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  • Layer of drains on top of the map on left.
  • What is the average elevation of ground in PVID? Get the Jan 2009. The drains are big and deep. Found an image of a drain. Convert units into SI.
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    • 1. Geospatial Technologies for Mapping andMonitoring Irrigated Agricultural Areas Christopher M. U. Neale ProfessorDept. of Civil and Environmental Engineering Irrigation Engineering Division Utah State University
    • 2. Outline of the PresentationDescribe an international project: thedevelopment of a digital cadastre of irrigationwater users in the Dominican RepublicAirborne remote sensing of agricultural andnatural resourcesEstimation of Crop Evapotranspiration andYield with Remote Sensing
    • 3. Estudios Básicos Para El Manejo de Los Sistemas de RiegoPrograma de Administración de Sistemas de Riego por los Usuarios (PROMASIR) Préstamo BID 905/OC-DRInstituto Nacional de Recursos Hidráulicos INDRHI Dominican Republic
    • 4. Basic Studies for Management of Irrigation Systems• Contract between INDRHI and (Utah State University) funded by the Inter-American Development Bank• $7.9 million dollars• Duration: 4 ½ years• Comprised 4 studies:1. Aerial Photography of the Entire Country2. Cadastre of Water Users (Padrón de Usuarios)3. Hydro-Agricultural Information System4. Monitoring of Salinity and Drainage ProblemsThese studies had the objective of providing basic information required for the transfer of Operation and Maintenance of 30 canal-based irrigation systems from the government control (INDRHI) to newly formed Irrigation Water User Associations
    • 5. Purpose of the studies:• In the Dominican Republic water is still distributed in fixed amounts and charged according to area served and not based on volume. Therefore a good estimate of irrigated area for each property is needed for proper assessment of system operation and maintenance fees.• An up-to-date cadastre of irrigators is necessary in the transfer of publicly operated system to private irrigator cooperatives so that appropriate Irrigation Systesm Operation and Maintenance charges can be estimated• Problems with salinity needed to be identified prior to the transfer in order to be included in the civil works and mitigation plans
    • 6. Final Results of the Aerial Photography Campaign99.6 % of the Country covered with color photographs at 1:20000 scale
    • 7. Remote Sensing based ProductsColor aerial photographs at 1:20,000 scale of the entirecountry in digital formatColor contact prints of the aerial photography at 1:20,000Color and Black and White diapositives at 1:20,000Digital orthophotoquads at 1:5000 for the irrigated areas ofthe country: (4400 Km2)Contour curves at 1-meter intervals, of the irrigated areas(ortofotos)High resolution multispectral image mosaics of theirrigated areasDigital Cadastre of Irrigation Water Users and InformationDatabase for all the irrigation systems of the country
    • 8. Digital Aerial Photograph at 1:20,000 scale The Basic Product Resulting from the scanning of the negative with software producing a color digital positive
    • 9. Contact Prints at 1:20,000 Printed on photographic quality paper
    • 10. Diapositive of the original photo:Used in analytical stereoplotters for the production of orthophotos and updating of topographic maps Diapositives are transparent photos (like a slide)
    • 11. Surveying of Ground Control Points used in theaerial triangulation and production of orthophotos High Precision Dual frequency GPS systems were used to obtain the ground control points for mapping
    • 12. Installation of GPS base station network to support aerial photography and ground control activitiesData from the base stations were used to correct the positions of themobile units based on differential correction techniques, resulting in highpositional accuracy. The network of GPS base stations installed was similarto the CORS network in the US.
