“chap04”—2004/1/20 — page 133 — #1 Chapter 4 Estimating spatially distributed surface fluxes in a semi-arid Great Basin desert using Landsat TM thermal data Charles A. Laymon and Dale A. Quattrochi 4.1 Introduction Ground-based measurements of hydrologic and micrometeorologic processes are now available for many parts of the world, especially for the United States and Europe, on a nearly routine basis. These measurements, however, are only representative of a very small area around the sensors, and, therefore, provide little information about regional hydrology. The variability of the land surface precludes using these measurements to make inferences about processes that occur over an area of a hectare, much less the size of an entire valley. Recent developments have demonstrated an increasing capability to estimate the spatial distribution of hydrologic surface fluxes for very large areas with remote sensing techniques. A number of studies have focused on the use of remote sensing to measure surface water and energy variables in attempts to derive latent heat flux or evapotranspiration (ET) over semi- arid regions (e.g. Kustas et al. 1989a,b, 1990, 1994a,b,d, 1995; Humes et al. 1994, 1995; Moran et al. 1994; Ottlé and Vidal-madjar 1994; Tueller 1994). All of these investigations have used aircraft-based instruments and were lim- ited to small areas. In only a few investigations has satellite-based remote sensing data been used to estimate ET. The synoptic and real-time attributes of remote sensing data from satellites offer the potential for measuring land- scape, hydrometeorological, and surface energy flux characteristics that can be used in both monitoring and modeling the state and dynamics of semi-arid regions. Choudhury (1991) reviewed the current state of progress in utilizing satellite-based remote sensing data to estimate various surface energy bal- ance parameters. Kustas et al. (1994c) used Advanced Very High Resolution Radiometer (AVHRR) data to extrapolate ET estimates from one location containing near-surface meteorological data to other areas in a semi-arid basin in Arizona. Moran et al. (1989) and Moran and Jackson (1991) used Landsat Thematic Mapper (TM) data to estimate ET over a small agricul- tural area. In this paper, we present a method for scaling from point to spatial estimates of instantaneous surface fluxes for a Great Basin desert valley using Landsat TM data and for characterizing the partitioning of fluxes among the different soil and landcover types found in the study area. “chap04”—2004/1/20 — page 134 — #2 134 Charles A. Laymon and Dale A. Quattrochi A field study was conducted from May 1993 through October 1994 to improve our understanding of the processes that govern the local energy and water fluxes in a Great Basin desert ecosystem. A survey of soils and vegeta- tion was conducted for the study area. Six surface water and energy balance flux stations were deployed in major plant ecosystems. These stations oper- ated nearly continuously throughout the study period, except for several of the winter months. Field work was conducted during special observ- ing periods at the peak “green-up” in the early summer of 1993 and 1994 and at “dry-down” during late summer of 1993. These periods included deployment of several eddy correlation systems, soil moisture measurements using the neutron probe and time domain reflectometry techniques, and radiosonde observations of the lower atmosphere. This research program provided an infrastructure to further study the use of remote sensing to measure surface properties and processes. 4.2 Setting The study was conducted in Goshute Valley of northeastern Nevada, a faulted graben valley of the Basin and Range Province of the western United States about 50 km west of the Great Salt Lake Desert (Figure 4.1). Although the entire valley is about 75 km long and 16 km wide, our study was ID NV 1–80 1–80 Great Goshute Valley Salt Lake City Great Salt Lake Salt Lake Desert UT Figure 4.1 Map showing the location of Goshute Valley (40 ◦ 44  N, 114 ◦ 26  W) in northeastern Nevada in relation to state boundaries and Great Salt Lake Desert, Utah. “chap04”—2004/1/20 — page 135 — #3 Estimating spatially distributed surface fluxes 135 Figure 4.2 Landsat-5 TM image of the Goshute Valley, Nevada, study area showing the types and location of surface water and energy balance flux stations (BR = bowen ratio, EC = eddy