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Computation and interpretation of different drought indicators

Dr. C.S. MurthyHead, Agricultural Sciences and ApplicationsNational Remote Sensing Centre, Hyderabad

murthy_cs@nrsc.gov.in, csmurthy09@gmail.com

SATELLITE MONITORING OF AGRICULTURAL DROUGHT

Ground water Storage

Surface Storage

Runoff

Rain

Infiltration &Percolation

Satellite Sensors

Evapo transpiration

Biophysical Parameters

Vegetation Indices

AgriculturalDrought

MeteorologicalDrought

HydrologicalDrought

Runoff

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GIS maps of rainfall status(Odisha state, India, Aug 2017)

RainfallRainfall deviation from the Normal

Cumulative Rainfall Aug2017 Deviation from Normal August Rainfall

GIS maps of rainy days(Odisha state, India, Aug 2017)

Rainydays deviation from the NormalRainy Days  in Aug2017

Deviation from Normal August Rainy Days

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Groundnut >90% of sown area

Groundnut >75‐90% of sown area

Groundnut >50‐60% of sown area

Groundnut >60‐75% of sown area

% of irrigated area, kharif

<20

21‐40

41‐60

Agricultural drought assessmentvisualisation of parameters 

Cropping pattern

Rainfall monitoring  at mandal  level

Percent of Normal

76 – 100

51 – 75

26 – 50

0 – 25

>   100

Source of data: KSNDMC, Government of Karnataka

Crop sown area progressUpto 21st July 2008

Sowing status upto 04 Aug 08

Sown area upto 04 Aug 08 40.9 l haNormal coverage: 56.38 la ha

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0

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JUNE JULY AUG SEP OCT

Fig. 4.27 Monthly MAI and NDVI Lingsugur Taluk, 2008

MAI NDVI

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Fig. 4.29 Monthly MAI and NDWILingsugur Taluk, 2008

MAI NDWI

Moisture adequacy vs. crop condition

Cellstructure

Leafpigments

Chlorophyllabsorption

Water content

Water absorptionREFLECTANCE

(%)

WAVELENGTH (um)

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6

80

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0

Spectral response of vegetation

Dominant factorcontrolling leaf reflectance

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Wavelength range (µm) 0.62 – 0.67 (red)0.841 – 0.876 (NIR) Spatial Resolution 250m  Swath 2330 kms

IRS‐WiFS   IRS P6 AWiFS

Spatial resolution   

56 metres 

Wave lengths   4 bands 

(green, red, NIR and MIR)

Swath : 700 kms.

Terra Modis NOAA AVHRR  

Spatial resolution  

188 metres 

Wave lengths   3 bands 

(green, red  and NIR  )

Swath : 700 kms.

Wavelength range (µm) 0.58 – 0.68 (red) 0.725 – 1.1 (NIR)3.55 – 3.93 (MIR)10.3 – 11.3 (TIR)11.5 ‐ 12.5 (TIR) Spatial Resolution 1.1 Km

Data from multiple satellites/sensors  NATIONAL STATE DISTRICT

Spectral response of vegetationRed – more absorption due to chlorophyllNear Infra red – more reflection due to leaf structure

Normalized Difference Vegetation Index (NDVI)NIR – Red / NIR+RedReflected radiation in Near infrared and red bands.NDVI ranges from        ‐1 to +1Water         – negative NDVIClouds       – zero NDVIVegetation – positive NDVI  represents density, vigor  

National Agricultural Drought Assessment & Monitoring System 

District level

Sub‐district level

NADAMSOperational serviceSeason : kharifObjective: prevalence, intensity and persistence of agricultural drought at district/sub‐district level

Coverage : 13 states

• Meteorological drought: reduced rainfall

• Hydrological drought: reduced surface water

• Agricultural drought: reduced soil moisture

Satellite/ Sensor Indices Relevant Parameter

Resourcesat AWiFS(60m)

NDVI, NDWI Crop condition, surface wetness

NOAA AVHRR (1km) NDVI Crop condition

Oceansat 2‐ OCM (360m) NDVI, ARVI Crop condition

Terra MODIS (500 m) SASI, NDWI Surface wetness/sown area discrimination

Terra AMSRE )25 km) Soil moisture Surface wetness/ sown area discrimination

INSAT 3A CCD (1 km) NDVI Crop condition

AWiFS NDVIFCC of R2  AWiFS

NDWI

AMSR E 

Soil moisture

OCM 2 NDVI

SASI

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NDVI anomaly Assessment(1) Relative dev.(2) VCI(3) In seasontransformation

