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Estimating surface fluxes over the SGP site with remotely sensed data
Phys Chem .Eurth (B), Vol. 25, No. 2, pp. 167-172,200O Published by Elsevier Science Ltd All rights reserved 1464-1909/00/$ - see front matter
Pergamon
PII: S1464-1909(99)00132-X
Estimating Surface Fluxes over the SGP Site with Remotely Sensed Data A. N. French, T. J. Schmugge and W. P. Kustas Hydrology
Lab, Building
007, BARC-WEST,
USDA/AR&
Beltsville,
MD 20705
Received 24 April 1999; revised IO August 1999; accepted I4 September 1999
cultural fields at the El Reno site (Fig. 1). Since TB is a function of surface temperature and emissivity, the contrast seen in an image represents how incoming solar radiation is partitioned. Cooler temperatures usually indicate abundant moisture and therefore the dominance of latent heat flux. Conversely, hotter temperatures indicate dryer conditions and the dominance of sensible heat flux.
Abstract. The estimation of surface energy.fluxes using remotely sensed data requires the combination of data from several sources, including land use, vegetation cover and surface temperature. Land use and vegetation cover were obtained from visible and near infrared (VNIR) data, while the state variable, surface temperature, was obtained from thermal infrared (TIR) data. An approach to combine these data with an energy balance model was studied as part of the 1997 Southern Great Plains Experiment (SGP97). Toward this end, VNIR and TIR images for 2 July 1997 were analyzed over the El Reno, Oklahoma (OK) site using data from the Thermal Infrared Multispectral Scanner (TIMS) and Thematic Mapper Simulator (TMS) airborne instruments. Intensive ground measurements constrained leaf area indices, canopy height and surface meteorological inputs required by the model. The observed brightness temperatures, when corrected for atmospheric effects using MODTRAN, nearby radiosoundings and the temperature-emissivity separation (TES) algorithm, were mostly within 1°C of ground based temperatures. The resulting surface temperatures were used in a twosource model that considers the heat flux and temperature contributions from the soil and vegetation. The heat flux predictions on average agree within 50 Wm-’ of tower-based observations.
Surface fluxes, however, are related not only to surface temperatures, but also to vegetative properties, which can significantly affect the surface-air temperature gradient. For example, a 20 meter tall pine forest can be just as cool as a well-watered alfalfa field due to differences in aerodynamic properties of these two surfaces. The problem, therefore, is to assemble observable characteristics that can distinguish heat flux variations caused by temperature gradients from those caused by changes in vegetation. Characteristics such as surface brightness temperature, vegetation indices and land use, for example, can be incorporated into models that quantify the spatial variation in heat flux. Observables used in this study are thermal infrared, visiblenear infrared aircraft data sets from July 2,1997, Landsat TM imagery from July 25, 1997, and ground flux measurements collected at the USDA-ARS, El Reno Grazing Lands Research Laboratory (35”33’N, 98”OO’W). These are part of the Southern Great Plains (SGP97) experiment conducted in Oklahoma (OK) during the summer of 1997. Further details are available from http://hydrolab.arsusda.gov/sgp97/. The thermal infrared data, acquired with the Thermal Infrared Multispectral Scanner (TIMS), contains six channels spanning 8-12 pm and an instantaneous field of view of 2.5 mrad. At the aircraft altitudes of 5.0 km, resolution of the thermal infrared data is 12 m. From the same aircraft, Thematic Mapper Simulator (TMS) visible-near infrared imagery were obtained at similar resolutions and were used to create NDVI images (Fig. 2). The land use data is 30 meter resolution imagery based upon the combination of known ground conditions and the spectral properties of Landsat TM imagery.
Published by Elsevier Science Ltd. 1
Introduction
Measuring land surface heat fluxes at regional spatial scales is important for modeling atmospheric behavior and monitoring water resources. Although there are several surfacebased methods that can accurately measure heat fluxes at point locations, it is not feasible to use a network of these systems to create spatially distributed flux maps because of the high variability of real landscapes. One can most easily recognize this variability when looking at surface brightness temperatures (TB) from thermal infrared images over agriCorrespondence to: Andrew N. French 167
168
A. N. French et al.: Estimating
Surface Fluxes over the SGP Site
25.0 Fig. 1. TIMS derived surface temperatures over the El Reno, OK agricultural sites on July 2, 1997. Cool temperatures, darker tones, occur over pastures, rangelands and the water treatment ponds on the right. Hot temperatures, lighter tones, occur over harvested winter wheat fields and bare soils.