correlation).The box defines the area over which energy balance components were derived and cooresponds to the restricted to a 40 km long central section (Figure 4.2). The valley floor, with an elevation of about 1700 m asl, is nearly flat with slopes of less than a few degrees. The valley is bordered by alluvial fans emanating from the mountains. A pluvial lake occupied the valley during the Late Pleistocene leaving strand lines and terraces on the alluvial fans and allowing for lacus- trine silt and clay to accumulate in the valley. Because outflow drainage was limited, dissolved weathering products from the surrounding moun- tains became concentrated in the lake producing significant amounts of soluble salts and carbonates in the lacustrine sediments. As a result, salt content and pH of the lacustrine soils in the central reaches of Goshute Valley are high. Vegetation of the valley is dominated by shrubs with some understory forbs and grasses. Land within the valley has not been heavily grazed or developed, although small portions of the valley have been chained for grazing and are easily identified by the regular geometric patterns in Figure 4.2. area shown in Figure 4.9(a)–(d). “chap04”—2004/1/20 — page 136 — #4 136 Charles A. Laymon and Dale A. Quattrochi 4.3 Methods 4.3.1 Approach The general surface energy balance can be summarized as: R n = H + LE + G (4.1) where R n is net radiation absorbed at the surface, G the flux of heat into the soil, and H and LE are the sensible and latent heat fluxes into the atmo- sphere. We use the sign convention that all the radiative fluxes directed toward the surface are positive, while other (non-radiative) energy fluxes directed away from the surface are positive and vice versa. LE, a prod- uct of the rate of evaporation E and the latent heat of vaporization L,is the rate of energy utilization in ET and is often treated as a proxy for ET. R n , G, and H can be estimated from micrometeorological measurements, or in some cases, using remote sensing techniques exclusively (Jackson et al. 1985; Clothier et al. 1986). The remote sensing techniques, however, usually require assumptions about surface conditions that are best measured on the ground. Remote sensing reflectance and emittance data used in conjunction with surface meteorological data can be used to estimate parameters needed to characterize R n , G, and H, leaving LE to be defined mathematically. Our approach is to establish a one-to-one relationship between surface radiation and energy fluxes measured at points on the ground to correspond- ing reflectance and emittance values of a geolocated remote sensing image. The empirical relationships are then used to extrapolate from the point mea- surements to spatial estimates of surface fluxes. Our procedure is based on a Landsat-5 TM image of June 19, 1994. This date closely follows field observations that occurred between June 7 and June 14, 1994. Five surface energy balance flux stations were installed in Goshute Valley most northerly and southerly stations were separated by 35 km. The stations were installed in different assemblages of dominant vegetation types present within the valley or in assemblages of vegetation with different plant density. only four stations. Measurements were made every 5 s and then output as 20-min averages. Malek et al. (1997) and Malek and Bingham (1997) have discussed the annual radiation and energy balance from these stations. The Bowen ratio method used to measure the surface energy balance in this experiment requires fetch. On the basis of instrument height and the wind speed measured during the hour that the TM scene was acquired, we assume that flux measurements are representative of an area within a 100 m radius of each Bowen ratio station. The image was geometrically corrected to within one pixel of the true location. Thus, the station data were related in May, 1993, and a sixth station was added in June, 1994 (Figure 4.2). The Each station contained the same instrument configuration (Figure 4.3 and Table 4.1), with the exception that infrared thermometers were located at “chap04”—2004/1/20 — page 137 — #5 Figure 4.3 Photo of a surface water and energy balance flux station deployed in Goshute Valley during the experiment. The letters correspond to the instrument descriptions in Table 4.1. Table 4.1 Instrument configuration at the surface energy balance stations Variable a Instrument Deployment