Agricultural drought situation

Change in crop calendar

Lag between   VI & rainfall

Abnormal weather eventsSuch as floods

Extent of  VI anomaly

Extent of rainfalldeviation

Extent of sown areadeviation

Methodology for agricultural drought assessment

Normal

Watch

Alert

Drought warning(June, July, August)

Drought declaration(Sep, Oct)

Mild

Moderate

Severe

Agricultural Drought Monitoring Activity;Work flow

Satellite data procurement( AWiFS)

Geometric correction

Radiometric correction

Cloud masking

VI Generation

Time Composition

Map composition of VI images

VI statistics extraction at district/sub‐district level

Agril. area VI images

Ground data analysis (rainfall, sown areas)

Agril. Drought assessment

Satellite data (Resourcesat ‐ AWiFS• Season• Selection of previous normal years• Identification of path and row• Browsing• Data procurement

Ground data• Crop season and its duration• Major crops• Soil map• Irrigation support• Maps of district/sub‐districts• histroic and current (near realtime) rainfall• Area, yield and production of major crops – time series

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04 AUG 11 AUG

12 AUG

Time composition of NDVI

Maximum value compositing (MVC)procedure

Data generation – cloud cover problem 

04 AUG  NDVI 11 AUG  NDVI

12 AUG  NDVI Time composite NDVI

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Advantage of using moderate resolution  data sets Delineation of non agricultural areas in each district

FCC of Guntur district ‐WiFS

Use of AWiFS data makes drought assessment focused only on agricultural areas in each district

Forest areas

Fallow areas 

Aggregation at state levelAndhra Pradesh state

NDVIRatio of  difference and sum of surface reflectance in NIR and red spectral bands

NIR can see roughly 8 layersRed – one layer

Most successful indicator for describing vegetation

Normalisation ‐ reduces the effect of sensor degradation sensitive to changes in vegetationeasy to  compute and interpretavailable from most of the sensor systems

Limitations of NDVI

Chlorophyll based index – saturates with LAI (=3)Limited capability to detect vegetation water contentOver‐estimation when the veg. density is lessSaturation at peak vegetative phasesConservative indexTime lag

Spectral response of vegetationRed – more absorption due to chlorophyllNear Infra red – more reflection due to leaf structure

Normalized Difference Vegetation Index (NDVI)

NIR – Red / NIR+Red

Reflected radiation in Near infrared and red bands.NDVI ranges from        ‐1 to +1Water         – negative NDVIClouds       – zero NDVIVegetation – positive NDVI  represents density, vigor  

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25/Apr/12

03/M

ay/12

11/M

ay/12

19/M

ay/12

NDVI

NDVI profile over agricultural area

Satellite/ Sensor Indices Relevant Parameter

NOAA AVHRR (1km) NDVI Crop condition

Oceansat 2‐ OCM (360m) NDVI, ARVI Crop condition

Terra MODIS (500 m) SASI, NDWI Surface wetness/ sown areadiscrimination

Terra AMSRE (25 km) Soil moisture Surface wetness/ sown areadiscrimination

INSAT 3A CCD (1 km) NDVI Crop condition

Satellites SensorSpatial resolution

Temporal resolution  Swath

Resourcesat 1 & 2 AWiFS 56 m 5 days 750 kmLISS III 23 m 26 days 140 kmLISS IV 6 m 48 days 70 km

LANDSAT 8 OLI 30 m 16 days 185 km

Some important VI data sets

Coarse resolution data

Moderate resolution data

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.

-100

-50

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100

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250

300

.

12/6 19/6 6/6 3/710/7 17/7 24/7 31/7 7/8 14/8 21/8 28/8 4/9 11/9 18/9 25/9

% d

evi

atio

n

Agricultural Drought Assessment

Combination of indices for assessment

Strong ground data base

Sub‐district level assessment

Objective and user friendly information

Close interaction with user departments

0

0.1

0.2

0.3

0.4

0.5

0.6

June July Aug. Sept. Oct. Nov.

