Operational Crop Monitoring Using Operational Crop Monitoring Using Synthetic Aperture Radar (SAR) Synthetic Aperture Radar (SAR)

C PATNAIKSpace Applications Centre

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SAR SYSTEMSSAR SYSTEMS

• Frequent cloud cover during monsoon and sometimes in winter is a hindrance for using data from optical remote sensing

• SAR, due to its self illuminating beam, has all weather and day/night acquisition capability

• Current space borne SAR systems are available in 3 frequencies: C, L and X

• For agricultural crop monitoring C band SAR is found to be most suitable

• A variety of beam modes are available from these sensors based on the look angles

• Operational C band SAR systems currently available are Envisat ASAR and Radarsat

• Envisat ASAR has low swath coverage with an image size of not more than 8500 km2

• Radarsat ScanSAR beam has coverage of 90000 km2 with Pixel spacing of 25 m

• For jute crop, due to small field sizes, Wide 2 beam is chosen. Image size is 22500 km2 and pixel spacing is 12.5 m

• Multi temporal SAR data is acquired to monitor the crop growth and use it for classification

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CROP DISCRIMINATION USING SARCROP DISCRIMINATION USING SAR

• The backscatter response is a function of the crop roughness, moisture and geometry

• Different crops have different backscattering properties based on their canopy structure and moisture content

• In the case of rice crop, the background standing water has a significant contribution to the backscatter till the PI stage

• In the case of crops where there is no background standing water, the backscatter is influenced by row orientation, density, canopy & soil moisture and roughness

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FASAL – SAR COMPONENTFASAL – SAR COMPONENT

RICE JUTE

Number of states covered 13 (K) + 4 (R) 03

Remote Sensing Sensor Radarsat SAR

Mode of pass Descending

Beam Position ScanSAR Narrow B Wide 2

Frequency (GHz) of C band 5.3

Polarisation HH

Swath (km) 300 150

Pixel Spacing (m) 25 12.5

Incidence angle 31-46º 31-38º

Number of repeat passes (acquisitions) 03 (4) 03

Parameter measured Return Signal (backscatter)

Seasons Kharif and Rabi Pre kharif

Forecasts Early Sept, Oct; Dec Mid July

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FASAL - RICEFASAL - RICE

Wet Season (Kharif)Total Coverage: 13 states accounting for>93 % rice production and>88 % acreage

36 Radarsat ScanSAR Narrow B frames acquired on three dates. (108 scenes)

(1999 )

Winter Rice (Rabi)Total Coverage: 04 states accounting for>86 % rice production and>82 % acreage

15 Radarsat ScanSAR Narrow B frames acquired on three dates. (45 scenes)

(2007 )

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PARTICIPATING AGENCIESPARTICIPATING AGENCIES

Scientists from the following centres / agencies participate in the project:

1. Space Applications Centre, Ahmedabad2. National Remote Sensing Centre (NRSC), Hyderabad3. Remote Sensing Applications Centre (RS AC-UP), Lucknow, Uttar Pradesh4. State Remote Sensing Applications Centre, Bhopal, Madhya Pradesh5. Institute of Environmental Studies and Wetland Management (IES&WM),

Kolkata, West Bengal6. Orissa Space Applications Centre (ORSAC), Bhubaneswar, Orissa7. Bihar Remote Sensing Applications Centre (BIRSAC), Patna, Bihar8. State Remote Sensing Applications Centre (JRSAC), Jharkhand9. Assam Remote Sensing Applications Centre (ARSAC), Guwhati, Assam10.AP State Remote Sensing Applications Centre (APSRAC), Hyderabad, Andhra

Pradesh11.Punjab Remote Sensing Centre (PRSC), Ludhiana, Punjab12.Department of Agriculture, Chennai, Tamil Nadu13.Remote Sensing Applications Centre, Raipur, Chhattisgarh14.Karnataka State Remote Sensing Applications Centre, Bangalore, Karnataka15.Haryana Remote Sensing Applications Centre (HARSAC), Hissar, Haryana

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DATA ANALYSISDATA ANALYSIS

DATA &EPHEMERIS DOWNLOAD

SPECKLE SUPPRESSIONSLANT – GROUND RANGE CONV.

