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Quantifying Agricultural and Water Management Practices from RS Data Using GA-Based Data Assimilation Techniques

This research focuses on quantifying agricultural and water management practices through remote sensing (RS) data assimilation techniques. It explores the use of genetic algorithms (GA) to optimize water use for higher crop yields. The study also discusses crop growth dynamics observed through RS and the implementation of the SWAP model for parameter identification.

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Quantifying Agricultural and Water Management Practices from RS Data Using GA-Based Data Assimilation Techniques

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  1. Quantifying agricultural and water management practices from RS data using GA based data assimilation techniques HONDA Kiyoshi Asian Institute of Technology Mie University Amor V.M. Ines Texas A&M University

  2. 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

  3. Monitoring Irrigation Performance through Crop Dynamics Fluctuation pattern of Non-irrigated rice Landsat TM 08 Jan 2002: False Color Composite Non-irrigated area (Map: 604632E, 1624227N) Non-irrigated/Rainfed rice field (20 th June 2003)

  4. Fluctuation pattern of Irrigated rice 2 crops/year (Homogeneous) Irrigated rice, large continuous field. (Map: 621930E, 1578132N) Irrigated rice, large continuous field (26 th April 2003)

  5. Fluctuation pattern of Irrigated rice 3 crops/year (Heterogeneous field) 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)

  6. Number of Cultivation in a YearSuphanburi: 5 Classes 2000 1999 Unclassified Non-irrigated rice Poor irrigated rice; 1 crop/year Irrigated rice; 2 crops/year 2001 Irrigated rice; 3 crops/year Others Provincial boundary Irrigation zone . Discrimination of Irrigated and Rainfed Rice in a Tropical Agricultural System using SPOT-VEGETATION NDVI and Rainfall Data: Daroonwan Kamthonkiat, Kiyoshi Honda, Hugh Turral, Nitin K. Tripathi, Vilas Wuwongse: International Journal of Remote Sensing , pp.2527-2547, Vol. 26, No. 12, 20 June, 2005 Non Irri. 3 1 2

  7. 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.

  8. Soil-Water-Atmosphere-Plant Model (SWAP) Adopted from Van Dam et al. (1997) Drawn by Teerayut Horanont (AIT)

  9. SWAP Model Parameter Determination - Data Assimilation using RS and GA - SWAP Input Parameters sowing date, soil property, Water management, and etc. RS Observation SWAP Crop Growth Model LAI, Evapotranspiration LAI, Evapotranspiration 4 . 00 4 . 00 Fitting 3 . 00 3 . 00 Assimilation by finding Optimized parameters By GA Eavpotranspiration LAI 2 . 00 2 . 00 Evapotranspiration LAI 1 . 00 1 . 00 0 . 00 0 . 00 0 45 90 135 180 225 270 315 360 0 45 90 135 180 225 270 315 360 Day Of Year Day Of Year RS Model

  10. February 4, 2001 March 8, 2001 ETa, mm ETa, mm 2.90 2.48 2.06 1.64 1.22 4.20 m m 0.80 3.44 2.68 1.92 1.16 0.40 ETa ( Evapotranspiration actuaul) in Bata Minor, Kaithal, Haryana, India Results from SEBAL Analysis

  11. GA solution to the regional inverse modeling February 4, 2001 March 8, 2001

  12. Water Stress Indicator ( Actual / Potential ) Harvest Emergence

  13. Optimization of water use 7000 +SD 6000 Expected -1 Yield Yield, kg ha 5000 -SD 4000 Before Optimization 3000 100 200 300 400 500 600 700 Water Supply, mm WatProdGA optimum solutions to the water management problem

  14. Field photos • Longitude: 100.008133 • Latitude: 14.388195

  15. LAI Data Collection From the Field

  16. LAI and NDVI Own Correlation R2 = 0.8886

  17. Result (2) • Estimated parameters • DOYCrop1 = 19 • DOYCrop2 = 188 • Crop.Int.Crop2 = 0.32 • Fitness = 4.537 • Generation found = 31 (popsize=5) • Calculation time approximate 15 minutes

  18. 1km grid on ASTER 2002 1km

  19. Mixed Pixel Modeling –1 Mixture of 3 patterns 1 crop/yr ( rainfed ), 2 crops/yr, 3 crops/yr ai: proportion of each agriculture pattern i: Agricultural Pattern a1: Rainfed 1 crop/yr 1 km a2: Irrigated 2 crops/yr a3: Irrigated 3 crops/yr 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 1 km

  20. ET data averaged at 10 days (ET10daveE): at 10% level of error

  21. Data Fusion in Data Assimilation

  22. Data FusionObtainingHigh-Resolution Multi-temporalDataETa, LAI

  23. Implementation in Cluster Computer 100x100 pixels will takes 7 months (30 min. * 100 * 100) -> Parallel computing 1CPU 5 Slave CPU Mr. Shamim Akhtar

  24. Future Development • Expand the modeling from a few pixels to regional scale. • Field Survey Support • Difficulty on field level calibration and validation • Field Server • Soil Moisture • Sowing and Harvesting • R/C Flying Monitoring • Develop a flow • local observation • satellite observation • data collection/fusion • modeling & simulation • feed back to decision making • ( farmers to regional - national )

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

  26. Field photos Thank you very much.www.rsgis.ait.ac.th/~honda • Longitude: 100.008133 • Latitude: 14.388195 LAI Measurement

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