0.30
-0.10 Fig. 2. TMS derived, normalized difference vegetation index (NDVI) over El Reno, OK sites on July 2, 1997. Green vegetation is indicated by lighter tones. Water bodies, senescent vegetation and bare soil surfaces are indicated by darker tones. Particularly note that NDVI can not distinguish between the bare soil in field ER13 from the unplowed, wheat stubble in field ERlO.
169
A. N. French et al: Estimating Surface Fluxes over the SGP Site
2
Surface Energy Balance
3
There are three main factors required to estimate land surface fluxes: - Energy inputs - Moisture conditions - Near-surface
in soil and vegetation
meteorological
conditions
Both the energy inputs and the moisture condition factors can be estimated from remotely sensed data. There has been considerable recent work addressing these issues, which are essentially questions of insolation, surface reflectance, soil moisture and amounts of vegetation. The third factor, nearsurface meteorology, can not be measured remotely and conventional surface measurements must be used. Combining these factors in a surface flux model requires energy conservation. Energy balance at the land surface, neglecting photosynthesis, is given by: R,--H-LE-G=O
(1)
where R, is the net radiation, H is the sensible heat flux, LE is the latent heat or moisture flux into the atmosphere and G is the soil heat flux. The net radiation is the sum of the incoming and outgoing short and long wave radiation fluxes: R, = (1 - CY)R,,L+ cRLJ - caT4
(2)
where cx is the surface albedo, Rsol is the incoming solar radiation, E is the surface emissivity, RLJ is the incoming long wave radiation, (Tis the Stefan-Boltzmann constant, and T is the surface temperature in Kelvin. If the land surface is considered as aggregated soil and vegetation, the sensible heat term H from Eq.( 1) is given by: (3) where p is the air density, cp is the specific heat at constant Taero is the aerodynamic temperature, T, is the air temperature in the surface layer, and r,H is the aerodynamic resistance to heat transfer. Neither T,,,, nor r& are simple to determine. The aerodynamic temperature is the temperature of the combined sources for convective heat transfer (i.e. both soil and vegetation contribute) and can be determined from profiles of temperatures and windspeed within the atmospheric boundary layer (Brutsaert, 1982). Although the surface brightness temperature is approximately the same as aerodynamic temperature over non-vegetated surfaces, it is not the same over most naturally vegetated surfaces. This has been demonstrated by Hall et al. (1992) working with data from the First ISLSCP Field Experiment (FIFE). Furthermore, when a model using.Eq.(3) is applied to sparsely vegetated surfaces, there are significant errors in predicted heat fluxes (Verhoef et al., 1997). pressure,
Two-Source Model
An important limitation to the preceding formulations of heat flux is that they implicitly aggregate the landscape into a single source with a single temperature. To illustrate the difficulty created with this assumption, consider the representation of temperature. In Eq.(3) aerodynamic temperature is unmeasureable. It represents an effective temperature required for a particular amount of heat transfer. The brightness temperature, on the other hand, is a radiometric observation and is affected by temperature distributions, surface orientations and viewing angle. Comparison of the two temperature types is thoroughly discussed in the paper by Norman et al. (1995a). Since a more generally useful model is sought, one that works well in heterogeneously vegetated landscapes, a more detailed model is needed. The one used here is a two-source model (Norman et al., 1995b), with modifications by (Kustas and Norman, 1999). It predicts heat flux by first partitioning net radiation, Rn, between soil and canopy elements, using an algorithm that computes net short wave and long wave balance for the canopy and soil. Using two-sources of energy flux, rather than one, accommodates differing absorptions of net radiation by vegetation and soil. In the parallel version of the two-source model (used here), the sensible and latent heat flux components for the vegetation are computed separately from the sensible and latent heat fluxes for the soil: R nc-Hc-LEc=O
(4)
R ,s-Hs-LEs-G=O
(5)
where the flux components are now distinguished by subscripts: the canopy, C and the soil, S. The total computed heat flux components are then: HT=Hc+Hs
(6)
LET = LEc + LEs
(7)
where the subscript T indicates total heat flux. The canopy heat fluxes, Eq.(4), are solved by first estimating the canopy latent heat flux from the Priestley and Taylor (1972) relation: LEc = 1.26f~R,c
A [ Y+A
1
where LEc is the latent heat flux from the vegetation canopy, fG is the fraction of green vegetation, R,c is the net radiation absorbed by the canopy, y is the psychrometric “constant” and A is the slope of the temperature-saturation vapor pressure curve. Note that Eq.(8) provides an initial calculation of the canopy fluxes, and can be overridden if vegetation is under stress (Norman et al., 1995b)). Since R,c has been previously computed, the canopy sensible heat flux, Hc, can solved by applying the energy balance. The soil heat fluxes, Eq.(5), on the other hand, are solved by computing the sensible heat flux first: Hs=~cp
[;rz;%;]
(9)
170
A. N. French et al.: Estimating Surface Fluxes over the SGP Site
Fig. 3. Sensible heat flux (Wm-‘) image over El Reno, OK using the two-source model and classifying surfaces based upon NDVI and land use imagery. Light tones indicate sensible heat flux over 2OOW - rnp2 and coincide with harvested and plowed fields.
