s i z e ( k m )

M 1 1 8 . 7 H + V 5 0 M 2 2 3 . 8 V 4 0 M 3 3 6 . 5 H + V 2 5 M 4 8 9 H + V 1 0 M 5 1 5 7 H + V 6

MADRAS

ScaRaB

SensorSensor ParameterParameter

MADRASMADRAS

Soil moistureSoil moisture

Vegetation phenologyVegetation phenology

TemperatureTemperature

ScaRaBScaRaB

Albedo (planetary)Albedo (planetary)

Net radiationNet radiation

TemperatureTemperature

Channel Wavelength (m)

Visible 0.55-0.65 Solar 0.20-4.00 Total 0.20-100 IR Window 10.5-12.5

• Experience withExperience with

(a) Passive Microwave Data: Soil moisture, vegetation (a) Passive Microwave Data: Soil moisture, vegetation phenology, temperature phenology, temperature

(b) Energy Radiation Budget Experiment Data: Albedo, (b) Energy Radiation Budget Experiment Data: Albedo, fluxes- Net Radiation fluxes- Net Radiation

NDVI

MPDI 19 GHzMPDI 37 GHz

MPDI 85 GHz

Microwave Polarization Difference Index (MPDI)Microwave Polarization Difference Index (MPDI)

It is a difference of vertical and horizontal polarized brightness temperature for a specified microwave frequency and expressed as

MPDI = (Tbv – Tbh)/ (Tbv + Tbh)

Where v & h stands for vertical and horizontal component of brightness temperature Tb.

Since vegetation depolarizes the radiation emitted from the soil, the increase in the vegetation fraction decreases the MPDI

June 1-10, 1999

1000

1005

1010

1015

1020

1025

1030

DAY OF THE YEAR

SCAL

ED MP

DI

A37w ht2

A85w ht2

A19w ht2•Multi frequency (19, 37 and 85 GHz) observation of MPDI of wheat crop

MPDI for vegetation growth assessmentMPDI for vegetation growth assessment

Rice (Punjab)

0

2

4

6

8

150 180 210 240 270 300

Julian DaysM

PD

I

0

0.2

0.4

0.6

0.8

ND

VI

NDVI

MPDI

37 GHz

(1999)

•Inverse relationship between NDVI and MPDI observed for rice crop in Punjab for year 1999.

Rice crop, Punjab

Wheat crop

Amritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)

Amritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)(Dominated by

irrigated area)

AmritsarJodhpur

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

soil

mois

ture

(m

3 /m3 )

0102030405060708090100

Rain

fall(

mm

)(Dominated by un-irrigated area)

Jodhpur

Surface Wetness(wi) = 0 [TB (2) - TB ( 1)] + 1 [TB ( 3) - TB( 2)] 1, 2 and 3 are 19, 37 and 85 GHz FrequencySoil Moisture (m) as a a function of wetness index (wi) is m = mad + [(mfc – mad)/(wimax - wimin)] * (wi-wimin)mad = Air dry moisture level of soilmfc = Field capacity of soilwimax = Maximum wetness indexwimin = Minimum wetness index

ILLINOIS (BONDVILLE STATION 40.050N, 88.220W)

0

10

20

30

40

50

60

90 140 190 240 290

JULIAN DAY

SO

IL M

OIS

TU

RE

(%

)

SM_1988(SSMI)

SM_1988(Observed)

June 18, 1998 Nov 19, 1998

Validation at Illinois site

Comparison with rainfall pattern

Water FloodingSoil Moisture0.01 0.48Water FloodingSoil Moisture0.01 0.48

Rainfall Soil MoistureRainfall

Soil Moisture

Relationship of emissivity over spectrum of microwave frequencies for different fraction (f) of surface water

content (Source: Basist et al. 1998)

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 90

Frequency (GHz)

Ver

tical

Pol

ariz

atio

nE

mis

ivity

f=0.0

f=0.2

f=0.4

f=0.6

f=0.8

f=1.0

Relationship of emissivity over spectrum of microwave frequencies for different fraction (f) of surface water

content (Source: Basist et al. 1998)

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 90

Frequency (GHz)

Ver

tical

Pol

ariz

atio

nE

mis

ivity

f=0.0

f=0.2

f=0.4

f=0.6

f=0.8

f=1.0

Amritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)

Amritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)(Dominated by

irrigated area)

AmritsarAmritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)

Amritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)(Dominated by

irrigated area)

AmritsarJodhpur

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

soil

mois

ture

(m

3 /m3 )

