Indian National (Weather) SATellites for Agrometeorological Applications

Bimal K. BhattacharyaAgriculture-Terrestrial Biosphere- Hydrology Group

Space Applications Centre (ISRO)Ahmedabad 380015, India

Email : bkbhattacharya@sac.isro.gov.in

1996

1995/1997

1999 2003

IRS-1C/1D LISS-3 (23/70M)

STEERABLE PAN (5.8 M)WiFS (188M)

IRS-P3 WiFS, MOS X-Ray

IRS-P4 (OCEANSAT-1)OCM, MSMR IRS-P6(RESOURCESAT)

LISS 3 – 23.5/140 KmLISS 4 - 5.8M/ 27Km

AWiFS - 55M/ 730 Km

19991992

1990

1993

2002

2003

INSAT-1DVHRR

INSAT-2AVHRR

INSAT-2E VHRR, CCD (1

KM)INSAT-2BVHRR METSAT-1

VHRR – 2 Km(vis); 8 Km(IR & WV) INSAT-3A

VHRR – 2 Km(vis);

8 Km(IR & WV)CCD – 1 Km

PRESENT ...

2005

IRS-P5 (CARTOSAT-I) PAN 2.5 m stereo

IRS-P7 (CARTOSAT-II) PAN 0.85 m

2007

VIS : 2km TIR-day : 8km TIR-night : 8kmWV-day : 8km CCD FCC : 1kmOptical band Water Vapour band Thermal Infrared band Optical bands

KALPANA - 1 VHRR INSAT 3A CCD

1km x 1km4km x 4km4km x 4km

VIS(0.52-0.75), SWIR(1.55-1.70)MIR(3.8-4.0)WV(6.5-7.0), TIR1(10.2-11.2), TIR2(11.5-12.5)

ImagerINSAT 3D(2011)

10km x 10km19 channelsSounder100m X 100m1 km X 1km

Red, NIR, SWIRThermal IR

ImagerGeo-HR(2015)

Satellite Sensor Bands (m) Spatial res.

Kalpana -1 VHRR VIS (0.55-0.75)WV (5.7-7.1), Thermal IR (10.5-12.5)

2km x 2km8km x 8km

INSAT 3AVHRR

CCD Red (0.62-0.68), NIR(0.77-0.86), SWIR (1.55-1.69) 1km x 1km

Suite of Indian geostationary Sensors

MODIS precipitable water (cm)

Before noon (<1200 hrs) Afternoon (>1200 hrs)

RED DN NIR DNSWIR DN0300 GMT 0400 GMT 0500 GMT 0700 GMT 0900 GMT

Molecular scattering (Rayleigh)

MODIS ozone (Dobson)

MODIS AOD (tau550)

DEM (m)

Mie scattering

Level 1A correction

N

D

V

I

Level 1B & 1C correctionCloud

maskingCloud

masking

F

C

C

F

C

C

Flow of INSAT 3A CCD NDVI operational product generation

13 Aug to 28 Aug’08

6 Mar to 21Mar’09

25 May to 9June’08

1 Nov to 16 Nov’08

16 Oct to 31Oct’08

7 April to 22 April’09

2 Feb to 17 Feb’093 Dec to 18 Dec’08

10 June to 25 June’08 12 July to 27 July’08

14 Sept to 29 Sept’08

26 June to 11 July’08 28 July to 12 Aug’08

29 Aug to13 Sept’08 30 Sept to 15 Oct’08

17 Nov to 2 Dec’08 18 Feb to 05 Mar’09

22 Mar to 6 April’09 23 April to 30 April’09

VEGETATION DYNAMICS FROM INSAT 3A CCD

-1.00 – 0.00

0.13 – 0.150.16 – 0.180.19 – 0.210.22 – 0.240.25 – 0.260.27 – 0.300.31 – 0.34

0.35 – 0.370.38 – 0.410.42 – 0.460.47 – 0.500.51 – 0.600.61 – 0.690.70 – 0.790.80 – 0.90

0.01 – 0.12

INSAT TIR, WV Data3 Hourly Image

Conversion from Grey Count to TBs

Look Up Table for Calibration

Grid Average of IR TBs (0.250x0.250)

Collocation of IR TBs and MW

Rainfall

Estimation of Rainfall

IR and WV - Cloud Classification

PW & RH Correction

Corrected RainfallEstimation

Final Rain Rate, Daily, Pentad, monthly & Seasonal Rainfall

Model PW & RH Forecast

Satellite Microwave Rainfall (TRMM/SSMI)

Grid Avg. Rainfall (0.250x0.250)

Rainfall Validation/ Fine Tuning (DWR/SFRG)

Flow Chart for rainfall estimation from INSAT VHRR (Gairola et al. 2008)