    • 13. Location of the GPS Base Station in the DRSan Cristóbal, Barahona, San Juan, Villa Vázquez, La Vega, Nagua, Higuey
    • 14. Digital Orthophoto with Contour Lines of the irrigated areas República Dominicana
    • 15. Project Design before this Project: Traditional design drawing and cartography
    • 16. After the development of the digital products, planning for irrigation of new systems was done digitally, such as the Azua II Irrigation Project Digital Orthophoto Mosaic with Contour Curves at 1-meter intervals
    • 17. Detail of a Digital Orthophoto with Contours
    • 18. Digital Orthophotoscovering 4430 Km2 of irrigated areas in the country
    • 19. What is a Digital Cadastre of Irrigators?It is a database containing information on all the property owners and irrigation water users within an irrigation project or system. It consists of: An up-to-date property boundary map containing information on every property owner or water user Information on total area and irrigated area of each property Information on the canal system that delivers water to the property=> Because of the geographic and distributed nature of the information as well as the requirement to have an associated database, USU opted in using Geographic Information System (GIS) technology to develop this product.
    • 20. Digital Orthophoto at 1:5000 scaleThe map base for the cadastre of irrigation water users The orthophoto is a digital photograph with geographic coordinates that has been adjusted to the terrain so that areas can be correctly measured
    • 21. Printed Orthophoto at 1:4000 scale were used for field verification of property boundaries Field brigades used printed and laminated maps to identify the property boundaries together with the land owner or a local facilitator that has a good knowledge of the irrigation system (president of a water association, ditch rider) A brigade consisted of 4 to 6 trained cartographers with one 4x4 dual cabin pickup and 3 or 4 cross- country motorbikes
    • 22. Survey Form used in Information Gathering by the Cartographers The cartographers conduct the survey with the property owner to obtain basic information on the water user (complete name, nickname, address) and on the property (delivery canal, water distribution problems, crops planted, salinity or drainage problems) Information was digitized using an Access DB application to automate the data entry. This work was done in the Dominican Republic at a Lab in USU’s local office.
    • 23. On-screen digitizing of the parcel boundaries in ArcInfo using the digital ortho as a backdropMost of the digitizing wasdone at USU usingbattalions of graduate andundergraduate studenttechnicians. Also themerging with surveydatabase and QualityControl Observation: We were very good clients of FEDEX!
    • 24. Some characteristics of the digital cadastre of irrigation water users• Union of the geographic layer containing the property boundaries with the information in the surveys• The geographic coordinates come from the digital orthophotos• The property area is exact• The database is complete because all properties within the command area of a main canal or irrigation system are included• Can be readily and easily updated• The property boundaries can be updated if a consolidation or division of properties occurs
    • 25. The Water Users Database Prior to the Estudios Basicos ProjectEssentially a listing of users => Rapidly became obsolete, areas were not correct, water users were repeated, owners of multiple properties sometimes were not registered and did not pay for the water. Some irrigation systems had property boundary maps but were static in time.
    • 26. Delivered ProductsWater Users Database and Property Boundary Layers Study Goals Goals Accomplished Cadastre maps for Cadastre maps for 81,000 properties over 84,500 properties over 4400 Km2 of 4460 Km2 of orthophotos of orthophotos (net) of irrigated areas irrigated areas Irrigator database for Irrigator database for all the irrigation 278 irrigation systems systems within 4400 over 5400 Km2 of Km2 of orthophotos orthophotos (gross) of irrigated areas
    • 27. The Hydro-Agricultural Information System wascreated to allow easy access to the Cadastre of Water Users Database and to Manage the Information Software developed in MapObjects and Visual Basic Allowed the search of irrigators by first and last name, nickname etc. Visualize the irrigation control structures for maintenance purposes GIS Layers: Cadastre (property boundary), canal and drain system, hydraulic control structures, crop and land use layer, soil layers Allowed for the updating of the crop agricultural statistics Estimates the irrigation water demand through ET calculations on a canal command area level and also by lateral and entire project Product installed at the Water Users Associations and Irrigation Districts
    • 28. Example: Search for a Water User or Irrigator
    • 29. Digital Database
    • 30. Information can be easily updated
    • 31. Information on irrigation infrastructure for Marcos A. Cabral System
