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Estimating Latent Heat Flux in the ShrubSteppe Using Lysimeter and Satellite Data
Steven O. Link
Gregg M. Petrie
Lee E. Rogers
Kustas and others (1994a) developed a model to predict
LE in semiarid rangelands using aircraft and ground-based
meteorological data. LE was predicted within 15% of Bowenratio measurements for three days. The model was sensitive
to parameter values used in the calculation of sensible heat
flux (H) and, thus LE. The approach of Kustas and others
(1994a) is complex, requiring measures or estimates of
several energy budget variables and parameters not easily
obtained from satellites.
Another variable directly related to LE is plant community structure. LE is positively correlated with LAI when
LA! is less than three (Seevers and Ottmann 1994). LAI is
also correlated with NDVI obtained from satellite data
(Huete andJackson 1987; Sellers 1985). Running and N emani
(1988) demonstrated that transpiration can be related to
NDVI through LAI and used in the estimation ofregional ET
rates for weekly intervals.
Most of the remote sensing literature has dealt with closed
canopy crops with little effort expended on the 60% of the
Earth's land surface composed of ecosystems where LAI < 1
(Graetz 1990; Moran and others 1994a). Our purpose was to
develop a simple approach for the prediction of instantaneous LE in a shrub-steppe ecosystem using satellite and
easily obtained surface data.
Abstract-A map of instantaneous latent heat flux for a shrubsteppe site in south-central Washington was produced using Landsat
Thematic Mapper data and simple ground based measurements.
Latent heat flux ranged from <50 W m-2 in rocky areas with little
green vegetation to 100 W m-2 at high elevations with cool temperatures to 160 W m-2 in areas of high temperature and leaf area index.
Quantitative remote sensing allowed us to predict absolute values
of latent heat flux rather than relative values. This work supports
efforts to produce a more mechanistic model for general use.
Latent heat flux (LE) is the resultant of evaporation from
the soil and plant transpiration. The prediction of LE at
large scales is a critical component oflandscape level process
models (Graetz 1990). Models ofLE can be complex (van de
Griend and van Boxe11989) making large scale applications
difficult. Bolin (1988) suggests that models be simple with
prediction based on variables that can be measured from
satellites. We present a simple model that combines surface
brightness temperature (Ts) and the normalized difference
vegetation index (NDVI) measured with the Landsat Thematic Mapper satellite with leaf area index (LAD measures
to predict LE for a shrub-steppe ecosystem.
Remotely sensed Ts has been used to estimate regional LE
rates for agricultural purposes using the energy balance
equation (Brown and Rosenberg 1973; Reginato and others
1985). Less work has been done to estimate LE in nonagricultural conditions using remotely sensed data. Seguin
and others (1985) analyzed LE for a varied landscape including agricultural and natural systems in southern France.
They made use of an empirical simplification of the energy
balance equation using net radiation (Rn), Ts' and air temperature (Ta) to predict LE. The analysis relied on ground
based measures of Rn and Ta plus the Meteosat satellite for
measurement of Ts. An evapotranspiration (ET) map was
produced with rates ranging from 1 to 10 mm d- 1• Poor
calibration of the satellite caused an uncertainty of10-15 °e
in Ts' but because ground based temperature sensors were
used for calibration, error in Ts was estimated to be ± 2-3 °e.
Methods ----------------------------------Study Site
The study area is the FitznerlEberhardt Arid Lands Ecology (ALE) Reserve on the Hanford Site in south-central
Washington. The 312 km2 Reserve varies in elevation from
150 m to 1,100 m and is semiarid with hot dry summers and
cool wet winters. Average yearly precipitation ranges from
260 mm at the higher elevations to 160 mm in the lower
areas (Thorp and Hinds 1977). Vegetation is dominated by
bluebunch wheatgrass (Pseudoroegneria spicata), big sagebrush (Artemisia tridentata ssp. tridentata), Sandberg's
bluegrass (Poa sandbergii), and downy brome (Bromus
tectorum). Because of frequent fires, approximately 80% of
ALE is dominated by bluebunch wheatgrass (Rogers and
Rickard 1988).
In: Barrow, Jerry R.; McArthur, E. Durant; Sosebee, Ronald E.; Tausch,
Robin J., comps. 1996. Proceedings: shrub land ecosystem dynamics
in a changing environment; 1995 May 23-25; Las Cruces, NM. Gen. Tech. Rep.
INT-GTR-338. Ogden, UT: U.S. Department of Agriculture, Forest Service,
Intermountain Research Station.