Month

ND

VI

Normal delayed season Drought

(1) relative deviation from normal, (2) vegetation Condition Index, (3) in season rate of transformation

Integration with ground data

.

0102030405060708090

100

5 J

un

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31

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ug

14

Au

g

21

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g

31

Au

g

11

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18

Se

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25

Se

p

30

Se

p

% o

f no

rma

l

Seasonal NDVI profiles for drought assessmentWeekly deviations of rainfall

Weekly progression of sown areas

Tie up with ground depts.

NDWI/LSWI

Reflectance in 0.9 – 2.5 microns dominated by  liquid water absorption

Sensitive to surface wetness/ vegetation moisture

Less affected by atmosphere

Reflectance 1.5‐2.5 microns does not saturate till LAI  reaches 8

SWIR availability – AWiFS, LISS‐III, MODIS, INSAT 3A

MODIS – 3 SWIR bands – 1240 nm, 1640 nm, 2100 nm

LSWI – Land Surface Wetness Index – uses SWIR at 2100 nm – very sensitive to wetness

NDWI/LSWI uses

Agriculture – crop stress detection, crop yield, classification of succulent crops, surface moisture

Forest – Monitoring, burnt area detection

(NIR-SWIR/NIR+SWIR)

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Most commonly adopted index – NDVIa) chlorophyll based indexb) plant vigour and densityc) easy to compute and interpretd) robust indexe) Limitations – soil back ground,

saturation, time lag etc.

Commonly used indices for drought assessment

Recently popularized index – NDWIa) Plant moisture based indexb) NIR and SWIR basedc) No saturation issuesd) Immediate responsee) Sensitive to surface wetness during sowing period

Combination of NDVI and NDWIa) Overcomes limitations of either oneb) amplifies anomalies andc) more responsive  to ground situation

NDVI anomaly

% dev. from normal

(actual NDVI‐normal NDVI)/normal NDVI*100

Selection of normal year –average of recent past normal years

NDVI is a conservative indicator and hence anomalies  are not very high 

Thumb rule:> 20% reduction in NDVI – drougtconditions

>30% reduction indicate moderate to severe drought conditions

Interpretation of NDVI changes to assess Agricultural drought  

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• Derivative of NDVI• Substitute to NDVI deviation

VCI = NDVI‐ NDVImin /(NDVImax –

NDVImin) *100

Range 0‐100%

<=25%  severe drought25‐50 Moderate drought50‐75 Mild to moderate>75 Normal

Vegetation Condition Index (VCI)

Interpretation of NDVI changes to assess Agricultural drought  

Vegetation Condition Index (VCI)

Computation

Septemberyear NDVI VCI

2000 0.5 8

2001 0.55 502002 0.6 922004 0.52 252005 0.55 50

2006 0.51 172007 0.49 02008 0.59 832009 0.61 100

2010 0.54 422011 0.51 17

Mean 0.54

Min 0.49Max 0.61diff 0.12

Some critical issues

Time series data base(at least 10‐12 years)

Differences due to cropping pattern, crop calendar to be normalised

VCI = NDVI‐ NDVImin /(NDVImax – NDVImin) 

*100

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Interpretation of NDVI changes

Reduction in NDVI is caused by;

Crop moisture stressFlooding/excess rainfall

Crop and crop stage differences between the two years  under comparison

Limitations of NDVI based interpretation

Time lag between rainfall and NDVI manifestation

Saturation with crop growth

Despite its limitations, NDVI is a successful indicator for agricultural drought assessment

Block level crop condition – Anantpur districtComparison between normal year (2005) and drought year (2002)

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Tadipatri mandal - Anantpur (Groundnut)

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

June July August Sep October

ND

VI

2005 2002

Pamidi mandal - Anantpur (Paddy)

-0.050

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

June July August Sep October

ND

VI

2005 2002

Impact of 2002 drought at mandal level Anantpur district

Mudigubba mandal - Anantpur districtgroundnut

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

June July August Sep October

ND

VI

2005 2002

Kanekal mandal - Anantpur districtgroundnut + paddy

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

June July August Sep OctoberN

DV

I

2005 2002

Crop area affected by drought in kharif 2015, West Bengal

5

17

40

29

81

0

10

20

30

40

50

< 10 %10‐20%

20‐30%

30‐40%

40‐50%

< 50 %

Agriltural area Affected 

(%)