    • 32. Irrigation Water Control Structures
    • 33. Canal Distribution GIS Layer
    • 34. Before Estudios Basicos Project: Agricultural Statistics Compiled Manually, with no map base
    • 35. Presently: Crop Layer can be Easily Updated
    • 36. Calculation of Agricultural Statistics
    • 37. Airborne Multispectral Mosaics of the Irrigated Areas Acquired with the USU digital airborne multispectral system Used in the study of salinity and drainage problems of the irrigation areas of the country Used to obtain the crop cover and land use for the hydro-agricultural database system Maps of areas with salinity impacts and drainage problems
    • 38. Multispectral Mosaic ofThe Mao-Gurabo Irrigation System
    • 39. Multispectral Mosaic of the Manzanillo Area used for Salinity Impact studies Soil sampling sites Results were verified with intensive soil sampling
    • 40. Training Activities:Creation of the Geomatics Laboratory at INDRHI • Repository of the printed and digital products developed though the project • Care and protect the information in a climatized, safe environment • Use the products and technical staff of the laboratory to support the different Departments of INDRHI • Maintain and update the water users cadastre database by supporting the water users associations • Development of a methodology for the expansion of the available digital cadastre for the entire country
    • 41. Present Situation• A modern geomatics lab with state-of-the-art hardware and software and well-trained technical staff serving the institution
    • 42. Training StrategyCombination of short and medium term training6 Technicians and Engineers were trained in Utah:Geographical Information Systems (GIS), Image Processing,Computer Programming (Visual Basic, Avenue),Development of the products: water users database and agro-hyrdological softwareAdditional technicians were trained in Santo Domingo byUSUSide-by-side training with direct participation in theproductionResponsibility for coordination with the Water UsersAssociationsResponsibility for future training
    • 43. Training of Water User Association Personnel and INDRHI Irrigation District Personnel Basic Windows: 4 Use of the Agro-hydrological System software: 4 Updating the Water Users Database: 4 Updating the Agricultural Statistics: 4
    • 44. Training of Personnel from theSoil and Water Division ofINDRHI on the use of theHydro-agriculturalSystem Software
    • 45. Services Offered by the Geomatics Laboratory at INDRHI Sale of contact prints of the aerial photographs to the public Support to the different Departments of INDRHI in the production of maps, geomatic products, GIS and verification of cartografic information Support to different institutions in the agricultural sector of the countryGoal => Offer the products for sale over the internet, sale of the digital products, sale of value added products to generate funds to keep the laboratory equipment updated and protect products
    • 46. Final ObservationsThe Geomatics Laboratory at INDRHI with itshighly trained technical personnel and serving as arepository for all digital and printed productssupports the different departments of INDRHI andthe general public and has the capacity to offerservices to other goverhment institutions.Became a new Department at INDRHI called theDepartment of Digital Cartography
    • 47. Final ObservationsThe cadastre was necessary information in thetransfer of the irrigation systems from governmentcontrol the Water Users AssociationsUSU facilitated the transfer of 30 irrigationsystems in the Dominican Republic, setting up thedemocratic structure, training the WUA staff onoperation and maintenance of the irrigationsystem, administration and governanceThe use of the digital cadastre of irrigators andrelated database by the transferred Water UserAssociations increased their O&M assessmentincome by up to 70% in some cases
    • 48. Airborne Multispectral RemoteSensing for Agricultural and Natural Resource Monitoring
    • 49. Remote Sensing Services Laboratory - RSSL USU Cessna TU206 Remote Sensing Aircraft USU Multispectral Digital System equipment rack with FLIR SC640 thermal infrared camera in the foreground.Detail of Multispectral Cameras
    • 50. Green RedNIR 3bands
    • 51. USU Airborne multispectral system merged with LASSI Lidar
    • 52. USU Airborne multispectral system merged with LASSI Lidar
    • 53. Details on MS Camera System – Four co-boresighted multispectral cameras • 16 megapixels each for IR, R, G, B bands • 64 megapixels total Camera Hardware • Integrated with lidar • CalibratedSample of Our Custom 16 mp System(R,G,B channels shown)
    • 54. ROW 3D Mapping System– 3D Lidar • Based on Riegl Q560 • 150,000 measurements/second (300 kHz laser) • 25 mm range accuracy at any range • 31 mm footprint @ 100 m range • Helicopter swath through Bonanza Power 60 degree swath angle Plant, Uintah Basin, Utah • Integrated with camerasHelicopter swath through Utah State University from 200 musing Riegl Q560 lidar system
    • 55. LASSI Lidar Image of Tropical Rainforest
    • 56. LIDAR classifiedpoint cloud data.