Steven O. Link and Gregg M. Petrie are Senior Research Scientists, and
Lee E. Rogers is a Staff Scientist, Pacific Northwest Laboratory, Richland,
WA 99352. Steven O. Link is now an Associate Scientist, Washington State
University Tri-Cities, Richland, WA 99352
This paper is dedicated to the memory of Dr. Daniel E. Gibbons for his
fundamental contributions to this work.
Latent Heat Flux Data
LE was measured using weighing lysimeters; two in a
bluebunch wheatgrass community and two in the big sagebrush-bluebunch wheatgrass community (Gee and others
1991). The lysimeters contain undisturbed monoliths of soil
74
and vegetation. To measure Ts, an infrared-temperature
sensor with a waveband of 8-14 ~m and a 15° field of view
was mounted on the end of a 1.83 m pipe and pointed down
at the surface of one of the bluebunch wheatgrass lysimeters. The long axis of the viewed ellipse on the ground was
0.5 m. Data were acquired every 10 sec using a CR-7 datalogger (Campbell Scientific, Logan, UT) and converted to
hourly averages.
measurements were collected concurrent with overpass near
the weighing lysimeters. The TM images were registered to
ground control points and TM digital numbers (DN) of the
sampling plots were extracted for analysis. Atmospheric
corrections using the local radiosonde data were determined
using LOWTRAN6. Conversion tables ofDN and TM determined atmospherically corrected temperatures were calculated as in Gibbons and others (1989). Temperatures obtained using average DN values of the river and sample plots
for different communities were compared with ground-truth
mean temperatures. Corrected water temperatures agreed
to within 0.5 °C of ground-truth values. Corrected Ts compared well with ground-based mean temperatures for both
homogeneous «0.7 °C variance) and heterogeneous communities «1.5 °C variance) (Gibbons and others 1990).
Leaf Area Index Data
LAI (m 2 green leaf arealm 2 ground area) was measured for
plants at peak biomass in a blue bunch wheatgrass community, a big sagebrush-bluebunch wheatgrass community,
and in 2 communities dominated by downy brome. Sampling
was conducted in 10 randomly located plots at each site. All
herbaceous species were clipped at the ground level in a
0.5 m 2 circular area. Single-sided green leaf area was determined with a Licor 3100 Leaf Area Meter (Licor, Inc.,
Lincoln, NE). Leaf area of big sagebrush was estimated
using a model relating leaf area to canopy measures taken on
each shrub in 10 randomly chosen 50 m 2 plots (Link and
others 1990a).
Temperature Map Preparation
An atmospherically corrected brightness temperature map
was produced using conversion tables and color coded by
computer. Two digital files were produced; one for the
unsmoothed Ts values with 120 m pixels for use in the LE
map and the other with 30 m pixels for the Ts map. Finally,
box filters smoothed the 30 m digital temperature data to
graphically present a contouring of temperatures.
Topographic Map Preparation
A 3-D topographic profile of ALE was produced by draping
satellite imagery over a U.S.G.S. digital elevation map. The
profile was generated using a SPOT (Satellite Pour
l'Observation de la Terre) 10 m panchromatic image and
3 Landsat bands. The SPOT image was registered to a UTM
coordinate system and then the Landsat bands were registered to the SPOT UTM image. The 30 m Landsat image was
then resampled to the 10 m scale of the SPOT image. The
SPOT and Landsat images were subjected to a linearcontrast stretch to improve their appearance. A composite
image was produced using bands 7 (red), 4 (green), and
1 (blue).
Thematic Mapper Determination of NDVI
NDVI was determined from Landsat TM data using Bands
4 (near IR) and 3 (visible) as:
NDVI = P(Band 4) - P(Band 3) ,
(1)
P(Band 4) + P(Band 3)
where P are atmospherically corrected reflectances. Atmospheric corrections are needed to derive reliable measures of
vegetation (Huete 1988). Use of the soil-adjusted vegetation
index (SAVI) was not required because of the presence of
consistent light dry soils across the landscape and because
of the low cover «50%) of green vegetation (Huete 1988).
NDVI is best for estimating low values ofphytomass (Huete
and Jackson 1987). Gibbons and others (1990) determined
TM reflectances for all bands and compared them with mean
ground-truth reflectances obtained with spectroradiometric
measurements taken of plant communities on ALE using a
Daeldalus Spectrofax AA440 concurrent with TM overpass.
TM data were corrected using radiosonde data and a modified form of LOWTRAN6 to determine atmospheric effects.
For all plant communities, the average deviation from the
site mean reflectances was <4%. Look-up tables ofTM DN's
and atmospherically corrected reflectances were determined
to produce an NDVI map.