Vendor a. Air temperature Thermocouple 1 and 2 m above sfc Campbell Scientific b. Dew point temperature Cooled mirror hygrometer 1 and 2 m above sfc General Eastern Corp. c. Relative humidity RH Sensor 2 m above sfc Campbell Scientific d. Wind speed/direction Anemometer/Vane 10 m above sfc RM Young e. Rainfall Tipping bucket 6 m above sfc Texas Electronics f. Net radiation Net radiometer 4 m above sfc REBS Fritschen g. Downwelling solar radiation Pyranometer 4 m above sfc LI-COR h. Reflected solar radiation Pyranometer 4 m above sfc Epply Lab i. Surface temperature IR Thermometer 4 m above sfc Everest InterScience j. Soil temperature Temperature probe 2 and 6 cm below sfc at three locations Campbell Scientific k. Ground heat flux Heat flux plate 2 and 8 cm below sfc at three locations Campbell Scientific Note a Letters correspond to the letters in Figure 4.3. “chap04”—2004/1/20 — page 138 — #6 138 Charles A. Laymon and Dale A. Quattrochi to the mean remote sensing reflectivity values of an area corresponding to 7 × 7 pixels (∼200 m ×∼200 m) centered over each station. 4.3.2 Geometric correction A full Landsat-5 TM scene covering the Goshute Valley was obtained from the Earth Observation Satellite (EOSAT) Corp. for June 19, 1994 (09:39 h local standard time). The image was a system-corrected, orbit-oriented prod- uct (type “P” data). Using 16 ground control points defined with Global Positioning System (GPS) instruments, the image was more precisely recti- fied with a first-order Affine transformation with no resampling yielding a standard error of 4.6 m. Visual inspection of the control points in relation to the image revealed they were all within one pixel (<28.5 m) of the correct location. The data were not topographically corrected as the valley floor is essentially flat. 4.3.3 Radiometric correction of the reflected bands Before remote sensors can measure components of the surface energy bud- get, the recorded digital values must be converted to measures of at-satellite radiance and then to surface reflectivity. Digital values in each band are converted to at-satellite spectral radiance (Chevez 1989) and then to appar- ent at-satellite reflectance after normalizing for the effects of variations in incident solar irradiation (Nicodemus et al. 1977; Markham and Barker 1986, 1987a,b; Hill and Sturm 1991; Markham et al. 1992; Gilabert et al. 1994). After accounting for viewing geometry, atmospheric scattering, and transmission losses, surface reflectance ρ(λ) (unitless) is defined as ρ(λ) = π(L 0 (λ) − L p (λ))d 2 T(λ)↑ E g (λ) cos θ 0 (4.2) where L 0 (λ) is the apparent at-satellite spectral radiance in band λ, and L p (λ) is the atmospheric path radiance resulting from scattering, d is the Earth–Sun distance (Sturm 1981), T(λ)↑ is the direct beam transmittance of the atmosphere in the upward direction, E g (λ) is the global solar irradiance at the surface, and θ 0 is the solar zenith angle. Thus, surface reflectance can be determined with estimates of L p (λ), T(λ)↑, and E g (λ). Atmospheric path radiance is the sum of Rayleigh and aerosol (Mie) scattering (Gordon 1978): L p (λ) = L r (λ) + L a (λ) (4.3) The Rayleigh scattering contribution, L r (λ), is all but constant in the atmo- sphere, as it is based on the solar zenith and sensor view angles, and thus, “chap04”—2004/1/20 — page 139 — #7 Estimating spatially distributed surface fluxes 139 can be determined from image header information (Saunders 1990). Gilabert et al. (1994) developed a procedure that integrates the dark object sub- traction and atmospheric transmission modeling techniques to estimate the aerosol scattering contribution to path radiance, L a (λ), on observed sur- face reflectances. The method consists of an inversion algorithm based on a simplified radiative transfer model in which characteristics of atmospheric aerosols are estimated from the observed radiance in TM bands 1 and 3. This is in contrast to many other procedures in which the characteristics of aerosols are measured or estimated a priori. The technique has the advantage over other methods in that it is based entirely on information derived from the image. The path radiance in TM bands 1 and 3 determined from dark objects in the image are used to define the aerosol spectral properties at the time the image was acquired. With this model, the parameters necessary to solve equation (4.2) can be determined from any Landsat-5 TM image