    • 57. Quality control: Check formismatch at the overlap region.
    • 58. Create ortho-rectified tiles.
    • 59. Evapotranspiration Analysis ofSaltcedar and Other Vegetation in theMojave River Floodplain, 2007 and 2010Mojave Water Agency Water Supply Management StudyPhase 1 Report
    • 60. Study Overview • Analyses included: – 2007 and 2010 classification of native and non-native vegetation – Vegetation evapotranspiration modeling – Lidar elevation map development – Groundwater mapping – Water evapotranspiration cost calculations • Results are presented as a whole and also by Mojave Water Agency Alto, Alto Saltcedar (Tamarix) Transition, Centro, and Baja subarea boundaries.
    • 61. Multispectral Ortho Imagery Block 1 and 2Ortho-rectificationusing direct geo-referencing with Lidarpoint cloud data
    • 62. Multispectral Image DetailPixel resolution: 0.35 meter (1 foot)
    • 63. Thermal infrared Imagery Temperature 20 – 30 C 30 – 35 C 35 – 40 C 40 – 45 C 34 – 50 C 50 – 55 C 55 – 60 C > 60 C
    • 64. Analysis Conducted within an Area ofInterest (AOI) Polygon
    • 65. SEBAL Model Bastiaansen et al (1995)One-layer modelsUniquely solve for H using the dT method, a linearrelationship between air temperature and surface temperatureobtained through a linear transformation generated from surfacetemperatures observed over selected “hot” and “cold” pixel in thesatellite image.Advantages:Do not require absolute calibration of the thermal imageryWell suited for irrigated areas under well watered conditionsProvides actual evapotranspiration of the cropsDisadvantages:Requires experienced operator to identify the “hot and cold” pixelsMay not work well in water limited, semi-arid natural vegetation
    • 66. Two-Source Model Normal et al (1995) Kustas et al (1999) Li et al (2005)Advantages:Well suited for modeling sparse canopies either in the agricultural ornatural vegetation context where water could be limitedHas a more diverse ecosystem area of applicationProvides actual evapotranspiration of the vegetationWorks better with higher spatial resolution thermal infrared imageryDisadvantages:Requires carefully calibrated and atmospherically corrected satellite orairborne imageryMore complex to program
    • 67. Two-Source Energy Balance Model (TSEB)Interpreting thermal remote sensing data TRAD( ) ~ fc(( )Tcc + [1-fcc( )]Ts s fc )T + [1-f( )]T (two-source approximation) Norman, Kustas et al. (1995) Treats soil/plant-atmosphere coupling differences explicitlyTAERO Accommodates off-nadir thermal sensor view angles Provides information on soil/plant fluxes and stress
    • 68. System and Component Energy Balance TRADSYSTEM RN = H + E + G Derived states = = =CANOPY RNC = HC + EC TAERO TC + + +SOIL RNS = HS + ES + G Derived fluxes TS
    • 69. SEBAL ET Results for Block 1 0–1 mm/day 1–2 mm/day 2–3.3 mm/day 3.4–7.1 mm/day 7.1–9 mm/day >9 mm/day