where the new terms are: Ts for soil temperature, r&J for resistance to heat transfer between the vegetation and the overlying air, and rs, for the additional resistance to heat transfer from the soil surface boundary layer as described by Norman et al. (1995b). The downgoing soil heat flux G can be computed as a fraction of net radiation at the soil surface (Choudhury et al., 1987):
The estimated heat fluxes are also used to revise the vegetation resistance. The entire flux computation is then repeated until subsequent iterations show minimal change in flux values.
G = CG&,S
Inputs required for the two-source model are surface winds, humidity and temperatures, as well as estimates of vegetation properties (canopy height and width, leaf width and distribution, fractional cover and greenness, leaf area indices (LAI), clumping factors, momentum roughness lengths, momentum displacement heights, canopy extinction coefficients and albedo). Since the model is not very sensitive to the vegetation properties, nominal values can be assigned. For this study, surface temperatures (Fig. 1) were derived using the temperature-emissivity separation (TES) algorithm, which separates temperature and emissivity effects for the six TIMS channels (for details on TES see Gillespie et al. (1998)). Temperatures range from 27°C for water bodies, to over 43°C for bare soils. Spectral radiances collected by TIMS were converted to surface temperature and estimates of band averaged emissivities. For estimates of fractional cover and LAI, TMS-derived NDVI images were used (Fig. 2). All the other vegetation parameters enumerated above were estimated from field observations and TM-derived land use imagery. Over the El Reno site, TMS-derived NDVI values ranged from -0.1 over water bodies and bare soil surfaces, to +0.7 over heavily vegetated rangeland (e.g. bare soil field ER13 and rangeland field EROl). As for the thermal data, the TMS data were atmospherically corrected with MODTRAN (Berk et al., 1998) and registered to UTM (Universal Transverse
(10)
where CG is a fractional factor, here estimated to be 0.3, and R,,s is the net radiation at the soil surface. Applying energy balance for the soil flux components , Eq.(5), resolves the soil latent heat flux. In order to solve Eq.(8) and Eq.(9), the temperatures of the soil and vegetation surfaces are also needed, as well as the vegetation resistance. But since these are in turn dependent upon the heat fluxes, the very terms we seek, an iterative scheme is needed. The approach used here is to first estimate the temperatures using the approximation: TR = [f&
+ (1 - foTj)] ’
(11)
where TR is the radiometric temperature and fo is the fractional vegetative cover as seen by the radiometer, By initializing the canopy temperature, Tc, to the air temperature, the soil temperature, Ts, can be solved. Then the vegetation resistance, r&, is estimated from ground windspeed measurements, which are corrected for stability using equations found in Brutsaert (1982). A first pass estimate of the heat flux terms in Eq.(4) and Eq.(5) can now be computed. Using an inverted form of the sensible heat resistance equation, the canopy and soil temperatures can then be revised:
(12)
4
Data Processing
A. N. French et al.: Estimating
Sensible _
300
“E \ z
250
g
150
2 E
8
Heat
Flux
(W/m’)
200
Latent F
500
> V
400
: u it E
300
6 w J
100
100 50
I
0 0
50
100
150
200
H Observed
Soil _
250
5
200
Heat
250
(W/m’
Flux
Heat
Flux
(W/m’)
200
0
300
0
100
)
200
300
LE Observed
(W/m’) A
Net
Radiation
0
200
400
500
(W/m’)
(W/m’
)
800
“E 2
2 150 u Q, z E
100
‘7 c3
50
600
D -z zE 8 ii
0 0
50
100
G Observed
Fig. 4. Two-source model flux predictions ER05, ER09, ERI 3)
150
200
(W/m2
250 )
compared to ground observations.