that contains some dark pixels. The only information needed to apply this model is the mean elevation of the imaged terrain, the day of year the image was acquired, the solar zenith angle, and the dark object digital values for TM bands 1 and 3. The sun elevation reported in the header of each Landsat-5 TM image is used to determine the solar zenith angle at the time of image acquisition. The definition of digital values for dark pixels in the image is the most critical step in the entire procedure and should be done with great care. Dark object digital values were defined for spectral minima associated with water and shadows within the scene, but outside the study area. 4.3.4 Estimation of energy balance components Net radiation The net radiation flux in equation (4.1) can be written as R n = (1 − α)R s ↓+R l ↓−ε s σ T 4 s (4.4) where α is the surface albedo, R s ↓ is incoming shortwave radiation or irra- diance, R l ↓ is incoming longwave radiation, ε s is surface emissivity, σ is the Stefan–Boltzmann constant, and T s is the surface temperature. The actual amount of insolation received at the ground may be considerably smaller than at the top of the atmosphere because of scattering, absorption, and turbidity of the atmosphere. It is, therefore, usually measured in the field and assumed to be spatially invariant over the study domain. R l ↓ emanates largely from the atmosphere and is spatially homogeneous relative to the land surface. Although R l ↓ has been estimated using measurements of near- surface air temperature and relative humidity (Brutsaert 1975; Humes et al. 1994), direct observations from the flux stations were used in this study. “chap04”—2004/1/20 — page 140 — #8 140 Charles A. Laymon and Dale A. Quattrochi Thus, net radiation was determined with field measurements of the down- welling radiation fluxes, R s ↓, and R l ↓, and remote sensing measurements of α, ε s , and T s . Albedo Albedo is the ratio of upwelling shortwave radiation to solar irradiance. For our purpose, solar irradiance at the land surface can be estimated satisfactorily using a radiative transfer model with parameters derived from atmospheric soundings. Solar irradiance at the surface in Goshute Valley was modeled for the day of the satellite overpass using the SPECTRL radiative transfer model (Justus and Paris 1985, 1987) and sounding data obtained from the National Weather Service at Ely, Nevada (0Z, June 20, 1994 = 17:00 h PST, June 19, 1994), about 140 km to the south-southwest. Shortwave radiometers on today’s satellites detect radiation in discrete bandwidths, not over the total solar spectrum (∼0.3–4.0 µm). These narrow band samples of the solar spectrum must be extrapolated over the entire spectrum to estimate broadband albedo. The technique used here follows that of Brest and Goward (1987) and Starks et al. (1991) in which broadband albedo is the reflectance in multiple bands integrated over the total solar spectrum. Each band is weighted according to the ratio of radiance sampled to the total radiance for an extended bandwidth associated with each band. Thus, broadband albedo, α BB , is (Starks et al. 1991) α BB = π 6  λ=1 (ρ(λ))(W(λ)) (4.5) where ρ(λ) is the reflectance in TM band λ, and W(λ), the weighting coefficient, is W(λ) =  U(λ) L(λ) E(λ) dλ   4.0 0.3 E(λ) dλ (4.6) where E(λ) is the solar irradiance in band λ and U(λ) and L(λ) are the upper and lower wavelengths of each TM bandpass, respectively. An assumption that the surface responds as a Lambertian reflector is necessary because the remote sensing instrument is nadir viewing. Generalized reflectance curves were developed for vegetation, soil, bedrock, and water using data from the National Aeronautics and Space Administration (NASA). These curves were used in conjunction with the modeled solar irradiance curve to define the extended bandwidths for each reflected TM band based on inflection points. Thus, spatially distributed broadband albedo was computed for the study “chap04”—2004/1/20 — page 141 — #9 Estimatingspatiallydistributedsurfacefluxes141 areaby α BB =π[(0.111ρ(1))+(0.119ρ(2))+(0.078ρ(3))+(0.124ρ(4)) +(0.041ρ(5))+(0.019ρ(7))](4.7) Emissivity justbeforedawnusingtheproceduredescribedbyHipps(1989).Themea- surementsincludedapparenttemperaturewithaninfraredthermometer, temperatureofthetargetcoveredbyanaluminumcone,andapparentand actualtemperaturesofanaluminumplateofknownemissivity.Emissiv- itywasdeterminedforbaresoil(0.92–0.93),andthedominantvegetation