0102030405060708090100

Rain

fall(

mm

)(Dominated by un-irrigated area)

JodhpurJodhpur

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

soil

mois

ture

(m

3 /m3 )

0102030405060708090100

Rain

fall(

mm

)(Dominated by un-irrigated area)

Jodhpur

Surface Wetness(wi) = 0 [TB (2) - TB ( 1)] + 1 [TB ( 3) - TB( 2)] 1, 2 and 3 are 19, 37 and 85 GHz FrequencySoil Moisture (m) as a a function of wetness index (wi) is m = mad + [(mfc – mad)/(wimax - wimin)] * (wi-wimin)mad = Air dry moisture level of soilmfc = Field capacity of soilwimax = Maximum wetness indexwimin = Minimum wetness index

ILLINOIS (BONDVILLE STATION 40.050N, 88.220W)

0

10

20

30

40

50

60

90 140 190 240 290

JULIAN DAY

SO

IL M

OIS

TU

RE

(%

)

SM_1988(SSMI)

SM_1988(Observed)

June 18, 1998 Nov 19, 1998

Validation at Illinois site

Comparison with rainfall pattern

Water FloodingSoil Moisture0.01 0.48Water FloodingSoil Moisture0.01 0.48

Rainfall Soil MoistureRainfall Soil MoistureRainfall

Soil MoistureRainfall Soil Moisture

Relationship of emissivity over spectrum of microwave frequencies for different fraction (f) of surface water

content (Source: Basist et al. 1998)

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 90

Frequency (GHz)

Ver

tical

Pol

ariz

atio

nE

mis

ivity

f=0.0

f=0.2

f=0.4

f=0.6

f=0.8

f=1.0

Relationship of emissivity over spectrum of microwave frequencies for different fraction (f) of surface water

content (Source: Basist et al. 1998)

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 90

Frequency (GHz)

Ver

tical

Pol

ariz

atio

nE

mis

ivity

f=0.0

f=0.2

f=0.4

f=0.6

f=0.8

f=1.0

SOIL MOISTURE ESTIMATION USING PASSIVE MICROWAVE SSM/I DATA

Relationship between Wetness Index and Surface Emissivity

Volumetric Soil Moisture From WI

Validation Exercise

Comparison with Rainfall pattern: Potential for early season monitoring is observed

Amritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)

Amritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)(Dominated by

irrigated area)

AmritsarJodhpur

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

soil

mois

ture

(m

3 /m3 )

0102030405060708090100

Rain

fall(

mm

)(Dominated by un-irrigated area)

Jodhpur

Surface Wetness(wi) = 0 [TB (2) - TB ( 1)] + 1 [TB ( 3) - TB( 2)] 1, 2 and 3 are 19, 37 and 85 GHz FrequencySoil Moisture (m) as a a function of wetness index (wi) is m = mad + [(mfc – mad)/(wimax - wimin)] * (wi-wimin)mad = Air dry moisture level of soilmfc = Field capacity of soilwimax = Maximum wetness indexwimin = Minimum wetness index

ILLINOIS (BONDVILLE STATION 40.050N, 88.220W)

0

10

20

30

40

50

60

90 140 190 240 290

JULIAN DAY

SO

IL M

OIS

TU

RE

(%

)

SM_1988(SSMI)

SM_1988(Observed)

June 18, 1998 Nov 19, 1998

Validation at Illinois site

Comparison with rainfall pattern

Water FloodingSoil Moisture0.01 0.48Water FloodingSoil Moisture0.01 0.48

Rainfall Soil MoistureRainfall

Soil Moisture

Relationship of emissivity over spectrum of microwave frequencies for different fraction (f) of surface water

content (Source: Basist et al. 1998)

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 90

Frequency (GHz)

Ver

tical

Pol

ariz

atio

nE

mis

ivity

f=0.0

f=0.2

f=0.4

f=0.6

f=0.8

f=1.0

Relationship of emissivity over spectrum of microwave frequencies for different fraction (f) of surface water

content (Source: Basist et al. 1998)

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 90

Frequency (GHz)

Ver

tical

Pol

ariz

atio

nE

mis

ivity

f=0.0

f=0.2

f=0.4

f=0.6

f=0.8

f=1.0

Amritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)

Amritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)(Dominated by

irrigated area)

AmritsarAmritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)

Amritsar

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

Soi

l Mois

ture

(m

3 m-3

)

010

203040

506070

8090

Rai

nfal

l (m

m)(Dominated by

irrigated area)

AmritsarJodhpur

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

soil

mois

ture

(m

3 /m3 )

