Quantifying agricultural and water management Quantifying agricultural and water management practices from RS data using GA based data practices from RS data using GA based data

assimilation techniquesassimilation techniques

HONDA KiyoshiHONDA KiyoshiAsian Institute of TechnologyAsian Institute of Technology

Mie UniversityMie University

Amor V.M. InesAmor V.M. InesTexas A&M UniversityTexas A&M University

Introduction• Agriculture

– Monitoring acreage, sowing date, growth– Monitoring impact of water availability to its impact– Optimize water use for higher yield

• Contents

– Crop Growth Dynamics observed by RS– Data Assimilation for SWAP model parameter

identification– Water use optimization– Mixed Pixel Modeling– High-Low RS Data Fusion for High Spatio -

Temporal Data– Future Plan

Fluctuation pattern of Non-irrigated rice

NDVI Fluctuation of Non-irrigated rice, Year 1999-2001

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

NDVI Time Series (10 days composite)

ND

VI

Val

ue

Peak of Rainfall Peak of Rainfall Peak of Rainfall

2000 20011999

Non-irrigated/Rainfed rice field (20 th June 2003)

Landsat TM 08 Jan 2002: False Color Composite Non-irrigated area

(Map: 604632E, 1624227N)

Monitoring IrrigationPerformance through Crop Dynamics

Fluctuation pattern of Irrigated rice 2 crops/year

(Homogeneous)

NDVI Fluctuation of Irrigated rice 2 crops, Year 1999-2001

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106NDVI Time Series (10 days composite)

ND

VI

Val

ue

Peak of Rainfall Peak of Rainfall Peak of Rainfall

2000 20011999

Irrigated rice, largecontinuous field (26 th April 2003)

Irrigated rice, large continuous field. (Map: 621930E, 1578132N)

Fluctuation pattern of Irrigated rice 3 crops/year

(Heterogeneous field)

NDVI Fluctuation of Irrigated rice 3 crops, Year 1999-2001

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

NDVI Time Series (10 days composite)

ND

VI

Val

ue

Peak of Rainfall Peak of Rainfall Peak of Rainfall

2000 20011999

Irrigate rice 3 crops per year, discontinuous/small patchy

fields (Map: 611549E, 1620653N).

Irrigate rice 3 crops per year, growing stage (20 th June 2003)

Unclassified

Non-irrigated rice

Irrigated rice; 2 crops/year

Irrigated rice; 3 crops/year

Poor irrigated rice; 1 crop/year

Others

Provincial boundary

Irrigation zone

1999

2000

2001

Number of Cultivation in a Year

Suphanburi: 5 Classes

33

2211

Non Irri.Non Irri.

. Discrimination of Irrigated and . Discrimination of Irrigated and Rainfed Rice in a Tropical Rainfed Rice in a Tropical Agricultural System using SPOT-Agricultural System using SPOT-VEGETATION NDVI and Rainfall VEGETATION NDVI and Rainfall Data: Daroonwan Kamthonkiat, Data: Daroonwan Kamthonkiat, Kiyoshi Honda, Hugh Turral, Nitin K. Kiyoshi Honda, Hugh Turral, Nitin K. Tripathi, Vilas Wuwongse: Tripathi, Vilas Wuwongse: International Journal of Remote International Journal of Remote Sensing , pp.2527-2547, Vol. 26, No. Sensing , pp.2527-2547, Vol. 26, No. 12, 20 June, 200512, 20 June, 2005

Modeling and Simulation• RS is a useful tool to monitor the situation• Limitation: Only a snap shot• Modeling the phenomena on the ground

– Quantitative prediction– Scenario Simulation / Impact assessment

• RS can provide model input / model calibration / validation

• However, not all parameter can be seen.

Soil-Water-Atmosphere-Plant Model Soil-Water-Atmosphere-Plant Model (SWAP)(SWAP)

Adopted from Van Dam et al. (1997)Drawn by Teerayut Horanont (AIT)

SWAP Model Parameter DeterminationSWAP Model Parameter Determination - Data Assimilation using RS and GA -- Data Assimilation using RS and GA -

0.00

1.00

2.00

3.00

4.00

0 45 90 135 180 225 270 315 360

Day Of Year

Eva

po

tra

nsp

iratio

n L

AI

RS ObservationRS Observation

SWAP Crop Growth ModelSWAP Crop Growth Model

SWAP Input ParametersSWAP Input Parameters

sowing date, soil property, sowing date, soil property, Water management, and etc.Water management, and etc.

LAI, LAI, EvapotranspirationEvapotranspiration

0.00

1.00

2.00

3.00

4.00

0 45 90 135 180 225 270 315 360

Day Of Year

E

avpo

tran

spira

tion

LA

I

FittingFitting

LAI, LAI, EvapotranspirationEvapotranspiration

Assimilation by Assimilation by finding Optimized finding Optimized

parametersparameters

By GABy GA

RSRS ModelModel

ETa ( Evapotranspiration actuaul)ETa ( Evapotranspiration actuaul)

in Bata Minor, Kaithal, Haryana, Indiain Bata Minor, Kaithal, Haryana, India

ETa, ETa, mmmm

ETa, mmETa, mm

m m

February 4, 2001February 4, 2001 March 8, 2001March 8, 2001

2.90

2.48

2.06

1.64

1.22

0.80

4.20

3.44

2.68

1.92

1.16

0.40

Results from SEBAL AnalysisResults from SEBAL Analysis

GA solution to the regional inverse modelingGA solution to the regional inverse modeling