    • 70. Seasonal ET Estimation using ET fractions (crop coefficients) derived from remotely sensed ET Kc = ETa / ET0ETa = Actual ET from Energy Balance ModelET0 = Reference ET from CIMMIS Weather StationPhenology Dates Code Greenup Begins Peak ET Senescence Begins Senescence EndsSalt Cedar (Tamarisk) SC 3/1 5/1 9/1 11/1Mesquite MS 4/1 5/15 8/1 9/15Cottonwood CW 4/1 5/15 9/15 11/1Desert Scrub DS 3/1 4/15 7/1 8/1Decadent Vegetation VD 4/1 5/15 8/1 9/15Mesophytes MP 4/1 5/15 7/1 8/1Conifer CO 3/1 5/15 10/1 11/15Arundo AR 4/1 6/1 10/1 11/1
    • 71. Classified Lidar point clouds to obtain canopy height at 1-meter grid cells
    • 72. Block 1 Seasonal ET results for Tamarisk using both energybalance modelsThe Two-source model was selected for all estimates due to processingspeed and expediency
    • 73. Results for other blocks ondownstream side
    • 74. Potato Yield Model Development Using High Resolution airborne ImageryDifferent areas of interest are selected in the field for sampling based on rate of growth and duration of the crop
    • 75. Empirical Relationship 3 date ISAVI SAVILAI Time Time LAD => Yield Yield = f(VI) 8 date ISAVI
    • 76. Spot yield sampling location Site OI6 Field • Locations marked by flags • Four rows (10 *12 ft) • GPS readings recorded
    • 77. Spot yield sampling locations based on variability OI6 Field Jul 30, 2004 Jul 05, 2004 Aug 31, 2004 Late Emergence Early Senescence – low yield spot
    • 78. Spot yield sampling locations based on variability OI6 Field Jul 05, 2004 Jul 30, 2004 Aug 31, 2004 Early Emergence Late Senescence – high yield spot
    • 79. Spot yield sampling locations based on variability OI6 Field Aug 31, 2004 Jul 05, 2004 Jul 30, 2004 Early Emergence Early Senescence – medium yield spot
    • 80. Spot yield sampling locations based on variability OI1 Field July 08,2005 August 05,2005 August 25,2005 Late Emergence Late Senescence – medium yield spot
    • 81. Model development and Validation 2004 8.00 • model developed using 16 hand y = 16.03x - 1.427 dug samples from two center pivotsActual yield kg/sqm 7.00 R² = 0.810 6.00 2004 season (7/05, 7/30, 8/31 2004) 5.00 4.00 • SE of yield estimate of 0.41 kg/sq.m 3.00 0.300 0.350 0.400 0.450 0.500 • ISAVI significant parameter (p< 0.05) ISAVI 2005 7.00 R-square = 0.89 • 18 hand dug samples from two center Actual Yield Kg/sqm MBE = 0.07 Kg/sqm 6.00 RMSE = 0.24 pivots 2005 season (7/08, 8/04,8/25) 5.00 • Model slightly over predicted at higher 4.00 end 3.00 3.00 4.00 5.00 6.00 7.00 Estimated Yield Kg/sqm
    • 82. 3-Date ISAVI model to 2004 and 2005 whole field 2004 Whole field data 2004 Whole field data in MT 8.00 4500 Actual Production (MT) R-square = 0.89 4000 R-square = 0.95 7.00 MBE = 0.16 kg/sqm 3500 MBE = 66.47 MT Actual yield kg/sqm RMSE = 0.29 RMSE = 144.63 6.00 3000 2500 5.00 2000 1500 4.00 1000 3.00 500 500 1000 1500 2000 2500 3000 3500 4000 4500 3.00 4.00 5.00 6.00 7.00 8.00 Estimated yield kg/sqm Estimated Production (MT) 2005 Whole field data in MT 2005 Whole field data 5500 9 Actual Production (MT) R-square = 0.96 R-square = 0.81 4500 MBE = -60.59 MTActual Yield kg/sqm MBE = -0.15 kg/sqm 8 RMSE =105.98 RMSE = 0.25 3500 7 2500 6 1500 500 5 500 2500 4500 5 6 7 8 9 Estimated Yield kg/sqm Estimated Production (MT) • 2004 3-date ISAVI model applied to 2004 and 2005 whole field data • Differences in yield prediction - ISAVI model less dates • More number of images covering emergence to vine kill