0102030405060708090100

Rain

fall(

mm

)(Dominated by un-irrigated area)

JodhpurJodhpur

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350

Julian Day

soil

mois

ture

(m

3 /m3 )

0102030405060708090100

Rain

fall(

mm

)(Dominated by un-irrigated area)

Jodhpur

Surface Wetness(wi) = 0 [TB (2) - TB ( 1)] + 1 [TB ( 3) - TB( 2)] 1, 2 and 3 are 19, 37 and 85 GHz FrequencySoil Moisture (m) as a a function of wetness index (wi) is m = mad + [(mfc – mad)/(wimax - wimin)] * (wi-wimin)mad = Air dry moisture level of soilmfc = Field capacity of soilwimax = Maximum wetness indexwimin = Minimum wetness index

ILLINOIS (BONDVILLE STATION 40.050N, 88.220W)

0

10

20

30

40

50

60

90 140 190 240 290

JULIAN DAY

SO

IL M

OIS

TU

RE

(%

)

SM_1988(SSMI)

SM_1988(Observed)

June 18, 1998 Nov 19, 1998

Validation at Illinois site

Comparison with rainfall pattern

Water FloodingSoil Moisture0.01 0.48Water FloodingSoil Moisture0.01 0.48

Rainfall Soil MoistureRainfall Soil MoistureRainfall

Soil MoistureRainfall Soil Moisture

Relationship of emissivity over spectrum of microwave frequencies for different fraction (f) of surface water

content (Source: Basist et al. 1998)

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 90

Frequency (GHz)

Ver

tical

Pol

ariz

atio

nE

mis

ivity

f=0.0

f=0.2

f=0.4

f=0.6

f=0.8

f=1.0

Relationship of emissivity over spectrum of microwave frequencies for different fraction (f) of surface water

content (Source: Basist et al. 1998)

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 90

Frequency (GHz)

Ver

tical

Pol

ariz

atio

nE

mis

ivity

f=0.0

f=0.2

f=0.4

f=0.6

f=0.8

f=1.0

SOIL MOISTURE ESTIMATION USING PASSIVE MICROWAVE SSM/I DATA

Relationship between Wetness Index and Surface Emissivity

Volumetric Soil Moisture From WI

Validation Exercise

Comparison with Rainfall pattern: Potential for early season monitoring is observed

Amritsar Jodhpur

(Basist, 1998)

MULTI-FREQUENCY APPROACH

SURFACE WETNESS AS OBSERVED FROM SSM/I DATA

2001 2002 May 14-20

May 21-27

Surface wetness indexSource: NCDC-NOAA

May 28-June 03

June 04-June 10

2001 2002

Surface wetness index

June 11-June 17

June 18-June 24

2001 2002

Surface wetness index

June 25-July 01

July 02 – July 08

2001 2002

Surface wetness index

July 09 – July 15

July 16 – July 22

2001 2002

Surface wetness index

0.01 0.48Water BodySurface flooded with waterSoil Moisture

WEEKLY SOIL MOISTURE IMAGES (2000)

1 2 3 4 5 6 7 8

9 10 11 12 13 14 15 16

17 18 19 20 21 22 23 24

25 26 27 28 29 30 31 32

33 34 35 36 37 38 39 40

41 42 43 44 45 46 47 48

49 50 51 52

1988 1998

2000 2002

WEEKS IN KHARIF DURING WHICH SOIL MOISTURE LEVEL ACHIEVED ABOVE 0.25, AFTER SUMMER SEASON IN NORTHERN INDIA

21 22 23 24 25 26 27 28 NA

May June

NA indicates that moisture has not crossed 0.25 level before July 15

Week Number

July

Analysis Duration: April 23 (Week 17) to Aug. 27 (Week 35)

CMD/ARG/RESIPA/SAC

•CHANGES IN AGRICULTURE PRACTICE IN PUNJAB/HARYANA (1988-2000)

•SEVERE DROUGHT IN U.P. (2002)

U.P

PUNJAB

HARYANA

Interannual variation in agricultural practicesInterannual variation in agricultural practices