DATA TRUNCATION

MULTI LAYER IMAGE STACK

GEOREFHEADER GCPs

ANALYSIS

Generation of Decision Rules & Classification

Accuracy Checking

Signature Generation

Validation and Forecast

Transfer of sample segments

Transfer of GT sites andAncillary Information

to image

Aggregation

8SECOND FORECAST

OPTIMAL DATA SETOPTIMAL DATA SET

Pu

dd

ling

Pe

ak V

eg.

Tille

ring

Tra

ns

plan

ting

E. F

low

erin

gFIRST ACQ. SECOND ACQ. THIRD ACQ.

Data is acquired based on the region’s crop calendar. Normally three dates are acquired; however, in some critical cases a fourth date is acquired.

FIRST FORECAST 45 Days after transplanting. Accounts for more than 75% season’s rice

30 days before harvesting

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MULTI TEMPORAL SAR DATAMULTI TEMPORAL SAR DATA

July 05, 2010 July 29, 2010 August 22, 2010

Normal Transplanted Rice Late Transplanted Rice Very late transplanted rice

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RICE IN MULTI TEMPORAL SAR DATARICE IN MULTI TEMPORAL SAR DATA

Two date Composite Three date Composite

(July 05, July 29, 2010) (July 05, 29 and Aug. 22, 2010)

On two date composite, early transplanted rice would show as cyan and due to land preparation late transplanted rice would show red tones

On three date composite, early rice is in blue tones and late transplanted rice is in magenta tones. Yellow tones are very late transplanted rice areas

Plantations

Urban

Water body

11

-24

-20

-16

-12

-8

-4

0

-24 0 24 48 72 96 120 144 168

DAYS (JULY 1 = 1)

BA

CK

SC

AT

TE

R C

OE

FF

. (d

B)

Rice

Water

Urban

Homestead

Cotton

Maize

Sunflower

TransplantationPre-transplantation Tillering Vegetative MaturityHeading Peak-vegetative

1 2 3 4 5 6 7

CROP DISCRIMINATION WITH TEMPORAL SAR CROP DISCRIMINATION WITH TEMPORAL SAR

2

34

56

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CLASSIFICATION CLASSIFICATION

1. Mask out the non-agriculture area

2. Within the agriculture area:

i. Delineate the crop phenology based on ground information

ii. Take multiple areas where soil conditions are similar – this would help in correlating backscatter with canopy.

iii. Conversely, with crop condition being similar, study the effect of soil conditions on backscatter

iv. Generate crop profiles based on the backscatter to help in the discrimination

v. Generate a knowledge base

vi. Create the decision rules (hierarchical)

vii. Classify the image and do accuracy check

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SAMPLE SEGMENT APPROACHSAMPLE SEGMENT APPROACH

District/Zone wise classification based on ground truth is done to accommodate different management practices. For each run, image under the segments of a zone is classified.

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ACREAGE ESTIMATIONACREAGE ESTIMATION

After classification and accuracy check:

• Crop proportion per segment is calculated

• Proportion is multiplied by N to get the crop area for the stratum

• Correct for the pseudo stratum area based on the factor derived from geographical

area to N segments area.

• Aggregate the stratum wise figures to obtain state level area.

• Project to National rice area

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YIELD MODELINGYIELD MODELING

Yield for the season is modeled based on the following information.

Daily weather data

Station latitude

Rain fall, Tmax, Tmin and solar radiation

GDDs to reach emergence

GDDs from emergence to flowering

Historical yield database of the region for the past 15-20 years is considered.

Production estimates are made and released along with acreage forecasts.

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PROGRESS OF RICE TRANSPLANTATIONPROGRESS OF RICE TRANSPLANTATION

Part of W. Bengal Part of Orissa

15th to 30th June; 1st to 8th July; 9th to 16th July; 17th to 23rd July; 24 th to 31st July;1st to 15th August.