% of reduction in crop condition

Normal

Mild Moderate

Severe

Normal

Mild Moderate

Severe

10

26

47

15

2 00

10

20

30

40

50

< 10 %10‐20 %20‐30 %30‐40 %40‐50 %< 50 %

Agriltural area Affected 

(%)

% of reduction in crop condition

Normal

Mild Moderate

Severe

23

3931

61 0

0

10

20

30

40

50

Agriltural area Affected (%)

% of reduction in crop condition 

Purulia district Bankura district West Midnapur district

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AWiFS derived crop condition anomalies showing agricultural drought situation   in Andhra Pradesh, kharif 2011 

Sep 2011 Sep 2010 NDVI anomaly Sep 2011

NDWI anomalyOct 2011

0.00

0.10

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0.40

0.50

FN1‐SEP FN2‐SEP FN1‐OCTFN2‐OCT Nov

NDVI

2010 2011

0.00

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0.15

0.20

0.25

0.30

0.35

F1SEP F2SEP F1OCT F2OCT F1NOV

NDWI

2011 2010

NDWI ‐ Roddam mandal, AnantpurNDVI ‐ Roddam mandal, Anantpur

164 mandals394  mandals

562 mandals

Assessment atsub‐district level

Resourcesat AWiFS – A work‐horse for monitoring Agriculture

AWiFS NDVIOct. 2010 (Drought year)

AWiFS NDVIOct. 2008 (Normal year)

1. Pas. Champaran2. Pur. Champaran3. Sheohar4. Sitamarhi5. Madhubani6. Supal7. Ararai8. Kishanganj9. Purnia10. Madhepura11. Saharsa12. Darbhanga13. Muzaffarpur14. Gopalganj15. Siwan16. Saran17.Vaishali18. Samastipur19. Begusarai

20. Khagaria21. Katihar22. Bhagalpur23. Banka24. Munger25. Lackeesarai26. Sheikhpura27. Nalanda28. Patna29. Bhojpur30. Buxar31. Bhabua32. Rhotas33. Aurangabad34. Jahanabad35. Gaya36. Nawada37. Jamui

‐60

‐40

‐20

0

Jamui

Gaya

Jahana…

Buxar

Aurang…

Samasti…

Bhojpur

Nalan

da

Patna

Bhagalp…

% deviation from 

norm

al

Rainfall deficiency

0

50

100

Jamui

Gaya

Jahana…

Buxar

Aurang…

Samasti…

Bhojpur

Nalan

da

Patna

Bhagal…% of norm

al

Crop sown area status

Crop areas affected by agriculturaldrought situation are showing lower NDVIcompared to normal, in kharif 2010 inBihar state.

Agricultural drought assessment(based on multiple indices; NDVI, NDWI, SASI)

Moderate  (103)Severe   (55)

Moderate  (103)Severe   (55)

Satellite derived  Area Favourable for Crop Sowing/Crop Sown Area (AFCS) ,  Lakh ha.

Kharif Nromal Area

AFCS AFCS Unfavourable

Jul‐10 Aug‐10 area

37 19 23 14

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Satellite based agricultural drought assessment

kharif 2010, West Bengal 

Monthly Condition Assessment of Kharif Cropped Area (2016 Vs. 2014)

+ 10% 10‐20%20‐30%30‐40%40‐50%< 50%

Sep Oct NovDeviation 

Area (in ha)

Affected area

Rajkot district, Gujarat

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Fortnightly NDVI Composite of Resourcesat‐2 AWiFS – 2016  

1 FN June 2 FN June 1 FN July 2 FN July

1 FN Aug 2 FN Aug 1 FN Sep 2 FN  Sep

1 FN Oct 2 FN  Oct 1 FN Nov 2 FN  Nov

Increasin

g vegetatio

n vigo

ur

Sep 2017(partly under drought)

Sep 2016(partly cloud covered)Normal year

Sep 2015

Drought year

Sep 2014Normal year

AWiFS derived Agril. area NDVI

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Drought 

conditions

AWiFS NDVI anomaly  indicating agricultural drought conditions in parts of the country