Mercator) coordinates. The NDVI maximum and minimum values were used to generate a normalized NDVI array, values that are related to canopy cover as described by Gillies and Carlson (1995). LA1 were estimated from fractional cover using methods of Norman et al. (1995b). Maximum LA1 was constrained by ground observations. The El Reno sites, labeled in Figs. 1,2,3, primarily consisted of rangeland, pasture, harvested or senescent winter wheat fields, bare soil and water bodies. The sites with flux instruments, EROl, ER05, ER09 and ER13, had green LA1 average values of 3.85,2.24,2.14 and 0, respectively. The first three sites were rangeland, and the fourth was a plowed, bare soil (formerly winter wheat). Field ERlO was a recently harvested field containing winter wheat stubble.
5
171
Surface Fluxes over the SGP Site
Results
Application of the two-source model to data collected on July 2, 1997 over the El Reno, Oklahoma sites verifies the expected relationships. TIMS derived surface temperatures typically ranged from 32°C to 38°C over vegetation and from 40°C to 46°C over bare soils. Although extensive com-
400 200 0 Rn
400
Observed
600 (W/m2
Numbers within the graphs correspond
800 )
to El Reno sites with flux stations (EROI,
parisons between TIMS measurements and ground-based measurements are not possible, there is evidence from field EROl that the TIMS radiometric temperatures are accurate within 1°C. Here, TIMS measurements ranged between 32.9”C and 34.O”C, while a simultaneous tower based measurement with a footprint of about 3m2 gave a reading of 33.1”C. When the TIMS temperatures were combined with TMSbased NDVI estimates of vegetation cover, the two-source model created reasonable sensible heat flux images over the site (Fig. 3). Over green vegetated surfaces, sensible heat fluxes are less than 100 Wmw2, while for bare soil and wheat stubble surfaces they are greater than 200 Wm-2. Comparison with ground-based flux station observations, using eddy covariance instrumentation, net radiometers and soil heat flux sensors (Fig. 4), shows good agreement. Considering that the ground-based fluxes are accurate to approximately 50 Wme2, the net radiation and soil heat flux predictions are in close agreement with observations. Sensible heat and latent heat flux predictions, despite some bias, are also close to observations, with average differences of 43 Wme2 and 41 Wme2, respectively. In a study by Zhan et al. (1996) it was shown that errors in predicted heat fluxes are predom-
172
A. N. French ef al.: Estimating Surface Fluxes over the SGP Site
inantly sensitive to errors in air and surface temperatures. They showed, for example, that a 10% error in surface temperature can result in over 50% error in predicted sensible heat flux. In this study, surface temperature errors are on the order of 3%, and therefore heat flux errors ranging from 15 Wmm2 to 45 Wm-’ are expected.
6
Conclusion
Methodology developed to estimate surface heat fluxes yields results over the El Reno, OK SGP sites that reasonably agree with tower flux measurements. Data combined from aircraft mounted remote sensors and ground based point measurements have been used to create images of the four heat flux components. The current version of the two-source model does use several empirical relations that are site specific. For example, parameterization of fractional vegetation cover is highly sensitive to the maximum and minimum NDVI values viewed over the El Reno area. In this instance, further investigation is needed to find ways to reduce the resulting variations in flux predictions. Another limitation is the inability to distinguish between bare soil fields and harvested wheat fields (e.g. fields ER13 and ERlO in Fig. 2). One potentially fruitful technique, under current investigation, may remove this limitation by using thermal band emissivity variations to discriminate between bare soil and dry vegetation. Future work is planned to incorporate a series resistance version of the model, which allows for more interaction between the soil and canopy, investigate the utility of thermal emissivity variations over sparsely vegetated surfaces, and to test the model under a wider range of conditions. Acknowfedgemenrs. Dr. John H. Prueger from the USDA National Soil Tiltb Lab provided the surface flux observations. The logistical support and cooperation of the USDA-ARS, El Reno Grazing Lands Research Laboratory were critical to the successful data collection.
References Berk, A., Bernstein, L., Anderson, G., Acharya, P., Robertson, D., Chetwynd, J., and Adler-Golden, S., MODTRAN cloud and multiple scattering upgrade with application to AVIRIS, Remore Senr. Environ., 65, 367-375, 1998. Brutsaert, W., Evaporation in the armosphere, D. Reidel Pub. Co., Dordrecht, Holland, 1982. Choudhury. B., Idso, S., and Reginato, R., Analysis of an empirical model for soil heat flux under a growing wheat crop for estimating evaporation by an infrared-temperature based energy balance equation, Agric. For, Meteorol., 39, 283-297,
1981.