RETRIEVAL OF

LAND SURFACE TEMPERATURE (LST)LST=C0 + C1*T19V + C2*T19H + C3*T22V + C4*T37H

-0.3031.3610.225-0.36234.97310&15

-0.0681.4320.216-0.5371.8669

-0.4931.271-0.0570.17837.7166

-0.7111.1950.3190.295-17.4473

0.3170.544-0.1480.461-36.771

C4C3C2C1C0Land Class

-0.3031.3610.225-0.36234.97310&15

-0.0681.4320.216-0.5371.8669

-0.4931.271-0.0570.17837.7166

-0.7111.1950.3190.295-17.4473

0.3170.544-0.1480.461-36.771

C4C3C2C1C0Land Class

Class 1 -- Dense vegetation Class 3 -- Dense agricultural/rangeland vegetation Class 6 -- Medium density or sparse vegetation with wet soil background Class 9 -- Medium density vegetation/arable soil Class 10 & 15 -- Sparse vegetation in semi-arid/desert regions

Tmin = 0.8597LST + 39.603

R2 = 0.8648, RMSE = 2.00270

280

290

300

310

270 275 280 285 290 295 300 305 310

Land Surface temperature (oK)

Min

imum

Te

mp

era

ture

(oK

) After removing coastal and Hilly stations

MAE=1.98

9

10

Feb. 7, 2000 Feb. 7, 2000

Land Cover LST

(McFarland, 1991)

275 303

Some examples of Albedo and other Some examples of Albedo and other Radiation components over India using Radiation components over India using

ERBE productsERBE products

44 220Short wave radiation W/m2132 301Long wave radiation W/m2

-81 73Net radiation W/m2

0 60Albedo percent

Examples ofExamples ofRadiation components over IndiaRadiation components over India

using ERBE datausing ERBE data

Jan 1989

Jan 1989

Jan 1989

Jan 1989

Earth Radiation Budget Experiment (ERBE)Earth Radiation Budget Experiment (ERBE)

(Source: Langley Research Centre)

Albedo using ERBE products over IndiaAlbedo using ERBE products over India

0 56Albedo, percent

Jan 89 Feb 89 Apr 89

May 89 Jun 89 Aug 89Jul 89

Sep 89 Dec 89Oct 89 Nov 89

Mar 89

Monthly average Albedo Profile over different targets over India

Jan 89 Feb 89 Mar 89 Apr 89

Aug 89

Dec 89

May 89 Jun 89 Jul 89

Sep 89 Oct 89 Nov 89

Net Radiation using ERBE data Net Radiation using ERBE data

Net Radiation over India, ERBS observation

0

10

20

30

40

-100 -50 0 50 100 150

Watts/m2

La

titu

de

de

gre

e N

Jan-89

May-89

Jan 89

May 89

The zonal distribution of net radiation in winter and summer seasons of 1989 over India using ERBE data

A positive value of net radiation indicates a warming of the Earth while a negative value indicates cooling

Approaches and some issuesApproaches and some issues

Derivation & comparison of parameters from other satellites

Approach for Derivation of

Broadband Albedo from ScaRaB sensor data

INSAT type

INSAT type

INSAT type

INSAT type

Issues: Spectral characteristics and correctionsIssues: Spectral characteristics and corrections

Spectral normalization and comparison of Narrow/Broad bands with other sensors

BR

OA

DB

AN

D S

W A

LB

ED

O [

%]

VISIBLE ALBEDO [%]

Issues: Angular ParametersIssues: Angular Parameters

N

Nadir

Sun

[ SUN - SENSOR - TARGET GEOMETRY ]

v s

s

v+1800

Sensor

View zenith angle Sun zenith angle Sun azimuth angle

Schematic showing viewing geometry

Example:Viewing geometry parameters for INSAT

00 530 00 580 00 1780

10:30 IST

Angular normalization for vegetation class

Anisotropic factor for vegetation

Canopy Reflectance RT model

Atmospheric RT model

I/Ps: Biophysical,Viewing geometry input

At-sensorBidirectionalReflectance

I/Ps: Atmo. inputs

Modeling Angular effects for vegetationModeling Angular effects for vegetation

)(

)0(

v

vg

s=530

Feasible study and use of MT dataFeasible study and use of MT data

• Development of model to describe the relationship between passive Development of model to describe the relationship between passive microwave multi frequency/polarization brightness temperature and land microwave multi frequency/polarization brightness temperature and land characteristics for different MADRAS channels.characteristics for different MADRAS channels.

• To understand the spectral (narrow to broadband conversion) and angular To understand the spectral (narrow to broadband conversion) and angular characteristics of veg. targets for ScaRaB Sensor.characteristics of veg. targets for ScaRaB Sensor.

• Retrieval of parameters viz. soil moisture, land surface temperature, albedo Retrieval of parameters viz. soil moisture, land surface temperature, albedo etc from MT sensors data and comparison with other satellite data products.etc from MT sensors data and comparison with other satellite data products.

