Post on 23-Jan-2016
description
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