@lespie, ‘A., Rokugawa, S.. Matsunaga, T., Cothem, J., Hook, S., and Kahle, A., A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER)
images,IEEE Trans. on Geo. Fernore Sets, 36, 1113-l126, 1998, Gillies. R. and Carlson, T., Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models, J. Appl. Mefeorol., 34, 745-756, 1995. Hall, F., Huemmrich, K., Goetz, S., Sellers, I?, and Nickerson, J., Satellite remote sensing of surface energy balance: sucess, failures and unresolved issues in FIFE, J. Geophys. Rex, 97, 19061-19089, 1992. Kustas, W. and Norman, J., Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover, Agric. For: Mefeorol., 94, 13-29, 1999. Norman, J., Divakarla, M., and Gael, N., Algorithms for extracting information from remote thermal-IR observations of the earth’s surface, Remnrr Sens. Environ., 51, 157-168, 1995a. Norman, J., Kustas, W., and Humes, K., A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperature, Agric. fix Meteorof., 77, 263293, 1995b.
Priestley, C. and Taylor, R., On the assessment ofsurface heat flux and evaporation using large-scale parameters, Monthly Wrath. Rev., 100, 81-92, 1972.
Verhoef, A., Bruin, H. D., and den Hurk, B. V., Some practical notes on the parameter kB- 1 for sparse vegetation, J. Appl. Mereoml., 36, 560-572, 1997. Zhan, X., Kustas, W., and Humes, K., An intercomparison study on models of sensible heat flux over partial canopy surfaces with remotely sensed surface temperature, Remote Sens. Environ., S8, 242-256. 1996.
Pergamon
PII: S1464-1909(99)00132-X
Estimating Surface Fluxes over the SGP Site with Remotely Sensed Data A. N. French, T. J. Schmugge and W. P. Kustas Hydrology
Lab, Building
007, BARC-WEST,
USDA/AR&
Beltsville,
MD 20705
Received 24 April 1999; revised IO August 1999; accepted I4 September 1999
cultural fields at the El Reno site (Fig. 1). Since TB is a function of surface temperature and emissivity, the contrast seen in an image represents how incoming solar radiation is partitioned. Cooler temperatures usually indicate abundant moisture and therefore the dominance of latent heat flux. Conversely, hotter temperatures indicate dryer conditions and the dominance of sensible heat flux.
Abstract. The estimation of surface energy.fluxes using remotely sensed data requires the combination of data from several sources, including land use, vegetation cover and surface temperature. Land use and vegetation cover were obtained from visible and near infrared (VNIR) data, while the state variable, surface temperature, was obtained from thermal infrared (TIR) data. An approach to combine these data with an energy balance model was studied as part of the 1997 Southern Great Plains Experiment (SGP97). Toward this end, VNIR and TIR images for 2 July 1997 were analyzed over the El Reno, Oklahoma (OK) site using data from the Thermal Infrared Multispectral Scanner (TIMS) and Thematic Mapper Simulator (TMS) airborne instruments. Intensive ground measurements constrained leaf area indices, canopy height and surface meteorological inputs required by the model. The observed brightness temperatures, when corrected for atmospheric effects using MODTRAN, nearby radiosoundings and the temperature-emissivity separation (TES) algorithm, were mostly within 1°C of ground based temperatures. The resulting surface temperatures were used in a twosource model that considers the heat flux and temperature contributions from the soil and vegetation. The heat flux predictions on average agree within 50 Wm-’ of tower-based observations.
Surface fluxes, however, are related not only to surface temperatures, but also to vegetative properties, which can significantly affect the surface-air temperature gradient. For example, a 20 meter tall pine forest can be just as cool as a well-watered alfalfa field due to differences in aerodynamic properties of these two surfaces. The problem, therefore, is to assemble observable characteristics that can distinguish heat flux variations caused by temperature gradients from those caused by changes in vegetation. Characteristics such as surface brightness temperature, vegetation indices and land use, for example, can be incorporated into models that quantify the spatial variation in heat flux. Observables used in this study are thermal infrared, visiblenear infrared aircraft data sets from July 2,1997, Landsat TM imagery from July 25, 1997, and ground flux measurements collected at the USDA-ARS, El Reno Grazing Lands Research Laboratory (35”33’N, 98”OO’W). These are part of the Southern Great Plains (SGP97) experiment conducted in Oklahoma (OK) during the summer of 1997. Further details are available from http://hydrolab.arsusda.gov/sgp97/. The thermal infrared data, acquired with the Thermal Infrared Multispectral Scanner (TIMS), contains six channels spanning 8-12 pm and an instantaneous field of view of 2.5 mrad. At the aircraft altitudes of 5.0 km, resolution of the thermal infrared data is 12 m. From the same aircraft, Thematic Mapper Simulator (TMS) visible-near infrared imagery were obtained at similar resolutions and were used to create NDVI images (Fig. 2). The land use data is 30 meter resolution imagery based upon the combination of known ground conditions and the spectral properties of Landsat TM imagery.
