Presentation on theme: "SEBAL Expert Training Presented by The University of Idaho and"— Presentation transcript:
1 SEBAL Expert Training Presented by The University of Idaho and The Idaho Department of Water ResourcesAug , 2002Idaho State UniversityPocatello, ID
2 The Trainers Richard G. Allen, University of Idaho, Kimberly Research StationWim M. BastiaanssenWaterWatch, Wageningen, The NetherlandsRalf Waters
3 SEBAL Surface Energy Balance Algorithm for Land Developed by Dr. Wim Bastiaanssen, International Institute for Aerospace Survey and Earth Sciences, The Netherlandsapplied in a wide range of international settingsbrought to the U.S. by Univ. Idaho in 2000 in cooperation with Idaho Department of Water Resources and NASA/Raytheon
4 Why Satellites? Typical method for ET: Satellite imagery: weather data are gathered from fixed points -- assumed to extrapolate over large areas“crop coefficients” assume “well-watered” situation (impacts of stress are difficult to quantify)Satellite imagery:energy balance is applied at each “pixel” to map spatial variationareas where water shortage reduces ET are identifiedlittle or no ground data are requiredvalid for natural vegetation
5 Definition of Remote Sensing: The art and science of acquiring information using a non-contact device
6 SEBAL UI/IDWR Modifications digital elevation models for radiation balances in mountains (using slope / aspect / sun angle)ET at known points tied to alfalfa reference using weather data from Agrimettesting with lysimeter (ET) datafrom Bear River basin (during 2000)from USDA-ARS at Kimberly (during 2001)
7 How SEBAL Works SEBAL keys off: reflectance of light energy vegetation indicessurface temperaturerelative variation in surface temperaturegeneral wind speed (from ground station)
8 Satellite Compatibility SEBAL needs both short wave and thermal bandsSEBAL can use images from:NASA-Landsat (30 m, each 8 or 16 days) - since 1982NOAA-AVHRR (advanced very high resolution radiometer) (1 km, daily) - since 1980’sNASA-MODIS (moderate resolution imaging spectroradiometer) (500 m, daily) - since 1999NASA-ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) (15 m, 8 days) - since 1999
9 Image Processing ERDAS Imagine used to process Landsat images SEBAL equations programmed and edited in Model Maker function20 functions / steps run per image
10 What Landsat Sees Land Surface Wavelength in Microns Landsat Band 6 is the long-wave “thermal” band and is used for surface temperature
11 What We Can See With SEBAL Evapotranspiration at time of overpassOakley Fan, Idaho, July 7, 1989
12 Uses of ET MapsExtension / Verification of Pumping or Diversion RecordsRecharge to the Snake Plain AquiferFeedback to Producers regarding crop health and impacts of irrigation uniformity and adequacy
13 Why Use SEBAL?ET via Satellite using SEBAL can provide dependable (i.e. accurate) informationET can be determined remotelyET can be determined over large spatial scalesET can be aggregated over space and time
14 Future Applications ET from natural systems ET from cities wetlands rangelandforests/mountainsuse scintillometers and eddy correlation to improve elevation-impacted algorithms in SEBALhazardous waste sitesET from citieschanges in ET as land use changes
19 ET is calculated as a “residual” of the energy balance Energy Balance for ETET is calculated as a “residual” of the energy balanceET = R - G - HnRGHETBasic Truth: Evaporation consumes EnergyThe energy balance includes all major sources (Rn) and consumers (ET, G, H) of energy
27 Obtaining Header File Information Get the following from the header file:Overpass date and timeLatitude and Longitude of image centerSun elevation angle (b) at overpass timeGain and bias ofr each and (Landsat 7 only)
28 Applicable for these satellites and formats: Method AApplicable for these satellites and formats:Landsat 5 if original image in NLAPS formatLandsat 7 ETM+ if original image is NLAPS or FAST
42 Calculating the Wind Speed for the Time of the Image For August 22, 2000: image time is 17:57 GMTApply the correction:timage (Local Time) = 17:57 – 7:00 = 10:57 amΔt = 1t1 = int 10+57/60 + ½ - 0 (1) + 1 = 12 hours1
43 Estimate Wind Speed at 10:57 am Interpolate between the value for 12:00 (1.4 m/s) and the value for 13:00 (1.9 m/s)U = 1.4+( )[(10+57/60) – (10+1/2)] = 1.63 m/sTo estimate ETr for 10:57 AM:Interpolate between the values for 12:00 (.59) and for 13:00 (.72)ETr = .59+( ) [(10+57/60) – (10+1/2)] = 0.65 mm/hr
71 Surface TemperatureSystematic errors that largely self-cancel in SEBAL:1) Atmospheric transmissivity losses are not accounted for.2) Thermal radiation from the atmosphere is not accounted for.Fortunately, in SEBAL, the use of a “floating” air-surface temperature function and the anchoring of ET at well-watered and dry pixels usually eliminates the need to applyatmospheric correction.