Sep 2017 vs. Sep 2014

Source: MNCFC

Meteorological drought in the current year 

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0

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09 Jun

25 Jun

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27 Jul

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ug

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ug

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ep

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ep

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a 00

0’ h

a

SA

SI

Weeks

SASI Sown area

Response of SASI to crop sown area

Features SASI valueDry vegetation low negativeMoist veg. high negative

Shortwave Angle Slope Index (SASI)

βSWIR1 = cos-1 [ (a2 + b2 - c2) /(2*a*b) ]Slope = (SWIR2 − NIR)SASI = βSWIR1 * Slope (radians)

where a, b and c are Euclidiandistances between vertices NIRand SWIR1, SWIR1 and SWIR2,and NIR and SWIR2, respectively

Chronological synchronization between(a) Decrease in SASI(b) Increase in rainfall(c) Increase in sown area

NADAMS projectConceptually and computationally simpleprocedures to discriminate the crop sowingfavorable areas at state level

Features SASI value

Dry soil highly positiveWet soil low positive

‐0.30

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09 June

17 June

25 June

3 July

11 July

19 July

27 July

04 Aug

12 Aug

20 Aug

28 Aug

05 Sep

13 Sep

21 Sep

29 Sep

SASI values

2002 2006 2009

Seasonal dynamics of SASI

Before crop sowing

Crop growing and maturity

Commencement of crop sowing

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‐1

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01/N

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ov/11

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ov/11

25/N

ov/11

03/Dec/11

11/Dec/11

19/Dec/11

27/Dec/11

04/Jan/12

12/Jan/12

20/Jan/12

28/Jan/12

05/Feb

/12

13/Feb

/12

21/Feb

/12

29/Feb

/12

08/M

ar/12

16/M

ar/12

24/M

ar/12

01/Apr/12

09/Apr/12

17/Apr/12

25/Apr/12

03/M

ay/12

11/M

ay/12

19/M

ay/12

SASI

Seasonal SASI profile

Area Favourable for Crop Sowing/Crop Sowan Area (AFCS) 

Geospatial product on Area Favourable for Crop Sowing (AFCS)using multi-criteria approach

Soil Texture

Rice area mask

Kharif areamask

SASIModelledsoil moisture

Input datasets

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SeptemberAugust

JulyJune State Kharif 

normal

AFCS  M ha. Unfavorablearea

state June July Aug Sep

Andhra Pradesh 7.8 2.0 6.8 6.9 7.3 0.4

Bihar 3.7 0.7 3.6 3.7 3.7 0

Chhattisgarh 4.8 3.2 4.8 4.8 4.8 0

Gujarat 8.7 1.3 5.0 5.8 8.1 0.6

Haryana 2.8 0.6 1.6 2.8 2.8 0

Jharkhand 2.5 0.3 2.4 2.5 2.5 0

Karnataka 7.5 3.5 6.0 6.0 7.0 0.5

Madhya Pradesh 10.4 0.7 9.7 10.3 10.4 0

Maharashtra 14.0 5.5 13.2 13.8 13.8 0.2

Odisha 6.3 3.9 6.1 6.2 6.3 0

Rajasthan 14.3 0.2 4.4 11.7 13.6 0.8

Tamil nadu 2.4 1.1 1.8 2.0 2.0 0.4

Uttar Pradesh 9.3 2.8 8.7 9.2 9.3 0

Sub‐Total 94.5 25.8 74.2 85.7 91.7 2.9

All India 108.6 34.2 87.0 97.7 105.5 3.1

Area Favourable for Crop Sowing (AFCS) derived from SASI and water balance methodology,  Kharif 2012

Soil moisture

Satellite based• Large area, daily coverage• 25‐50 km resolution• Increasing popularity Several microwave sensors• SMRR – 1978‐1987• TRMM – TMI since 1997• Scatterometer – ERS 1 & 2• ASCAT – MetopA• AMSRE – 2002‐2011• SMOS – 2009• SMAP ‐ 2015Retrieval algorithms from passive systems• NASA• LPRM• PRI

Soil moisture important data for hydrology, agriculture, environment, climate system etc.