Published by Elsevier Science Ltd. 1
Introduction
Measuring land surface heat fluxes at regional spatial scales is important for modeling atmospheric behavior and monitoring water resources. Although there are several surfacebased methods that can accurately measure heat fluxes at point locations, it is not feasible to use a network of these systems to create spatially distributed flux maps because of the high variability of real landscapes. One can most easily recognize this variability when looking at surface brightness temperatures (TB) from thermal infrared images over agriCorrespondence to: Andrew N. French 167
168
A. N. French et al.: Estimating
Surface Fluxes over the SGP Site
25.0 Fig. 1. TIMS derived surface temperatures over the El Reno, OK agricultural sites on July 2, 1997. Cool temperatures, darker tones, occur over pastures, rangelands and the water treatment ponds on the right. Hot temperatures, lighter tones, occur over harvested winter wheat fields and bare soils.
0.30
-0.10 Fig. 2. TMS derived, normalized difference vegetation index (NDVI) over El Reno, OK sites on July 2, 1997. Green vegetation is indicated by lighter tones. Water bodies, senescent vegetation and bare soil surfaces are indicated by darker tones. Particularly note that NDVI can not distinguish between the bare soil in field ER13 from the unplowed, wheat stubble in field ERlO.
169
A. N. French et al: Estimating Surface Fluxes over the SGP Site
2
Surface Energy Balance
3
There are three main factors required to estimate land surface fluxes: - Energy inputs - Moisture conditions - Near-surface
in soil and vegetation
meteorological
conditions
Both the energy inputs and the moisture condition factors can be estimated from remotely sensed data. There has been considerable recent work addressing these issues, which are essentially questions of insolation, surface reflectance, soil moisture and amounts of vegetation. The third factor, nearsurface meteorology, can not be measured remotely and conventional surface measurements must be used. Combining these factors in a surface flux model requires energy conservation. Energy balance at the land surface, neglecting photosynthesis, is given by: R,--H-LE-G=O
(1)
where R, is the net radiation, H is the sensible heat flux, LE is the latent heat or moisture flux into the atmosphere and G is the soil heat flux. The net radiation is the sum of the incoming and outgoing short and long wave radiation fluxes: R, = (1 - CY)R,,L+ cRLJ - caT4
(2)
where cx is the surface albedo, Rsol is the incoming solar radiation, E is the surface emissivity, RLJ is the incoming long wave radiation, (Tis the Stefan-Boltzmann constant, and T is the surface temperature in Kelvin. If the land surface is considered as aggregated soil and vegetation, the sensible heat term H from Eq.( 1) is given by: (3) where p is the air density, cp is the specific heat at constant Taero is the aerodynamic temperature, T, is the air temperature in the surface layer, and r,H is the aerodynamic resistance to heat transfer. Neither T,,,, nor r& are simple to determine. The aerodynamic temperature is the temperature of the combined sources for convective heat transfer (i.e. both soil and vegetation contribute) and can be determined from profiles of temperatures and windspeed within the atmospheric boundary layer (Brutsaert, 1982). Although the surface brightness temperature is approximately the same as aerodynamic temperature over non-vegetated surfaces, it is not the same over most naturally vegetated surfaces. This has been demonstrated by Hall et al. (1992) working with data from the First ISLSCP Field Experiment (FIFE). Furthermore, when a model using.Eq.(3) is applied to sparsely vegetated surfaces, there are significant errors in predicted heat fluxes (Verhoef et al., 1997). pressure,
Two-Source Model
An important limitation to the preceding formulations of heat flux is that they implicitly aggregate the landscape into a single source with a single temperature. To illustrate the difficulty created with this assumption, consider the representation of temperature. In Eq.(3) aerodynamic temperature is unmeasureable. It represents an effective temperature required for a particular amount of heat transfer. The brightness temperature, on the other hand, is a radiometric observation and is affected by temperature distributions, surface orientations and viewing angle. Comparison of the two temperature types is thoroughly discussed in the paper by Norman et al. (1995a). Since a more generally useful model is sought, one that works well in heterogeneously vegetated landscapes, a more detailed model is needed. The one used here is a two-source model (Norman et al., 1995b), with modifications by (Kustas and Norman, 1999). It predicts heat flux by first partitioning net radiation, Rn, between soil and canopy elements, using an algorithm that computes net short wave and long wave balance for the canopy and soil. Using two-sources of energy flux, rather than one, accommodates differing absorptions of net radiation by vegetation and soil. In the parallel version of the two-source model (used here), the sensible and latent heat flux components for the vegetation are computed separately from the sensible and latent heat fluxes for the soil: R nc-Hc-LEc=O