species:greasewood(0.94–0.95),shadscale(0.95),andsagebrush(0.98). Thepercentageofgroundsurfacecoveredbyvegetationateachfluxstation andtheproportionoftotalvegetationrepresentedbydifferentspecieswas determinedusingthepointquadratmethod(Groeneveld1997).Basedon thesedata,thearea-weightedmeanemissivitywasdeterminedforeachflux station.Becauseemissivitywasgenerallyhigherforvegetationthanbare soil,spatiallydistributedemissivitywasestimatedfromtheNormalized DifferenceVegetationIndex(NDVI).Therewasinsufficientvariabilityin emissivityamongfluxsitestodefinethenatureoftherelationshipuntil emissivityatendpointNDVIvaluesof0.1and1.0forbaresoilandcom- pletevegetationcoverage,respectively,wereincluded.Theresultinglinear relationshipisdefinedby ε s =0.022NDVI+0.928(4.8) Asthisrelationshipisbasedontheobservedemissivityforspecificplant species,itisappropriateonlyforthestudysiteandsimilarsettings.More observationsofemissivityoverabroaderrangeofNDVIvaluesarerequired tomorepreciselydefinetheε s toNDVIrelationship(cf.LabedandStoll 1991). Surface temperature Longwave radiation is emitted from the surface in proportion to its temper- ature as described by Planck’s law. Using pre-launch calibration constants for Landsat-5 TM band 6, surface temperature T s (λ) is determined by (Markham and Barker 1986) T s (λ) = C 2 ln((C 1 /L s (λ)) + 1) (4.9) where C 1 and C 2 are the calibration constants equal to 60.776 mW cm −2 ster −1 µm −1 Emissivity was measured in the field at two sites (BR1, EC1; Figure 4.2) and 1260.56 K, respectively (see also Goodin 1995). Surface “chap04”—2004/1/20 — page 142 — #10 142CharlesA.LaymonandDaleA.Quattrochi radiation,L s (λ),canbeexpressedintermsoftheobservedradiation,L 0 (λ), as(SchottandVolchok1985) L s (λ)= L 0 (λ)−τ(1−ε s )L d (λ)−L p (λ) τε s (4.10) whereL 0 (λ)istheapparentat-satellitespectralradianceinbandλ,L d (λ)is thedownwellinglongwaveradiationreachingthesurface,L p (λ)istheatmo- sphericpathradiance,ε s isthesurfaceemissivity,andτistheatmospheric transmissivity. Forsensorswithmorethanonethermalchannel,varioussplit-window algorithmshavebeendevelopedforatmosphericcorrection.Onlyonether- malchannelontheTMsensorpreventsuseofthesealgorithms.Instead, variousalternativemethodshavebeendevelopedthatusesoundingdata andradiativetransfermodelstocharacterizetheatmosphere(cf.Vidaletal. 1994). Atmospheric transmissivity and downwelling and path radiance at the TM thermal waveband were calculated using the radiative transfer model SPECTRL and atmospheric sounding data from Ely, Nevada (described previously). The model was run for the TM-6 bandwidth with no surface reflectance to determine path radiance, and again, with surface reflectance (albedo) consistent with field measurements to determine downwelling radi- ance and atmospheric transmissivity. These values were assumed to be constant in space throughout the study area and were applied to calculate surface temperature for each image pixel. Soil heat flux The surface temperature at a given location is controlled by the surface energy balance, which, in turn, depends on the radiation balance and veg- etation cover among other factors. Thus, the soil heat conduction flux can be estimated as a fraction of the net radiation (Clothier et al. 1986). Based on this theory, several investigations have attempted to define soil heat flux as a function of net radiation and reflectivity in the red and near-infrared wave bands (Reginato et al. 1985; Clothier et al. 1986; Jackson et al. 1987; Kustas and Daughtry 1990). Soil heat flux at a depth of 8 cm (G 8cm ) was measured directly at each of the surface energy flux stations (Malek et al. 1997). G 8cm was converted to surface heat flux (G sfc ) (Hanks and Ashcroft 1980; Oke 1987; Malek 1994) using the following relationship (Malek et al. 1997) (n = 518, r = 0.96): G sfc = 1.615G 8cm (4.11) This is an obvious soil-specific realtionship and its validity here without modification is unknown. Because of the strong relationship between soil heat flux, net radiation, and vegetation cover, G sfc can be defined on the [...]... spatially distributed surface fluxes 149 Surface temperature (°C) 50 IR thermometer Remote sensing 45 40 35 30 25 20 1 2 3 4 Flux station 5 6 Figure 4. 