Rainfall product

Surface Emissivity

Original VHRR thermal IR data

Correction for atmospheric water vapour & surface

emissivityCharacteristic vertical

tropospheric air temperature profiles and

AIRS sounder based surface air temperature

cm

K

K

K1VHRR LST

Experimental product – Land surface temperature (LST)

Precipitable water

290

295

300

305

310

315

320

290 295 300 305 310 315 320Aggregated MODIS AQUA LST(K) at 0.08o grid

K1V

HR

R L

ST (K

) at 0

.08

o grid

r = 0.93, n = 1446 RMSE = 2.30 K

1:1 Line

Agromet Applications

• Late season drought detection from NDVI• Identification of cold wave zone• Disaster (flood)• Evapotranspiration monitoring for stress

detection & productivity mapping

30thSept to 15thOct 2008 30thSept to 15thOct 2009

Late season agricultural drought detection from INSAT 3A CCD NDVI

-1.00 – 0.00

0.13 – 0.150.16 – 0.180.19 – 0.210.22 – 0.240.25 – 0.260.27 – 0.300.31 – 0.34

0.35 – 0.370.38 – 0.410.42 – 0.460.47 – 0.500.51 – 0.600.61 – 0.690.70 – 0.790.80 – 0.90

0.01 – 0.12-1.00 – 0.00

0.13 – 0.150.16 – 0.180.19 – 0.210.22 – 0.240.25 – 0.260.27 – 0.300.31 – 0.34

0.35 – 0.370.38 – 0.410.42 – 0.460.47 – 0.500.51 – 0.600.61 – 0.690.70 – 0.790.80 – 0.90

0.01 – 0.12

16thOct to 31stOct 2008 16thOct to 31stOct 2009

IMD June-Sept 2009

NDVI ranges

5 Feb’ 08 7 Feb’ 088 Feb’ 08 9 Feb’ 08

-3-2-101234

oC

Capturing Coldwave spell over Northwestern India during January and February2008 using nighttime thermal infrared data from Kalpana-1 VHRR

19 Jan’ 08 20 Jan’ 08 22 Jan’ 08

22 Aug 2008 31 Aug 2008 1 Sept 2008

2 Sept 2008 3 Sept 2008 5 Sept 2008

Capturing damaging events : Koshi flood 2008 from 3A CCD

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

Nov

05_d

ekad

2

Nov

05_d

ekad

3

Dec

05_d

ekad

1

Jan0

6_de

kad1

Feb0

6_de

kad1

Feb0

6_de

kad2

Feb0

6_de

kad3

Mar

06_d

ekad

1

Mar

06_d

ekad

2

AET

(mm

d-1

)

Trans Gangetic Plain

1.52

2.53

3.54

4.55

5.56

6.57

7.5

Nov

05_d

ekad

2

Nov

05_d

ekad

3

Dec

05_d

ekad

1

Jan0

6_de

kad1

Feb0

6_de

kad1

Feb0

6_de

kad2

Feb0

6_de

kad3

Mar

06_d

ekad

1

Mar

06_d

ekad

2

AET

(mm

d-1

)

Forest

0

0.2

0.4

0.6

0.8

1

1.2

Nov

05_d

ekad

2

Nov

05_d

ekad

3

Dec

05_d

ekad

1

Jan0

6_de

kad1

Feb0

6_de

kad1

Feb0

6_de

kad2

Feb0

6_de

kad3

Mar

06_d

ekad

1

Mar

06_d

ekad

2

AET

(mm

d-1

)

DesertAgriculture Forest Desert

AET November AET December AET January AET February AET March

RET November RET December RET January RET February RET March

Monthly Actual and Relative Evapotranspiration during rabi seasonusing surface energy balance approach

1.0

2.03.0

4.0

5.06.0

7.0

8.0

mm d-1

0.2

0.40.6

0.8

1.0>1.0

Automated Microclimatein situ observational NetworkUsing INSAT Communication

Transponder – A recent Initiativefrom ISRO

Defining 10 m INSAT- uplinked micrometeorological tower for short (2-3m) vegetation

Agro- Met Station (AMS)

Micromet tower network in India

Selection criteria : fetch ratio (1:50 to 1:100), Agroclimate, vegetation, soil type

CROP

GRASS

WETLAND

DESERT

Forecasting Agricultural out put using Space, Agrometeorologyand Land based observations (FASAL)

Econometry

Agro

Meteorology

LandObservations RS, Mod. Re.

TemporalRS, High Re.

Single date

Conventional Remote Sensing

MULTIPLE IN-SEASON FORECAST

Pre-Season

Early-Season

Mid-SeasonState

Pre-HarvestState

Pre-HarvestDistrict

Cropped areaCrop condition

Crop acreage

Crop yield

RevisedIncorporating damage

Thank you……..