(4)
R ,s-Hs-LEs-G=O
(5)
where the flux components are now distinguished by subscripts: the canopy, C and the soil, S. The total computed heat flux components are then: HT=Hc+Hs
(6)
LET = LEc + LEs
(7)
where the subscript T indicates total heat flux. The canopy heat fluxes, Eq.(4), are solved by first estimating the canopy latent heat flux from the Priestley and Taylor (1972) relation: LEc = 1.26f~R,c
A [ Y+A
1
where LEc is the latent heat flux from the vegetation canopy, fG is the fraction of green vegetation, R,c is the net radiation absorbed by the canopy, y is the psychrometric “constant” and A is the slope of the temperature-saturation vapor pressure curve. Note that Eq.(8) provides an initial calculation of the canopy fluxes, and can be overridden if vegetation is under stress (Norman et al., 1995b)). Since R,c has been previously computed, the canopy sensible heat flux, Hc, can solved by applying the energy balance. The soil heat fluxes, Eq.(5), on the other hand, are solved by computing the sensible heat flux first: Hs=~cp
[;rz;%;]
(9)
170
A. N. French et al.: Estimating Surface Fluxes over the SGP Site
Fig. 3. Sensible heat flux (Wm-‘) image over El Reno, OK using the two-source model and classifying surfaces based upon NDVI and land use imagery. Light tones indicate sensible heat flux over 2OOW - rnp2 and coincide with harvested and plowed fields.
where the new terms are: Ts for soil temperature, r&J for resistance to heat transfer between the vegetation and the overlying air, and rs, for the additional resistance to heat transfer from the soil surface boundary layer as described by Norman et al. (1995b). The downgoing soil heat flux G can be computed as a fraction of net radiation at the soil surface (Choudhury et al., 1987):
The estimated heat fluxes are also used to revise the vegetation resistance. The entire flux computation is then repeated until subsequent iterations show minimal change in flux values.
G = CG&,S
Inputs required for the two-source model are surface winds, humidity and temperatures, as well as estimates of vegetation properties (canopy height and width, leaf width and distribution, fractional cover and greenness, leaf area indices (LAI), clumping factors, momentum roughness lengths, momentum displacement heights, canopy extinction coefficients and albedo). Since the model is not very sensitive to the vegetation properties, nominal values can be assigned. For this study, surface temperatures (Fig. 1) were derived using the temperature-emissivity separation (TES) algorithm, which separates temperature and emissivity effects for the six TIMS channels (for details on TES see Gillespie et al. (1998)). Temperatures range from 27°C for water bodies, to over 43°C for bare soils. Spectral radiances collected by TIMS were converted to surface temperature and estimates of band averaged emissivities. For estimates of fractional cover and LAI, TMS-derived NDVI images were used (Fig. 2). All the other vegetation parameters enumerated above were estimated from field observations and TM-derived land use imagery. Over the El Reno site, TMS-derived NDVI values ranged from -0.1 over water bodies and bare soil surfaces, to +0.7 over heavily vegetated rangeland (e.g. bare soil field ER13 and rangeland field EROl). As for the thermal data, the TMS data were atmospherically corrected with MODTRAN (Berk et al., 1998) and registered to UTM (Universal Transverse
(10)
where CG is a fractional factor, here estimated to be 0.3, and R,,s is the net radiation at the soil surface. Applying energy balance for the soil flux components , Eq.(5), resolves the soil latent heat flux. In order to solve Eq.(8) and Eq.(9), the temperatures of the soil and vegetation surfaces are also needed, as well as the vegetation resistance. But since these are in turn dependent upon the heat fluxes, the very terms we seek, an iterative scheme is needed. The approach used here is to first estimate the temperatures using the approximation: TR = [f&
+ (1 - foTj)] ’
(11)
where TR is the radiometric temperature and fo is the fractional vegetative cover as seen by the radiometer, By initializing the canopy temperature, Tc, to the air temperature, the soil temperature, Ts, can be solved. Then the vegetation resistance, r&, is estimated from ground windspeed measurements, which are corrected for stability using equations found in Brutsaert (1982). A first pass estimate of the heat flux terms in Eq.(4) and Eq.(5) can now be computed. Using an inverted form of the sensible heat resistance equation, the canopy and soil temperatures can then be revised:
(12)
4
Data Processing
A. N. French et al.: Estimating
Sensible _
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Fig. 4. Two-source model flux predictions ER05, ER09, ERI 3)
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compared to ground observations.
