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

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

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. 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 Classes3 21 Non Irri.. 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

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. Data Assimilation for Crop Modeling using RS Agriculture Activity Monitoring –Sowing date, cropping intensity, Water stress, Yield and etc. –Production for Food Security –Water Management in Irrigation Activity Crop growth model ( SWAP ) –Continuous monitoring in various aspects –Prediction –Spatial parameter estimation & calibration ->RS Data Assimilation Technique –To estimate parameters which cannot be observed by RS –High-Resolution Remote Sensing – A.V.M.Ines et. al ( 2003 ) –Temporal Info -> Low-Moderate Resolution Remote Sensing –Mixed Pixel

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

10. SWAP Model Parameter Determination - Data Assimilation using RS and GA - - Data Assimilation using RS and GA - 0.00 1. 2. 3. 4. 04590135180225270315360 Day Of Year Evapotranspiration LAI RS Observation SWAP Crop Growth Model SWAP Input Parameters sowing date, soil property, Water management, and etc. LAI, Evapotranspiration 0.00 1. 2. 3. 4. 04590135180225270315360 Day Of Year Eavpotranspiration LAI Fitting LAI, Evapotranspiration Assimilation by finding Optimized parameters By GA RS Model

11. . Genetic Algorithm in a nutshell A1A1A1A1 B1B1B1B1 Reproduction Selection Crossover Mutation AnAnAnAn BnBnBnBn : Population (t) Fitness (Measure) A1A1A1A1 B1B1B1B1 A5A5A5A5 B5B5B5B5 :. A3A3A3A3 (t+1) Variable1Variable2 B1B1B1B1 B5B5B5B5 Mating Pool  

12. ETa ( Evapotranspiration actuaul) in Bata Minor, Kaithal, Haryana, India ETa, mm ETa, mm ETa, mm m m February 4, 2001 March 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 Analysis

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

14. System characteristics derived by GA * Mualem-Van Genuchten (MVG) parameters. ** Sowing dates were represented by emergence dates in Extended SWAP. *** Irrigation scheduling criterion, T a /T p **** Average value of surface water and groundwater, surface water has good quality for irrigation + Assumed spatially distributed but not significantly distributed in time Table 4. Derived System characteristics from IM_GA

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

16. 3000 4000 5000 6000 7000 100200300400500600700 Water Supply, mm Yield, kg ha Expected Yield -SD +SD WatProdGA optimum solutions to the water management problem Before Optimization Optimization of water use

17. Irrigation depths: Measured: 180-485 mm Predicted: 125-572 mm Table 5. Average groundwater salinity levels in Bata Minor Groundwater depths

18. Field photos Longitude: 100.008133 Latitude: 14.388195

19. LAI Data Collection From the Field

20. LAI and NDVI Own Correlation R 2 = 0.8886

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

22. 1km grid on ASTER 2002 1km

23. a 1 : Rainfed 1 crop/yr a 2 : Irrigated 2 crops/yr a 3 : Irrigated 3 crops/yr a i : proportion of each agriculture pattern i: Agricultural Pattern sd i,j : sowing date j: sowing count 1 crop/yr: sd 1,1 2 crops/yr: sd 2,1, sd 2,2 3 crops/yr: sd 3,1, sd 3,2, sd 3,3 Mixed Pixel Modeling –1 Mixture of 3 patterns 1 crop/yr ( rainfed ), 2 crops/yr, 3 crops/yr

24. Mixed-pixel model - 2 In case Evapotranspiration (ET) for the Assimilation (1) (2) (3) (4) Rainfed Irrigated 2crops/yr Irrigated 3crops/yr Simulated ET for mixed pixel

25. Objective Function: Where: (5) (6) SWAP model RS data 9 Un-mixing parameters Objective Function

26. ET data averaged at 10 days (ET10dave E ): at 10% level of error

27. Data Fusion in Data Assimilation

28. Water management => RS ETa at large area Water management => RS ETa at field level Problems: –Low spatial resolution satellite every day –High spatial resolution satellite not every day How to get high spatial resolution of ETa, while having it at short time interval? Data Fusion in the evaluation function

29. Data Fusion Obtaining High-Resolution Multi-temporal Data ETa, LAI

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

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

32. Ubiquitous Geo-informatics Geo-informatics supports our life from global, local to personal phase. Car Navigation ITs Man Navi More LBS Web GIS

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

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