Sources of soil moisture  data

I. Insitu measurements non‐spatial dataManual• accurate• inadequate coverageAutomatic systems• calibration related issues• large area coverage is 

expensive

Spatial dataNon‐spatial data

Hydrological models

• Mass balance approach• Profile level moisture• Parameterisation of 

models – challenge

Soil moisture products from NRSC

• VIC hydrological models – daily soil moisture images

• AMSR 2 LPRM soil moisture 25 km, 2 day frequency 

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4_11 JUNE 12_18 JUNE 19_25 JUNE 26_2 JULY 3_9 JULY 10_16 JULY

Tracking the drought conditions of 2014 using LPRM Soil Moisture datasets of NRSC

17_23 JULY 24_30 JULY 31_06 AUGUST17_23 JULY

Soil moisture deviations from normal in 2014

0.000.050.100.150.200.250.300.350.40

01 jun

05 Jun

09 Jun

13 Jun

17 Jun

21 Jun

25 Jun

29 Jun

03 Jul

07 Jul

11 Jul

15 Jul

19 Jul

23 Jul

27 Jul

31 Jul

04 Aug

08 Aug

12 Aug

Soil moisture (m3/m

3)

LPRM_SM 2014 LPRM_SM 2013

Drought frequency in the sowing period

SOIL MOISTURE -Time Series dataSOIL MOISTURE -Time Series data

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

CV_20

Mean

Drought_Years

CV Frequency of SM reduction

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Weather based indices

Rainfall deviationStandardised Precipitation IndexMoisture Adequacy Index

Hydrological indicators

Reservoir Storage IndexGround water index

Weather and hydrology indicators

nrscOperational Web-based National Hydrological Modeling System

Hydrological Modeling Framework

Temperature

Rainfall

Elevation

Soil

Land use

Output Products

Soil Moisture

Evapotranspiration

Runoff

Input dataset

Variable Infiltration Capacity Hydrological Model

Open source; Grid-wise water and energy balance

Sub-grid heterogeneity of Land cover

Soil depth-wise hydrological response

Vegetation phenological changes

Daily / sub-daily time step

Geo-spatial data

Terrain - Topographic, Soil (NBSSLUP), LULC (NRC-250k), LAI, Albedo

Meteorological – Rainfall, Temperature, … (IMD & CPC)

Hydrological - River discharge, Reservoir Storage/Releases, GW levels, …

9 min (~16.5km), 3 min (~ 5.5km) Grid-wise data base

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nrscXYZ

Meteorological Data Source

Parameter Resolution Latency

IMD Gridded data Rainfall, Min, Max Temperature

0.5 degree 1 day

IMD AWS data Rainfall, Min, Max Temperature

Point data (interpolated to 9min/3min)

1 day

IMD high density data (Godavari & 

Mahanadi)

Rainfall Point data (interpolated to 

3min)

1 day

CPC Rainfall 0.1 degree 2 days

GEFS Rainfall, Min, Max Temperature

0.5 degree (interpolated to 9min/3min)

Daily forecastdata

APSDPS AWS data Rainfall, Min, Max Temperature

Point data (interpolated to 

3min)

1 day

Data Sources and related info.

Product Resolution Frequency

Water BalanceComponents for entire 

India

3min (~5.5km), 9min (~16.5km)

Daily

Forecast Surface Runoff(d*+3)

9min (~16.5km) Daily

Accumulated Surface Runoff

9min (~16.5km) Daily

Climate Indices – SPI , SRI (1, 3, 6, 12 Months)

9min (~16.5km) Daily

River Basin Wise Statistics ‐ Weekly

WBC’s for Godavari, Mahanadi River

3min (~5.5km) Daily

Web Published VIC Model Derived Products

Tikerapara

Fig. Mahanadi Basin

nrscModel Calibration and Validation 

Mahanadi NSE

1990 0.82

1991 0.85

1992 0.85

1993 0.62

1994 0.83

1995 0.54

• Model is calibrated using CWC river discharge data at river basin scale for the period 1972‐2006.

• Average Nash Sutcliff coefficient (NSE) of 0.71 is observed.

Case1: Uniform rainfall across India Case2: Region specific rainfall

Soil Moisture Validation 

Observed Data: IMD Soil Moisture Data, 2013

Pearson Correlation Coefficient (R2) ~ 0.3 ‐ 0.7

Number of Observation Stations: 130

ET Validation 

Observed Data: MODIS 1km 8day Product, 2013

Comparison of VIC ET VsMODIS ET estimates.