Landscape Thermal Measurements
Landsat Thematic Mapper (TM) band 6 thermal data
were acquired on May 8, 1989. TM brightness temperatures
(corrected for atmospheric conditions) were calculated for
each pixel and compared with selected ground-truth plant
community brightness temperatures. Concurrent with the
overpass, field data were collected including: (1) radiosonde
measurements (launched from the Hanford Meteorological
Station), (2) hand-held radiometer temperature measurements of the vegetation and soil communities, (3) Columbia
River temperatures (used to verify the atmospheric corrections modeled by the atmospheric computer code LOWTRAN6
[Air Force Geophysical Lab 1986]), and (4) thermal emissivities of vegetation and soil. The TM thermal channel (10.4212.46 ~m), with proper atmospheric and ground adjustments, yields Ts to within 0.6 °C of ground measurements
(Gibbons and others 1989; Wukelic and others 1989).
To compare satellite (120 m pixel) determined Ts to groundtruth plant community brightness temperatures a field
sampling technique was devised using hand-held radiometers (Gibbons and others 1990). Radiometer temperature
Prediction of Latent Heat Flux
Our approach to predicting LE is derived from the energy
balance equation:
LE=Rn- G - H,
(2)
where LE is latent heat fl ux, Rn is net radiation, G is soil heat
flux, and H is sensible heat flux, all in units ofW m-2 .
75
We empirically related LE to Ts as:
=b o + b I
LE
Ts ,
cooler than surrounding areas. The cool patch on the upper
left (a) is composed of a dense community of annuals in an
ephemeral playa. The cool strip (b) is a riparian area with
trees.
The NDVI map (fig. 5B) indicates higher ratios (>0.4) at
the higher elevations, in the playa, and riparian areas than
at lower elevations and bare rock areas (c). NDVI in riparian
areas is greater than 0.5.
(3)
where b o and b I are regression parameters estimated by
relating diurnallysimeter values of LE to diurnal groundlevel measurements of Ts. The only component of Eq. 2
measured from the satellite is Ts (Jackson and others 1983).
Thus, we chose to relate Ts to LE rather than make assumptions about how Rn , G, and H vary in the landscape. Diurnal
changes in T B are highly correlated with LE rates (Taconet
and Vidal-Madjar 1988). It is difficult to provide accurate
estimates of G in natural landscapes where heterogeneous
surface characteristics create sampling and thus, modeling
difficulties. This is also true of H which is a function ofTa' T s'
and wind speed (D). Ta and D are similarly difficult to predict
across the landscape. Each of these terms is related to or can
be empirically related to T s' The assumptions we make are
that the relationship is independent of variation in vegetative cover over the landscape and that temporal variation
can be substituted for spatial variation.
The other predictor ofLE is LAI (Link and others 1990a).
To compute LAI from satellite data we related observed LAI
from a bluebunch wheatgrass community, a big sagebrushbluebunch wheatgrass community, 2 communities dominated by downy brome, and bare ground to NDVI as:
LAI
=
bo + b I * NDVI
ifNDVI < 0.3375
0.31
ifNDVI>
= 0.3375
120
E
3=
= (bo + b I Ts) * (LAI/O.251).
80
>::::s<
u::
--
60
as
CD
::J:
•
c: 40
CD
as
...J
20
(4)
0
where b o is the intercept and b I is the slope. We assumed a
linear relationship between LAI and LE and relativized the
relationship between LE and LAI to that occurring at the
bluebunch wheatgrass lysimeter where LAI = 0.251. Rock
surfaces have an LA! and LE of O.
The model used to predict LE is:
LE
100
'1'
30
20
Figure 1-Relationship between latent heat flux
and surface temperature measured on a bluebunch
wheatgrass weighing Iysimeter. Data were collected on May 8, 1988.
(5)
This model makes simplifying assumptions about the distribution of driving variables in the landscape, namely that the
whole landscape is like the weighing lysimeter. We assume
that Rm G, H, and D are consistent across the landscape.
Such assumptions limit the relevancy of the model to this
site and time.
120
D
-
100 -
D
•
'1'
Results
50
40
Surface Temperature (Oe)
E
3=
-----------------------------------
--
i
D
•
D
D
::::s
u::
D
iii
80
><
LE, measured by lysimetry, is strongly related to Ts (eq. 3;
b o=-10.44; b I =2.82; R2 =0.94; fig. 1). Predicted and observed
LE are also graphed as a function of time (fig. 2). The
coefficient of variation for the 4 lysimeters averaged 16%
from 10:00 to 14:00 when LE was greatest.