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BIOMASS RETRIEVALBIOMASS RETRIEVAL

RadarsatJul 23Aug 16Sep 09

0

2

4

6

8

10Kg/m2

Sep 09

Rice Biomass Map of the area3-Date FCC of the area

y = 0.3536x2 - 0.3796x - 15.612

R2 = 0.8632

-18

-16

-14

-12

-10

-8

-6

-4

0 2 4 6 8Wet Bio-mass (kg.m 2̂)

Bac

ksca

tter

(d

B)

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DAMAGE ASSESSMENTDAMAGE ASSESSMENT

Flood Affected Rice Area Assessment

Super cyclone of Orissa, Oct 29, 1999

Crop at soft dough stage.

Crop lodging and submergence were the main causes of damage.

Assessment made by Nov.06.

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DAMAGE ASSESSMENTDAMAGE ASSESSMENT

2008 Normal Year

2009 Drought year

2009: Jul 08, Aug 01, Aug 25

Yellow : Rice

2008:

Jul 13, Aug 06, Aug 30

SAR Data FCC Classified

Drought Affected Rice Area

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MONITORING OF RICE CULTURAL TYPE

Legend

Permanent vegetation

Deep water rice

Intermediate water rice

Shallow water rice

Urban

Water

Multi-date FCC ScanSAR image Rice Cultural Types (Derived from remote sensing)

WEST BENGALSIKKIM

BHUTAN

AS

SAM

BANGLADESH

BIHAR

ORISSA

BAY OF BENGAL

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FASAL - JUTEFASAL - JUTE

• Area under jute: 7.71 lakh ha in 2008-09.

• Assam, Bihar, West Bengal and Orissa are the major jute growing states in the country, which account for about 98 % of jute area.

• The following table shows the state contribution to national jute area.

State Per Cent Contribution

Major jute growing districts

West Bengal 78 Nadia, Murshidabad, Uttar & Dakshin Dinajpur, Jalpaiguri, Cooch Behar, Hugli, N-24 Parganas, Bardhaman, Malda

Bihar 10 Purnea, Katihar, Kishanganj, Supaul, Madhepura, Araria (Source: NIC)

Assam 09 Dhubri, K.Anglong, Sonitpur, Dibrugarh, Kamrup, Darrang, Nagaon, Barpeta, Goalpara, Marigaon

Orissa <1 Balasore, Cuttack, Keonjhar, Jajpur, Kendrapara

Source:Agricultural Situation in India

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ROAD MAPROAD MAP

• About 85% of world’s jute cultivation is concentrated in Ganges delta.

• Sown by mid April and harvested by mid July. No row spacing.

• Crop identification was possible using 3 date SAR data.

• Other vegetation class found was mostly homesteads, forest and occasional

patches of vegetables (<1 ha).

• Signature generation of various land cover was carried out.

• Signature analysis showed that the crop signature in SAR stands apart from other

land cover.

• Since 2008, state and national level pre-harvest estimates were made.

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• Radarsat Wide beam 2 data of three dates is used.

• 11 frames x 3 dates = 33 scenes

• GT using GPS comprises of land cover, crop and soil parameters.

• Based on signatures of different features, decision rules are developed for

jute crop discrimination.

• Average accuracy around 92 %. Overall accuracy around 91 %.

• Three major Jute growing states of Assam, Bihar and West Bengal are taken up.

• State and national level pre-harvest estimates are made.

• Final forecast of jute is given by mid July.

METHODOLOGYMETHODOLOGY

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JUTE SIGNATURESJUTE SIGNATURES

-27

-24

-21

-18

-15

-12

-9

-6

-3

0

3

6

0 1 2 3 4Data Acquisition Number

bac

ksca

tter

(d

B)

jute

fallow

river

urban

other veg

FCC and classified image of 3-date Radarsat Wide 2 SAR data (May 3, May 27 and June 20, 2008) showing Jute growing areas ((YellowYellow))

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CONCLUSIONCONCLUSION

• Lack of cloud free data during the rice season necessitated the development of methodology to adopt SAR data for crop monitoring

• Operational crop acreage estimation currently being done for rice and jute crop using multi temporal SAR data

• Acquisition plan has been taken care of depending on each state’s crop calendar

• Stable signature banks have been developed for these crops

• Databases are regularly updated for the program

• Yield estimations done using in-season weather data

•Three forecasts are given pre-harvest for rice and two preharvest forecasts for Jute

• Our estimates match well with the DES figures released post harvest.

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