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Use of RS in Quantifying Irrigation Water Management Based on Data Assimilation Technique

Use of RS in Quantifying Irrigation Water Management Based on Data Assimilation Technique. HONDA Kiyoshi, Amor V.M. Ines Asian Institute of Technology (AIT) Bangkok, Thailand. Background. Agriculture Activity Monitoring Sowing date, cropping intensity, Water stress, Yield and etc.

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Use of RS in Quantifying Irrigation Water Management Based on Data Assimilation Technique

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  1. Use of RS in Quantifying Irrigation Water Management Based on Data Assimilation Technique HONDA Kiyoshi, Amor V.M. Ines Asian Institute of Technology (AIT) Bangkok, Thailand

  2. Background • 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

  3. Objectives • To develop a Data Assimilation Technique for a Crop Model ( SWAP ) using RS • Low Resolution RS Data under Mixed Pixel Environment. • A numerical experiments to test the performance of the algorithm • Estimate area fractions of 3 types of agriculture pattern in a pixel ( 1, 2, 3 crops / year ) • Estimate agriculture practices ( sowing dates, water management ) • High Resolution RS Data - Stochastic Approach

  4. Contents • SWAP Model • Data Assimilation on SWAP and RS • Genetic Algorithm (GA) • Use of Low Resolution RS • Mixed Pixel Modeling for SWAP-GA-RS • Generation of RS data from SWAP • Result - Evaluation the performance of the algorithm • Use of High Resolution RS • Stochastic Approach

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

  6. 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

  7. Fitness (Evaluation) A1 B5 B1 Selection Reproduction Crossover Mating Pool A5 B5 B1 Mutation . : Genetic Algorithm in a nutshell Variable1 Variable2 A1 B1 : (t+1) Population (t) . An Bn    A3

  8. Time 1.1 x 1.1 km2 Low resolution RS; Mixed-pixel 1 crop/yr ( Rainfed ), 2 & 3 Crops/yr( Irrigated ) Observed Mixed Pixel Reality ET ET LAI LAI Depending on the proportions of rainfed1, irrigated2 and irrigated3 in the 1.1 km x 1.1 km pixel

  9. 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

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

  11. Implementation to GA Modified Penalty Approach (20) (21) Penalties: GA (22) (23) (24) (26) (25) The chromosome (p) consisted only of 8 genes because a1+a2+a3 = 1.0

  12. Generation of RS simulated data for the Numerical Experiment • Assumption of Fixed environmental data • Rain fall, Solar Radiation, Temperature, Vapor, and etc. • Assumption of base data (area fraction and sowing dates) • Calculate ET & LAI using SWAP • Generation of 10 days composite RS Data • pick up 1 day at 10 days interval • Average of 10 days data • Noise Simulation • No-Noise • With Noise ( 10 % )

  13. Results In case of Data without Noise GA solutions to the mixed-pixel problem (dynamic linkage). Time=1.8 hrs Note: sdk,j in Day Of Year (DOY) where sdi,j = sowing date; i = 1 (rainfed), 2 (irrigated with two-croppings), 3 (irrigated with three cropping); j = 1 (first sowing), 2 (second sowing),3 (third sowing); ai = is the area fraction of k.; 10d = aggregated from every 10 days;10dave = average every 10 days. †population = 10 and 5, respectively; prob.crossover=0.5; prob.mutation (creep)=0.5; seed=-1000;no. of generation=150. ‡ population = 5 for both cases; genetic parameters same as ET. DOY 1 = Jan 1 DOY 365 = Dec 31 (non-leap year) Link

  14. Generated ET data with 10% random noise 10 days average 10 days interval

  15. ET data every 10 days (ET10dE): at 10% level of error Dynamic Linkage ET data averaged at 10 days (ET10daveE): at 10% level of error

  16. Estimation of Irrigation Scheduling parameter ( Tact / Tpot ) irrigation irrigation 1.00 Water Management Indicator Tact/Tpot= 0.72 0.80 0.60 Tact/Tpot (-) 0.40 Harvest 0.20 0.00 300 330 360 25 55 85 115 Emergence Day Of Year

  17. GA solutions to the mixed-pixel problem with water management ( without noise ) Note: w2 = is the water management variable (defined as the irrigation scheduling criteria, Ta/Tp) for i2. w3= is the water management variable (defined as the irrigation scheduling criteria, Ta/Tp) for i3.

  18. Using high spatial resolution RS data In India

  19. 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 in Bata Minor, Kaithal, Haryana, India Results from SEBAL Analysis

  20. [x, y] Irrigation dates, depths Spatial distribution yield t+2t t+t ETa water balance Extended SWAP RS model SEBAL By Genetic Algorithm water productivity . . . t t+2t … t+t t+nt Past Time The future Using high spatial resolution RS data Stochastic parameter estimation technique

  21. GA solution to the regional inverse modeling Distribution of ETa from RS and SWAP RS SWAP February 4, 2001

  22. Measured and estimated agro-hydrological variables. Note: Irrigation (mm); Yield (kg ha-1); PW (kg m-3); Ir, ETa and Ta (mm); cv is coefficient of variation.

  23. Conclusion • A methodology for Data Assimilation to identify SWAP parameters under mixed pixel environment using low resolution RS was established. • Numerical experiment showed promising result. • With ET & LAI data without noise; good matching of the estimated parameters and base value. • The ET data with 10 % noise; successful estimation, which is 1-7% for area fractions, and, 1-10days for sowing date error. • Water Management parameter ( irrigation scheduling criteria) was successfully estimated. • Use of high resolution RS with stochastic approach showed good estimation of agricultural parameters including a water management parameter.

  24. Future Work • Testing of the algorithm using actual data. • SPOT Vegetation in Suphamburi, Thailand • Pure pixel environment • Parameter for Data Assimilation ET or LAI • Selection – De-selection of parameters for data assimilation • Pure Pixel -> Un-mixing parameters • Water Management parameter • Data Fusion of High and Low Resolution RS Data • Improvement of Calculation Speed • Look up Table Method • Implementation on Cluster – Grid Computers.

  25. Thank you very much http://www.star.ait.ac.th/~honda

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