    • 83. FCC Aerial Images of HF19 field (Top) and WSA09 (Bottom) showing soil and crop growthvariations during the year 2004 July 05, 2004 July 30, 2004 August 31, 2004 July 05, 2004 July 30, 2004 August 31, 2004
    • 84. HF 19 WSA 9 OI10Actual yield 2607 MT Actual yield 2637 MT Actual yield 2956 MTEstimated yield 2765 MT Estimated yield 2714 MT Estimated yield 2901 MT Yield map showing the variability in yields for field HF19, WSA9 during 2004 and OI10 during 2005 season
    • 85. Improvements to 3-date model
    • 86. Land Sat TM 5 Scene of the Study area Path/Row 40 30 Path/Row 39 31
    • 87. Layout of Cranney Farm potato fields for the year 2004
    • 88. Layout of Cranney Farm potato fields for the year 2005
    • 89. Soil Water Balance in the Study Area • The soil water balance model: SWj+1 = SWj + I +P – ETc – DP SW represents the available soil water, j and j+1 - soil water content in the current time and a succeeding time j+1. I represents the irrigation amount applied P - precipitation. ETc represents the crop evapotranspiration, and DP is the deep percolation. • Reference ET - 1982 Kimberly-Penman method Contd..
    • 90. Actual ET calculation Canopy-Based Reflectance Method • Kcrf linear transformation of SAVI corresponding to bare soil and SAVI at effective full cover with the basal crop coefficient (Kcb) values corresponding to bare soil and effective full cover (EFC). • The resulting transformation is: Kcrf = 1.085 x SAVI + 0.0504 (Jayanthi et al. 2007 ) • Basal crop coefficient method ETcrop = Kc * ETref Kc = Kcb * Ka + Ks Ka = ln(Aw + 1)/ln(101) Ks = (1-Kcb) [1-(tw/td)0.5] * fw
    • 91. Incorporating Actual ET into Yield model• Actual ET computed for all hand dug samples from 2004 season• ISAVI values extracted for all the AOIs• Multiple linear regression (MLR) • ISAVI and ET independent variable • Yield dependent variable• Significance of variables analyzed using ANOVA• MLR model applied to 2004 and 2005 satellite images and tested
    • 92. Yield model with Actual ET and ISAVI 10.00 9.00 LEES 8.00 7.00 EEES 6.00 EELS 5.00 LELS ET (mm) 4.00 3.00 2.00 1.00 0.00 75 100 125 150 175 200 225 250 275 300 DOY 8.00Actual Yield Kg/sqm 7.00 R² = 0.87 6.00 5.00 Y = 5.56 * ISAVI + 0.0359 * ET – 21.397 4.00 3.00 R2 = 0.88 2.00 620 640 660 680 700 720 ET (mm)
    • 93. 3 date ISAVI model to Satellite images 4000 R-square = 0.92Actual Production (MT) 3500 MBE = 90.51 MT RMSE = 203.74 3000 June 30, Aug 2500 01, Sep11, 2004 2000 1500 1500 2000 2500 3000 3500 4000 Estimated Production (MT) 5000 R-square = 0.61 Actual Production (MT) 4500 MBE = -150.33 MT RMSE = 247.09 4000 July 03, Aug 04, Sep05 3500 2005 3000 2500 2000 2000 2500 3000 3500 4000 4500 5000 Estimated Production (MT)
    • 94. MLR model validation using Satellite images 4000 R-square = 0.97 3500 MBE = 44.13 MTActual Prodcution (MT) RMSE = 146.20 3000 MBE 90.51 MT to 44.13 MT 2500 2000 1500 1500 2000 2500 3000 3500 4000 Estimated Prodcution (MT) 5000 R-square = 0.75 4500 MBE = -39.34 MT Actual Prodcution (MT) RMSE = 200.94 4000 MBE -150 MT to -39.34 MT 3500 3000 2500 2000 2000 2500 3000 3500 4000 4500 5000 Estimated Prodcution (MT)
