Amor V.M. Ines and Kyoshi Honda

1. Data Assimilation using Remote Sensing and Genetic Algorithms: Applications to Agriculture and Water Management Amor V.M. Ines and Kyoshi Honda Space Technology Applications and Research, School of Advanced Technologies Asian Institute of Technology (AIT), Bangkok, Thailand

2. Introduction Regional studies in agricultural and water management require a large amount of spatial and temporal information. Remote sensing (RS) and geographic information system (GIS) have been used to account for these spatial information. Temporal integration with RS is quite difficult and expensive especially with high spatial resolution data. This gap can be bridged upon by the integration of RS/GIS with physically based simulation models, which is now becoming a robust approach in water management studies. But a major problem is how to derive these spatial and temporal data for the simulation models, which are in general, non-observable (neither we can see nor observe directly) from RS data, e.g. soil hydraulic properties and other transport parameters, sowing dates, water management practices etc.

3. Objectives In this seminar, we aim to present a methodology to quantify crop and water management practices and other system properties that are needed in regional agricultural and water management studies from remote sensing (RS) using low spatial remote sensing data (LSRD) and high spatial resolution remote sensing data (HSRD). The LSRD methodology is demonstrated here by using a synthesized “RS data” for a hypothetical pixel where themixed pixel problem in agricultureis addressed. The issue of computational efficiency and how it has been improved is also discussed. A methodology for high spatial resolution RS data (HSRD) will be also presented. This is based on stochastic approach because pixel by pixel analysis is deemed impossible for practical applications. For HSRD methodology, results of a case study in an irrigation system in Kaithal, Haryana, India will be discussed.

4. MODEL of a SYSTEM RESULTS MODEL Output Time

5. EXTERNAL CONSTRAINTS Need to characterize and understand these complexities We can explore options in water management System and Heterogeneity INPUT WEATHER Management practices (water, crop mgt…) Physical properties (soil, water quality, GW depth…) OUTPUT IRRIGATION SYSTEM Yield, water balance, water productivities…

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

7. Data Assimilation using RS and Genetic Algorithms 4 . 00 4 . 00 3 . 00 3 . 00 2 . 00 2 . 00 ET / 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 Set of Parameters Water management, sowing date, soil property and etc. RS Observed - SWAP Physical Model - e.g SEBAL Analysis Fitting ET / LAI Assimilation by finding set of parameters By Genetic Algorithms Day Of Year Day Of Year RS Modeling

8. eaten eaten Just married eaten eaten eaten down to hole Genetic Algorithm in Action low IQ high jumpers high IQ low jumper low IQ low jumpers gluttons

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

10. Part 1. Using low spatial resolution RS data

11. A typical mixed-pixel problem in agriculture Time … - Time series RS data - Sub-pixel information 1.1 x 1.1 km2 Depending on the proportions of rainfed1, irrigated2 and irrigated3 in the 1.1 km x 1.1 km pixel

12. Un-mixing parameters A simple mixed-pixel model Assuming that: (1) (2) (3) (4) Objective Function: Simulation model RS data (5) Where: (6)

13. A simple mixed-pixel model Subject to these constraints: Possible range of sowing dates: Constraints to avoid cropping overlaps: (7) (13) (8) (14) (9) (15) Proportions of cropping area: (10) (16) (11) Possible range of cropping area: (12) (17) (18) (19)

14. Modified Penalty Approach (20) (21) Penalties: GA (22) (23) (24) (26) (25) The chromosomes consisted only of 8 genes because A3 can be expressed in terms of A1 and A2 i.e. A3=|1-A1-A2| hence the length of string is reduced. The chromosome (p) then for this problem is defined as:

15. Solution Approaches A. Dynamic Linkage Approach B. Look up table Approach

16. Dynamic Linkage

17. Look up table

18. Example of a GA solution to the mixed-pixel problem

19. GA solutions to the mixed-pixel problem (dynamic linkage). Time=1.8 hrs Note: sdk,j in Day Of Year (DOY) where sd = sowing date; k = r (rainfed), i2 (irrigated with two-croppings), i3 (irrigated with three croppings); j = 1 (first sowing), 2 (second sowing),3 (third sowing); ak = is the area fraction of k.; 10d = aggregated every 10 days;10dave = moving 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

20. Random noise in the data: at 10% level

21. ET data every 10 days (ET10dE): at 10% level of error Dynamic Linkage 10-day moving average ET (ET10daveE): at 10% level of error

22. Look up tables

23. GA solutions to the mixed-pixel problem (look up table). Time = 30 min LUT prep. + 0.70 sec (GA solution)

24. With water management

25. Water Stress Indicator 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

26. GA solutions to the mixed-pixel problem with water management (dynamic linkage) 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.

27. Part 2. Using high spatial resolution RS data

28. [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 Stochastic parameter estimation technique

29. Case Study: Bata Minor, Kaithal, Haryana, India

30. 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

31. Cropped area delineation Cropped area Cropped area February 4, 2001 March 8, 2001

32. Fitness Function Water management variables (Irrigation scheduling criterion) Depth to groundwater k = {μα, σα, μn, σn, μTa/Tp, σTa/Tp, μeDate, σeDate, μgw, σgw, μwq, σwq} crop management variables (Emergence dates - represent the sowing dates) Water quality (Salinity) Soil hydraulic properties (Mualem-Van Genuchten parameters)

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

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

35. Data Collected IM_GA Sandy Clay Loam Validation Emergence dates: Mean: Nov 23; SD: 8 days Soil water retention curve

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

37. 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.

38. Conclusions Data assimilation using RS is very promising in agriculture and water management studies. It has been known that LRSD can provide a vast of spatial information especially if the sub-pixel information contained in the large pixel can be abstracted. This mixed-pixel problem is a usual bottleneck in using LSRD for agricultural and water management studies. Based on our numerical experiments, solution of the mixed-pixel problem in agriculture is highly possible using the proposed approach for LSRD. The methodology developed for HSRD proves to be useful in understanding the processes of a hydrologic system as a whole. Processes that are hard to measure in the field can be analyzed using simulation models. Overall, GA is powerful in both HSRD and LSRD data assimilation.

39. Future Works Application of the LSRD methodology using actual data

40. THANK YOU FOR YOUR ATTENTION avmines@ait.ac.th honda@ait.ac.th http://www.star.ait.ac.th http://www.ait.ac.th

41. Un-mixing Mixing Generated Numerical Data

42. A E B C D LSRD 1 km 1 km A typical mixed pixel in LSRD

43. Surface Energy Balance Algorithm for Land (SEBAL) Adopted from WaterWatch Drawn by Teerayut Horanont (AIT)

44. Surface Energy Balance Algorithm for Land (SEBAL) Rn = K – K + L – L Energy Balance Equations Rn = H + G + λET Rn – Net radiation H – Sensible heat flux G – Soil heat flux λET – Latent heat flux K – incoming and outgoing shortwave radiation L – incoming and outgoing longwave radiation Units in W m-2

45. Extended SWAP model μi, σi Etc… Spatial representation of the system