7 Comparison of surface temperatures measured at the flux stations using a tower-mounted infrared thermometer and those obtained with remote sensing et al 1988; Vining and Blad 1992; Kohsiek et al 1993; Stewart et al 19 94) Without further information, no correction... greasewood–rabbit brush Pine Gsfc “chap 04 — 20 04/ 1/20 — page 151 — #19 H LE 152 Charles A Laymon and Dale A Quattrochi Rn derived from remote sensing 580 560 540 520 500 48 0 46 0 44 0 44 0 46 0 48 0 500 520 540 560 Rn measured at flux stations 580 Figure 4. 10 Comparison of net radiation measured directly at the flux stations and net radiation derived from remote sensing estimates of emissivity, surface temperature,... Calculation of basin-scale surface fluxes by combining remotely-sensed data and atmospheric properties in a semiarid landscape Bound.-Layer Meteorol 73: 105– 24 “chap 04 — 20 04/ 1/20 — page 157 — #25 158 Charles A Laymon and Dale A Quattrochi Labed, J and M.P Stoll (1991) Spatial variability of land surface emissivity in the thermal infrared band: spectral signature and effective surface temperature Remote Sens... remote sensing, In S.B Monsen and S.G Kitchen (eds) Proceedings – Ecology and Management of Annual Rangelands USDA Forest Service, Intermountain Research Station, General Technical Report INT-GTR-313, USDA Forest Service, Intermountain Research Station, Ogden, UT, pp 126–31 Vidal, A., C Devaux-Ros, and M.S Moran (19 94) Atmospheric correction of Landsat TM thermal band using surface energy balance In. .. grassland with multiresolution sensors J Geophys Res 100: 25397–2 541 0 Gilabert, M.A., C Conese, and F Maselli (19 94) An atmospheric correction method for the automatic retrieval of surface reflectances from TM images Int J Remote Sens 15: 2065–86 Goodin, D.G (1995) Mapping the surface radiation budget and net radiation in a Sand Hills wetland using a combined modeling /remote sensing method and Landsat... corrections in a shrub-steppe ecosystem Remote Sens Environ 27: 337 42 Högström, U (1988) Non-dimensional wind and temperature profiles in the atmospheric surface layer: a re-evaluation Bound.-Layer Meteorol 42 : 55–78 Humes, K.S., W.P Kustas, and M.S Moran (19 94) Use of remote sensing and reference site measurements to estimate instantaneous surface energy balance components over a semiarid rangeland watershed... regional scales using optical remote sensing from an aircraft platform and atmospheric data collected over semiarid rangelands Water Resour Res 30: 1 241 –59 Kustas, W.P., E.M Perry, P.C Doraiswamy, and M.S Moran (1994c) Using satellite remote sensing to extrapolate evaportranspiration estimates in time and space over a semiarid rangeland basin Remote Sens Environ 49 : 275–86 Kustas, W.P., R.T Pinker, T.J Schmugge,... demonstrated an increasing capability to estimate the spatial distribution of hydrologic surface fluxes for very large areas utilizing remote sensing data The Goshute Valley research program provided the necessary instrumentation to further study the use of remote sensing for measuring surface properties and processes, and to relate these processes to the geomorphological setting of the Great Basin The distribution... estimate instantaneous regional-scale ET using Landsat TM data Well-developed pointbased models of surface energy and water balance fluxes were applied to individual pixels of the remotely sensed image The method requires certain assumptions be made about the spatial distribution of several physical parameters In some instances, remotely sensed proxies were used; in “chap 04 — 20 04/ 1/20 — page 1 54 — #22... Moran, R.J Reginato, R.D Jackson, L.W Gay, and H.L Weaver (1989a) Determination of sensible heat flux over sparse canopy using thermal infrared data Agric For Meteorol 44 : 197–216 Kustas, W.P., R.D Jackson, and G Asrar (1989b) Estimating surface energy-balance components from remotely sensed data In G Asrar (ed.) Theory and Applications of Optical Remote Sensing John Wiley, New York, 743 pp Kustas, . −79 44 7 85 7 Pine 1 661 −52 332 1 64 “chap 04 —20 04/ 1/20 — page 152 — #20 152 Charles A. Laymon and Dale A. Quattrochi R n measured at flux stations R n derived from remote sensing 44 0 46 0 48 0 500 520 540 560 580 44 0. “chap 04 —20 04/ 1/20 — page 133 — #1 Chapter 4 Estimating spatially distributed surface fluxes in a semi-arid Great Basin desert using Landsat TM thermal data Charles A. Laymon. has satellite-based remote sensing data been used to estimate ET. The synoptic and real-time attributes of remote sensing data from satellites offer the potential for measuring land- scape, hydrometeorological,