Pearson Correlation Coefficient (R2) ~ 0.5 ‐ 0.6

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nrscSeasonal Water Balance Components Estimation

Jun‐Oct, 2016

RunoffRainfall Evapotranspiration

Daily Mean (India) Water Balance Components

nrscSeasonal Water Balance Components Estimation

Jun‐Oct, 2017

RunoffRainfall Evapotranspiration

Daily Mean (India) Water Balance Components

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Field enumeration for relief management

• Drought declaration

• Notification of affected areas

• Field enumeration

• Relief assessment and distribution

2015 Yield dataDrought year

2014 YieldNormal year

yield

No. of  GPS2014

No. of  GPS2015

No data 23 0

< 1 t/ha 271 12731‐2 534 25492‐3 1711 9903‐4 1967 6384‐5 1251 4705‐6 357 2196‐7 50 407‐8 19 58‐9 2 1

Kharif rice yield comparison

GIS mapping of drought impact on rice yield

Drought year

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0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

<0.4 0.4‐<0.5 0.5‐<0.55 0.55‐<0.6 0.6‐<0.65 =/>0.65

GP average rice yield (kg/ha)

Season's max. NDVI of  rice crop  (GP average)

Paddy yield Vs. NDVI – kharif 2016, Odisha

Mobile Apps for crop field data collection

Improved  field data collection system

• Real‐time field data collection,  

robust & versatile system, 

automation etc.

• Surveillance of events, automated 

alerts generation and dissemination 

• Objective enumeration system

• Localised crop damages

Field Data Collection using Geo‐ICT 

Observation TransmissionInformation Decision Action

Value addition and information products from field data

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Crop‐sowing variability information ‐ crucial input for (1) Cropmanagement plans, (2) crop growth modelling based crop yieldestimation

Crop prospects information products – act as proxies for expected yieldfor crop insurance and drought impact assessment

Information on Midseason adversaries – Pest/Disease incidence for cropmanagement and crop insurance applications

Evidence based verification final CCE yield data for crop yield assessmentand indemnity computation in insurance

Integration of mobile data with satellite data

Analytics on the Mobile app based field data andgeneration of value added information products

Mobile AppsImproved  field data collection system

• Real‐time field data collection,  robust & 

versatile system, automation etc.

• Surveillance of events, automated alerts 

generation and dissemination 

• Objective enumeration system

• Localised crop damagesField Data Collection using Geo‐ICT 

Observation Transmission Information Decision Action

Value addition and information products from field data

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Details of International Systems for drought monitoring

US Drought  Monitor  Five Parameters

Palmer drought index

CPC Soil Moisture Model

Standardized Precipitation Index (SPI)

Satellite Vegetation Health Index

VEG DRI – Drought response modelling with data 

mining techniques

FEWS NET NOAA NDVI Meteosat Rainfall

IWMI Drought Monitor

MODIS NDVI differences

FAO Drought MonitoringARTEMIS/GIEWS

• NDVI Difference between Current Decade and Average (SPOT 4)• Cold Cloud Duration

China Drought Monitoring

NDVI deviation from coarse resolution data,LST

Global Scenario

Harmonization of data & Unified index – A Classic example

• Multi disciplinary • Involvement of multiple organizations(about 150 scientists are involved)

• Comprehensive drought assessment• It is not a forecasting/early warning system

Drought monitor is atclimatic division averaged.

Not good for local leveldecision making

Drought Monitoring ‐ Global Scenario 

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Criteria in US Drought monitor

Category Palmer drought index

CPC soil moisture model

Stream flows

SPI Satellite Vegetation health index

Normal -1 to -1.9

21-30 21-30 -.5 to -.7

36-45

Moderate drought

-2 to -2.9

11-20 11-20 -.8 to -1.2

26-35

Severe drought

-3 to -3.9

6-10 6-10 -1.3 to -1.5

16-25

Extreme drought

-4 to -4.9

3-5 3-5 -1.6 to -1.9

6-15

Exceptional drought

-5 or less

0-2 0-2 -2 or less

1-5