February 4, 2001February 4, 2001 March 8, 2001March 8, 2001

0

10

20

30

40

50

60

<=1.9 1.9-2.1 2.1-2.3 2.3-2.5 2.5-2.7 >2.7

ETa, mm

Rel.

frequ

ency

, % SEBAL

SWAPGA

0

10

20

30

40

50

60

<=2.9 2.9-3.1 3.1-3.3 3.3-3.5 3.5-3.7 3.7-3.9 >3.9

ETa, mm

Rel.

frequ

ency

, %

SEBAL

SWAPGAExtended SWAP

0.00

0.20

0.40

0.60

0.80

1.00

300 330 360 390 420 450 480

Day Of Year

Mag

nit

ud

e (-

)

Tact/Tpot ETact/ETpot

Water Stress Indicator ( Actual / Potential )Water Stress Indicator ( Actual / Potential )

EmergenceEmergence

HarvestHarvest

3000

4000

5000

6000

7000

100 200 300 400 500 600 700

Water Supply, mm

Yie

ld,

kg h

a-1 Expected Yield

-SD

+SD

WatProdGA optimum solutions to WatProdGA optimum solutions to the water management problemthe water management problem

Before Before OptimizationOptimization

Optimization of water useOptimization of water use

Field photos

• Longitude: 100.008133

• Latitude: 14.388195

LAI Data Collection From the FieldLAI Data Collection From the Field

LAI from Field Measurement

0

24

68

10

9/12/04

19/12/04

29/12/04

8/1/05

18/1/05

28/1/05

7/2/05

17/2/05

Date

LA

I

LAI Week1

LAI Week2

LAI Week3

LAI Week4

LAI Week5

LAI Week6

LAI Week7

LAI and NDVI Own CorrelationLAI and NDVI Own Correlation

NDVI VS LAI From Field Measurement

0

2

4

6

8

10

-1.00 -0.50 0.00 0.50 1.00

NDVI

LA

I

Observed

EstimatedLAI

LAIeNDVI 0.44599128.15.2

R2 = 0.8886

Result (2)Comparison of Satellite LAI and Simulated LAI

0

1

2

3

4

5

0 30 60 90 120 150 180 210 240 270 300 330 360

DOY

LA

I

LAI_sat

LAI_sim

• Estimated parameters

• DOYCrop1 = 19

• DOYCrop2 = 188

• Crop.Int.Crop2 = 0.32

• Fitness = 4.537

• Generation found = 31 (popsize=5)

• Calculation time approximate 15 minutes

1km grid on ASTER 2002

1km

1 1 kmkm

1 1 k

mkm

a1: Rainfed 1 crop/yr

a2: Irrigated2 crops/yr

a3: Irrigated3 crops/yr

0.1321 aaa

ai : proportion of each agriculture pattern

i: Agricultural Pattern

sdi,j: sowing date

j: sowing count

1 crop/yr : sd1,1

2 crops/yr : sd2,1 , sd2,2

3 crops/yr : sd3,1, sd3,2, sd3,3

Mixed Pixel Modeling –1 Mixture of 3 patterns

1 crop/yr ( rainfed ), 2 crops/yr, 3 crops/yr

Base Pattern values 1 2 3 4 5 Mean S.D. (a)

sd1 141 138 127 142 135 153 139.00 9.57 sd2 32 32 34 31 23 34 30.80 4.55 sd3 186 182 185 189 190 180 185.20 4.32 sd4 1 3 2 1 16 3 5.00 6.20 sd5 121 118 126 108 126 126 120.80 7.95 sd6 248 248 248 250 249 246 248.20 1.48 a1 0.15 0.14 0.19 0.22 0.22 0.06 0.17 0.07 a2 0.50 0.52 0.47 0.48 0.43 0.51 0.48 0.04 a3 0.35 0.34 0.34 0.3 0.35 0.43 0.35 0.05

Fitness - 4.73 4.37 3.49 2.57 3.21 3.67 0.88 Error, mm d-1 - 0.21 0.23 0.29 0.39 0.31 0.29 0.07

Par

amet

ers

valu

es

ET data averaged at 10 days (ET10daveET data averaged at 10 days (ET10daveEE): at 10% level of error ): at 10% level of error

Data Fusion in Data Assimilation

Data Fusion

ObtainingHigh-Resolution Multi-temporal

DataETa, LAI

Implementation in Cluster Computer

Serial and Distributed Pixel: Time Curve(Optima Cluster: 1 Master, 5 Slaves, 5 populations,10 generation)

0

200

400

600

800

1000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

No of Pixel

Tim

e (s

ec)

Cluster (Optima): Time Curve

Serial: Time Curve

1CPU

5 Slave CPU

100x100 pixels will takes 7 months(30 min. * 100 * 100) -> Parallel computing

Mr. Shamim AkhtarMr. Shamim Akhtar

Future Development Future Development

• Expand the modeling from a few pixels to regional scale.Expand the modeling from a few pixels to regional scale.• Field Survey SupportField Survey Support

• Difficulty on field level calibration and validationDifficulty on field level calibration and validation• Field ServerField Server

• Soil MoistureSoil Moisture• Sowing and HarvestingSowing and Harvesting• R/C Flying MonitoringR/C Flying Monitoring

• Develop a flowDevelop a flow• local observationlocal observation• satellite observationsatellite observation• data collection/fusiondata collection/fusion• modeling & simulationmodeling & simulation• feed back to decision making feed back to decision making •( farmers to regional - national )( farmers to regional - national )

Develop a flow from monitoring, modeling, simulation and feed back to decision makings

Field photos

• Longitude: 100.008133

• Latitude: 14.388195

LAI Measurement

Thank you very much.www.rsgis.ait.ac.th/~honda