• Assessment and Comparisons of MADRAS and ScaRaB Data (Radiances, Assessment and Comparisons of MADRAS and ScaRaB Data (Radiances, Brightness Temperature) using other satellite data and products (INSAT, Brightness Temperature) using other satellite data and products (INSAT, EOS-AQUA/TERRA etc ) and ground measurements. EOS-AQUA/TERRA etc ) and ground measurements.

• Hydrological and vegetation dynamics modeling in different agro-ecosystem Hydrological and vegetation dynamics modeling in different agro-ecosystem using estimated parameters from MT data.using estimated parameters from MT data.

Thank YouThank You

Global Status on Passive Microwave Radiometers for Land ApplicationsGlobal Status on Passive Microwave Radiometers for Land Applications

SensorSensor SatelliteSatellite Frequency (Ghz)Frequency (Ghz) Spatial Spatial Resolution (km)Resolution (km)

RemarkRemark

Special Sensor Special Sensor Microwave Imager Microwave Imager (SSM/I)(SSM/I)

Defense Meteorological Defense Meteorological satellite Program satellite Program (DMSP)(DMSP)

19.419.4

22.222.2

37.037.0

85.585.5

6969

5050

37 37

1515

VVertical and ertical and HHorizontal orizontal Polarizations (except Polarizations (except 22.2 only H)22.2 only H)

Swath = 1200 kmSwath = 1200 km

Viewing 53.1degViewing 53.1deg

TRMM Microwave TRMM Microwave Imager Imager (TMI)(TMI)

Tropical Rainfall Tropical Rainfall Measuring Measuring Mission(TRMM)Mission(TRMM)

10.710.7

19.419.4

21.321.3

37.037.0

85.585.5

3838

1818

1717

1010

44

Swath = 790 kmSwath = 790 km

Viewing 52.7 degViewing 52.7 deg

Coverage : -38 deg to 38 Coverage : -38 deg to 38 degdeg

Scanning Multi channel Scanning Multi channel Microwave Radiometer Microwave Radiometer (SMMR)(SMMR)

Nimbus-7Nimbus-7 6.636.63

10.6910.69

18.018.0

21.021.0

37.037.0

150 & 25150 & 25 VVertical and ertical and HHorizontal orizontal Polarizations (except Polarizations (except 21.0 only H). Data: 21.0 only H). Data: 1978-19871978-1987

Multi-frequency Multi-frequency Scanning Microwave Scanning Microwave Radiometer Radiometer (MSMR)(MSMR)

IRS-P4IRS-P4 6.66.6

10.6510.65

1818

2121

40 to 150 40 to 150

Advanced Microwave Advanced Microwave Sensing Radiometer Sensing Radiometer (AMSR)(AMSR)

EOS – AQUA and EOS – AQUA and ADEOS-IIADEOS-II

6.92 to 89 6.92 to 89 Planned Soil Moisture as Planned Soil Moisture as a Producta Product

MEGHA TROPIQUEMEGHA TROPIQUE

(MT)(MT)

PlannedPlanned 10 to 15710 to 157 10 10 Electronically scanned Electronically scanned Thinned array Thinned array radiometerradiometer

SMOS SMOS

Synthetic Aperture Synthetic Aperture RadiometerRadiometer

Soil moisture and Ocean Soil moisture and Ocean Salinity Mission (ESA) Salinity Mission (ESA) PlannedPlanned

1.41.4 50 50 VVertical and ertical and HHorizontal orizontal Polarizations (Several Polarizations (Several Angles)Angles)

Global Status on Earth Radiation Budget Experiment SensorsGlobal Status on Earth Radiation Budget Experiment Sensors

SensorSensor SatelliteSatellite Ref.Ref.

ERBEERBE ERBS, Nimbus, ERBS, Nimbus, NOAA-9, NOAA-10NOAA-9, NOAA-10

Raschke Raschke et alet al., 1973., 1973

Jacobowitz Jacobowitz et alet al., 1984., 1984

Barkstorm Barkstorm et alet al., 1986., 1986

ScaRaBScaRaB METEOR, ResursMETEOR, Resurs Kandel Kandel et alet al., 1994., 1994

CERESCERES TRMM, EOS-TRMM, EOS-TERRA, AQUATERRA, AQUA

Wielicki Wielicki et alet al., 1996., 1996

GERBGERB MSG-1MSG-1 Harries Harries et alet al., 1999, 2000., 1999, 2000

ScaRaBScaRaB Megha TropiquesMegha Tropiques Proposed sensorProposed sensor