Mercator) coordinates. The NDVI maximum and minimum values were used to generate a normalized NDVI array, values that are related to canopy cover as described by Gillies and Carlson (1995). LA1 were estimated from fractional cover using methods of Norman et al. (1995b). Maximum LA1 was constrained by ground observations. The El Reno sites, labeled in Figs. 1,2,3, primarily consisted of rangeland, pasture, harvested or senescent winter wheat fields, bare soil and water bodies. The sites with flux instruments, EROl, ER05, ER09 and ER13, had green LA1 average values of 3.85,2.24,2.14 and 0, respectively. The first three sites were rangeland, and the fourth was a plowed, bare soil (formerly winter wheat). Field ERlO was a recently harvested field containing winter wheat stubble.
5
171
Surface Fluxes over the SGP Site
Results
Application of the two-source model to data collected on July 2, 1997 over the El Reno, Oklahoma sites verifies the expected relationships. TIMS derived surface temperatures typically ranged from 32°C to 38°C over vegetation and from 40°C to 46°C over bare soils. Although extensive com-
400 200 0 Rn
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Numbers within the graphs correspond
800 )
to El Reno sites with flux stations (EROI,
parisons between TIMS measurements and ground-based measurements are not possible, there is evidence from field EROl that the TIMS radiometric temperatures are accurate within 1°C. Here, TIMS measurements ranged between 32.9”C and 34.O”C, while a simultaneous tower based measurement with a footprint of about 3m2 gave a reading of 33.1”C. When the TIMS temperatures were combined with TMSbased NDVI estimates of vegetation cover, the two-source model created reasonable sensible heat flux images over the site (Fig. 3). Over green vegetated surfaces, sensible heat fluxes are less than 100 Wmw2, while for bare soil and wheat stubble surfaces they are greater than 200 Wm-2. Comparison with ground-based flux station observations, using eddy covariance instrumentation, net radiometers and soil heat flux sensors (Fig. 4), shows good agreement. Considering that the ground-based fluxes are accurate to approximately 50 Wme2, the net radiation and soil heat flux predictions are in close agreement with observations. Sensible heat and latent heat flux predictions, despite some bias, are also close to observations, with average differences of 43 Wme2 and 41 Wme2, respectively. In a study by Zhan et al. (1996) it was shown that errors in predicted heat fluxes are predom-
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A. N. French ef al.: Estimating Surface Fluxes over the SGP Site
inantly sensitive to errors in air and surface temperatures. They showed, for example, that a 10% error in surface temperature can result in over 50% error in predicted sensible heat flux. In this study, surface temperature errors are on the order of 3%, and therefore heat flux errors ranging from 15 Wmm2 to 45 Wm-’ are expected.
6
Conclusion
Methodology developed to estimate surface heat fluxes yields results over the El Reno, OK SGP sites that reasonably agree with tower flux measurements. Data combined from aircraft mounted remote sensors and ground based point measurements have been used to create images of the four heat flux components. The current version of the two-source model does use several empirical relations that are site specific. For example, parameterization of fractional vegetation cover is highly sensitive to the maximum and minimum NDVI values viewed over the El Reno area. In this instance, further investigation is needed to find ways to reduce the resulting variations in flux predictions. Another limitation is the inability to distinguish between bare soil fields and harvested wheat fields (e.g. fields ER13 and ERlO in Fig. 2). One potentially fruitful technique, under current investigation, may remove this limitation by using thermal band emissivity variations to discriminate between bare soil and dry vegetation. Future work is planned to incorporate a series resistance version of the model, which allows for more interaction between the soil and canopy, investigate the utility of thermal emissivity variations over sparsely vegetated surfaces, and to test the model under a wider range of conditions. Acknowfedgemenrs. Dr. John H. Prueger from the USDA National Soil Tiltb Lab provided the surface flux observations. The logistical support and cooperation of the USDA-ARS, El Reno Grazing Lands Research Laboratory were critical to the successful data collection.
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