The piece-wise relationship between LAI and NDVI (fig. 3)
was significant for NDVI < 0.3375 (eq. 4; bo =-0.418; bI = 2.158;
R2 = 0.96; fig. 3). For NDVI > 0.3375, LAI was assigned the
value of 0.31, the predicted value for the linear portion of the
relationship when NDVI =0.3375.
T B ranged from 30 to 55°C being the lowest at the highest
elevation and the highest at the lowest elevations (figs. 4 and
5A). Two locations within the> 50°C zone were 5 to 10 °C
D
• • •
•
•
60
a
as
CD
•
•
::J:
c: 40 .
D
CD
as
...J
20
a Observed
• Predicted
c=::J
0
I
I
6
8
10
12
14
I
16
Time (h)
Figure 2-Predicted and observed latent heat flux
at the bluebunch wheatgrass weighing Iysimeter.
76
I
18
20
LE ranged from 0 to 160 W m-2 across the landscape (fig. 5D).
Lowest values are in rocky areas with little green vegetation
«75 W m-2 ), low to middle elevation areas where Sandberg's
bluegrass had senesced (100 W m-2 ), and at high elevations
with cool temperatures «100 W m-2 ). Highest values (>150
W m-2 ) are at middle to low elevations having high LA! and
Ts (>45 DC).
0.5.-------------------.
0.4
Sagebrush-bunchgrass
•
><
Q)
•
"C 0.3
.E
as
Q)
Downy brome
•
....
«
Discussion
0.2
--------------------------------
We present a simple approach to predict LE across the
landscape in the shrub-steppe using quantitative remote
sensing satellite and lysimeter data. The range ofLE values
across the landscape (0-160 W m-2 ) compares well with the
micrometeorological observations of Kustas and others (1989)
in the arid Owens Valley of California. This range of LE is
lower than is commonly found in agriculture. Moran and
others (1989) reported LE in cotton, wheat, and alfalfa fields
of 400-700 W m-2 . The low values we found are because ofthe
low LA! in the shrub-steppe. This ecosystem is water limited
and thus plant growth is limited (Link and others 1990a).
Quantitative remote sensing data allowed us to estimate
absolute values of LE across the landscape rather than
relative values (Pierce and Congalton 1988; Thunnissen and
Nieuwenhuis 1989).
LE patterns are related to vegetation patterns. Most of
ALE is dominated by bluebunch wheatgrass (Rogers and
Rickard 1988). The LE model was developed from bluebunch
wheatgrass-dominated lysimeters. The effects of soil water
stress on LE are manifested through lower LA! and thus
aQ)s
...J
0.1
0.0 t----.--r-----4'1F~--r-....,......---y-...........~-.....---1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Normalized Difference Vegetation Index
Figure 3-Piece-wise linear relationship between
the leaf area index and the normalized difference
vegetation index.
The LA! map (fig. 5C) indicates large areas with values of
0.31 at high and low elevations. The lowest LA! values occur
in rocky areas and at the middle and low elevations where
the dominant plant cover, Sandberg's bluegrass, had senesced.
F~gu.re 4-Topographic view of the Fitzner/Eberhardt Arid Lands Ecology Reserve on the Hanford
Site In south-central Washington.
77
;
...•
A
Figure 5-Map of (A) surface temperature (0C), (8) normalized difference vegetation index, (C) leaf area
index, and (D) latent heat flux (W m-2 )
from satellite imagery of the Fitzner/
Eberhardt Arid Lands Ecology Reserve, Hanford, Washington, on
May 8, 1989 at 11 :16 PDT. The cool
patch on the upper left (a) is an
ephemeral playa and the cool strip
(b) is a riparian area.
c
dense canopy up to 0.6 m tall with basal diameters up to 0.5 m.
Interspaces are bare soil, cryptogamic crusts and Sandberg's
bluegrass. The downy brome communities are homogeneous
and generally less than 0.5 m tall. Interspaces are covered
with litter with little bare soil showing. It is likely that the
differing NDVI values of these communities can yield similar values of LAI due to this structural difference. If the
bunchgrasses were clipped and the foliage arranged about
the surface as in the downy brome communities we suspect
the surface would have a higher NDVI.
Huete and Jackson (1987) investigated the suitability of
spectral indices for evaluating vegetation in arid rangelands
and found that the amount of senesced grass strongly
lower values ofLE. Rocky areas where the dominant vegetation is the shallow rooted perennial grass (Sandberg's bluegrass) had low LA! and thus low LE even though Ts was near
50 °e. At lower elevations where LE was low, Sandberg's
bluegrass, the dominant species had senesced and consequently had a low LA! (Link and others 1990b). At other low
elevation areas where Tsand LAI were both high, the model
predicted high LE.