    • 95. FCC images of Airborne images (Top) comparing with LandSat TM5 (Bottom) for WC4 field showing soil and crop growth variability during 2004 season July 05, 2004 July 30, 2004 August 31, 2004 June30, 2004 August 01, 2004 September 11, 2004
    • 96. Actual yield 2651 MT Actual yield 2651 MTEstimated yield 2839 MT Estimated yield 2763 MT Yield map showing the variability in yields for field WC4 using airborne images(Left) and Satellite images (Right) during 2004 season
    • 97. Water Balance of an Irrigated Area:Palo Verde Irrigation District, CA 98 Contribution from Saleh Taghvaeian
    • 98. Palo Verde Irrigation District (PVID)• Location: imperial and Riverside counties, CA.• Area: more than 500 km2.• Elevation: 67 m at South to 88 m at North.• 400 km of irrigation canals and 230 km of drains.• Predominant crops: alfalfa, cotton, grains, melons.• Alluvial soil with predominant sandy loam texture. 99
    • 99. 100
    • 100. Water balance of irrigation schemesI + P = ET + DP + RO + ΔS I: Applied irrigation water; P: Precipitation; ET: Evapotranspiration; DP: Deep percolation; RO: Surface runoff; and, ΔS: Change in soil water storage. 101
    • 101. PVIDGroundwater Dynamics 102
    • 102. Over 260 piezometers1 mile by 1 mile gridMonthly measurements 103
    • 103. Average Depth to Groundwater (m) January 2007 to December 2009 104
    • 104. Evapotranspiration 105
    • 105. Remote Sensing of Energy Balance Rn = H + G + LE Rn: Net Radiation H: Sensible Heat Flux G: Soil Heat Flux LE: Latent Heat FluxSurface Energy Balance Algorithm for Land (SEBAL) Developed by Dr. Wim Bastiaanssen, Wageningen, The Netherlands 106
    • 106. SEBALLandsat TM5imageryCIMISWeather data 107
    • 107. EToF 108
    • 108. Total volume of water consumption by PVID crops for 20 dates of Landsat overpass 109
    • 109. Precipitation 110
    • 110. 111
    • 111. SurfaceInflow & Outflow 112
    • 112. 2400 Main Canal Outfall Drain 2000 Operational SpillsDaily Average Flow Rate (cfs) 1600 1200 800 400 0 J-08 M-08 M-08 J-08 S-08 N-08 J-09 113
    • 113. ClosingWater Budget 114
    • 114. 18 18 Precip. ETa 15 Inflow 15 Outflow 12 12Depth (mm) Depth (mm) 9 9 6 6 3 3 0 0 F-08 F-09 M-08 M-08 S-08 O-08 J-09 J-08 J-08 J-08 A-08 A-08 N-08 D-08 F-08 F-09 M-08 M-08 J-08 S-08 J-08 J-08 O-08 J-09 A-08 A-08 N-08 D-08 Depth (mm) Percentage Precipitation 71 3 Surface inflow 2479 97 Σ Inputs 2550 100 Canal Spills 284 11 Drainage 998 39 Evapotranspiration 1286 50 Σ Outputs 2568 100 Σ Inputs – Σ Outputs -18 -0.7 115
    • 115. Depleted fraction (DF) - DFg = ETa / (Pg + Vd) - DFn = ETa / (Pg + Va) 1.0 DFg DFn Nilo Coelho: 0.8 DFn = 0.60 0.6 DF 0.4 PVID: DFn = 0.55 0.2 0.0 116
    • 116. Final ObservationsGeo-spatial technologies have facilitated the designand management of large irrigation systemsEstimating spatial ET from remote sensing isbecoming a common approach for irrigation watermanagement and basin water balance studies
    • 117. Thank You!