79 Selection of “Anchor Pixels” The SEBAL process utilizes two “anchor” pixels to fix boundary conditions for the energy balance.“Cold” pixel: a wet, well-irrigated crop surface with full cover Ts Tair“Hot” pixel: a dry, bare agricultural field ET 0
80 Incoming Longwave Radiation (RL) RL↓ = ea × σ × Ta4a = atmospheric emissivity= 0.85 × (-ln tsw).09 for southern IdahoTa Tcold at the “cold” pixelRL↓ = 0.85 × (-ln tsw).09 × σ × Tcold4For August 22, 2000: tsw = 0.774, Tcold = K, RL↓ = W/m2
88 Soil Heat Flux Image and Histogram Light – high GDark – low G
89 G/Rn for Various Surfaces Surface Type G/RnDeep, Clear Water 0.5SnowDesert – 0.4Agriculture – 0.15Bare soil – 0.4Full cover alfalfa 0.04Clipped Grass 0.1Rock – 0.6These values represent daytime conditions
90 Sensible Heat Flux (H) H rah dT H = (r × cp × dT) / rah dT = the near surface temperature difference (K).rah = the aerodynamic resistance to heat transport (s/m).Hrahz2dTz1
91 Friction Velocity (u*) ux is wind speed (m/s) at height zx above ground.zom is the momentum roughness length (m).zom can be calculated in many ways:For agricultural areas: zom = 0.12 height of vegetation (h)From a land-use mapAs a function of NDVI and surface albedo
92 Zero Plane Displacement (d) and Momentum Roughness Length (zom) The wind speed goes to zero at the height (d + zom).
93 Calculations for the Weather Station For August 22, 2000:zx = 2.0 m, ux = 1.63 m/s,h = 0.3 m, zom = 0.120.3 = .036 mu* = m/su200 = 3.49 m/s
95 Friction Velocity (u*) for Each Pixel u200 is assumed to be constant for all pixelszom for each pixel is found from a land-use mapFor agricultural fields, zom = 0.12hFor our area, h = 0.15LAIzom = × LAI
97 Setting the Size of the Land-use Map Insert coordinates from LAI image
98 Aerodynamic Resistance to Heat Transport (rah) for Each Pixel z1 height above zero-plane displacement height (d) of crop canopyz1 0.1 mz2 below height of surface boundary layerz2 2.0 m
99 Model_13 – Friction Velocity and Aerodynamic Resistance to Heat Transport
100 Near Surface Temperature Difference (dT) To compute the sensible heat flux (H), define near surface temperature difference (dT) for each pixeldT = Ts – TaTa is unknownSEBAL assumes a linear relationship between Ts and dT:dT = b + aTs
101 How SEBAL is “Trained”SEBAL is “trained” for an image by fixing dT at the 2 “anchor” pixels:At the “cold” pixel: Hcold = Rn – G - lETcoldwhere lETcold = 1.05 × ETrdTcold = Hcold × rah / (r × cp)At the “hot” pixel: Hhot = Rn – G - lEThotwhere lEThot = 0dThot = Hhot × rah / (r × cp)
102 How SEBAL is “Trained”Once Ts and dT are computed for the “anchor” pixels,the relationship dT = b + aTs can be defined.
103 Graph of dT vs TsCorrelation coefficients a and b are computed
104 Sensible Heat Flux (H)dT for each pixel is computed using: dT = b + aTsH = (r × cp × dT) / rah
107 Stability Correction for u*and rah New values for dT are computed for the “anchor” pixels.New values for a and b are computed.A corrected value for H is computed.The stability correction is repeated until H stabilizes.
113 Seasonal Evapotranspiration (ETseasonal) Assume ETrF computed for time of image is constant for entire period represented by image.Assume ET for entire area of interest changes in proportion to change in ETr at weather station.
114 Seasonal Evapotranspiration (ETseasonal) Step 1: Decide the length of the seasonStep 2: Determine period represented by each satellite imageStep 3: Compute the cumulative ETr for period represented by image.Step 4: Compute the cumulative ET for each period(n = length of period in days)Step 5: Compute the seasonal ETETseasonal = ETperiod
115 ET - July-Oct., mm Montpelier, 1985 Validation of SEBALET - July-Oct., mm Montpelier, 1985Lysimeter 388 mmSEBAL 405 mm
116 ET - April-Sept., mm - Kimberly, 1989 Validation of SEBALET - April-Sept., mm - Kimberly, 1989Sugar BeetsLysimeter 718 mmSEBAL 714 mm
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