Values of LA! in our study were less than 0.5 and were
apparently nonlinear with NDVI. Generally, the relationship is linear up to LAI values of3-4 (Nemani and Running
1989). Our relationship may be explained by differences in
plant community structure. Bluebunch wheatgrass has a
78
influenced the green vegetation signal. This complication
would make it difficult to quantitatively measure green
phytomass remotely using NDVI. In our case, we are relying
on a site-specific relationship between NDVI and LAI to
estimate LA! across the site. Thus, the relationship we found
between NDVI and LAI is unique to the plant communities
of ALE.
Most areas had high LAI, thus LE for these areas is a
function of T s. This is why LE is relatively low at the high
elevations. In this ecosystem, where much of the surface is
dominated by soil, it is likely that hot air rising from the soil
increases the vapor pressure deficit, thus increasing transpiration. Soil temperature can be 30°C higher than adjacent canopy temperature in this ecosystem (Gibbons and
others 1990).
We did find a strong positive correlation between Ts and
LE in time, thus we feel it is reasonable to expect LE to vary
with Ts across the landscape. We were not able to test this
expectation. The use of Ts to empirically predict LE has been
proposed by others (Carlson and Buffum 1989). While this
logic supports the use ofTs from satellites to estimate LE, it
remains only an empirical attempt to predict a very complex
process.
A playa (a) and a riparian area (b) where LE was poorly
predictedhadhighNDVI and relatively low Ts (fig. 5A,B). In
these areas LAI is likely to be greater than one reducing the
effect of soil on observed Ts and lowering the observed Ts by
evaporational cooling. Such areas are similar to agricultural
sites. Our model is not adequate for such areas.
Mapping accuracy ofLE will increase with improvements
in our application of the energy budget equation after the
inclusion of site level meteorological data as in Moran and
others (1989), Kustas and others (1994a). Although the
collection of meteorological data (Bowen ratio method for
estimating LE) will likely improve our ability to estimate
LE, it has not been used successfully in pheatophytic rangelands (Nichols 1992). Improvements in the estimation ofLE
for semiarid rangeland have been made (Kustas and others
1994a; Moran and others 1994b), but difficulties remain.
The difficulty is, in part, the use of a single valued abovecanopy bulk aerodynamic resistance term when plant and
soil processes operate very differently in terms of the energy
budget. LE from plants is, in part, driven by the sensible
heat flux (H) of the soil in sparse canopy environments. The
separation of plant and soil in the energy budget is very
difficult. As pointed out by Moran and others (1989) in the
case where Ts is dominated by soil rather than the transpiring
vegetation, H is overestimated and LE is underestimated.
While our prediction ofLE would be improved by measuring and estimating each of the terms in the energy budget
equation across the landscape (Humes and others 1994;
Kustas and Daughtry 1990; Kustas and others 1994b), the
practicality of measuring and estimating Rn , G, Ta , U, and
surface parameters for aerodynamic resistance for routine
mapping in complex terrain (Moran and others 1989) at the
regional scale is limited.
Although difficulties exist in making use of the energy
balance equation at the regional scale, progress is possible.
For example, surface parameters for aerodynamic resistance depend on plant community characteristics. Further
improvements in the mapping of LE will be made by improved classification of plant communities, as has been done
for ALE using AVHRR satellite data (Kremer and Running
1993), and relating characteristics of the communities to LE
on the ground and then relating these measures to satellite
data. This route will lead to a series of equations unique to
each plant community.
A further generalization of such equations into an explicit
prediction of LE using the energy budget will improve the
prediction oflandscape scale LE (Wu 1990). This will require
the development of predictive systems models using a combination of ground based and satellite data to predict plant
growth and as a consequence, LAI. Ecosystem simulation
models have successfully predicted LE using remotely sensed
data at the daily time scale in agricultural systems
(Thunnissen and Nieuwenhuis 1989), at the weekly time
scale (Running and Nemani 1988) and monthly time scale
(Hoshi and others 1989) in forested ecosystems. The use of
simulation models may improve our ability to predict LE in
the landscape at shorter time scales.
Acknowledgments _ _ _ _ _ __
Research was supported by the U.S. Department of Energy
under Contract DE-AC06-76RLO 1830. We thank O. Abbey,
P.A. Beedlow,J. L. Downs,M.J. Harris,R. R. Kirkham,L. G.
McWethy, K Steinmaus, and M. E. Thiede for technical assistance. Discussions with Danny Marks and Sue Moran are
gratefully acknowledged.
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