Charlotte Bay: Satellite remote sensing as a tool for
spatial evapotranspiration estimation in vegetated
Geografisk Tidsskrift 93:56-62. Copenhagen
The present paper outlines
and tests a method for satellite mapping of
evapotranspiration based on the canopy resistance
theory. The dataset used for the analysis was obtained
during a field campaign conducted in an agricultural
region east of Viborg, Jutland, April to August 1990.
Scale between low-resolution NOAA satellite images and
ground truth values were tested with a single
high-resolution Landsat satellite image.
The results are found to
be in accordance with field observations. In accordance
with canopy resistance theory, a simple empirical
algorithm was found effective for evapotranspiration
estimation using satellite data. This approach also
produced better defined differences between fields than
a model based on net radiation, air temperature, and
satellite-derived surface temperature.
sensing, evapotranspiration, NDVI.
Hasager, M.Sc., Meteorology and Wind Energy
Department, Risø National Laboratory, DK-4000
The hydrological cycle is of
indisputable importance to human life on earth. In
vegetated regions water budget calculations are useful
in determining growth potentials, irrigation needs, and
potential wash out of sediments and pollutants to
rivers, seas and groundwater. Evapotranspiration is a
major factor in these water budget models. Until now,
however, the spatial variation in evapotranspiration due
to heterogenity of landscapes has been difficult to
sensing data offer a great potential for
spatial estimates of phenomena like evapotranspiration
(Seguin & Itier, 1983, Hope, 1988,
Nemani & Running, 1989,
Lagouarde, 1991). The purpose of this study was to
determine if sufficiently accurate estimates of
evapotranspiration, and its variability over a
significant area, could be derived in part from
satellite data. In this study a combination of
information from visual (VIS), near-infrared (NIR) and
thermal infrared (TIR) channels were used to estimate
and map evapotranspiration. Results were compared to
ground reference evapotranspiration measurements.
Evapotranspiration itself is not directly measurable by
The ground measured
evapotranspiration reference data were obtained from
measurements in a springbarley field located east of
Viborg in the vicinity of Research Center Foulum (Fig.
1). The field campaign was conducted in cooperation with
Dept. of Agrometeorology, Research Center Foulum.
Intensive data analysis was carried out (Højgaard et
al., 1990, Søgaard, 1992, Hasager, 1992).
Fig. 1. Location of the 15 km by 15 km
study area at Research Center Foulum.
SATELLITEE BASED ESTIMATION
Vegetation growth and
transpiration is largely controlled by water and
nutrient availability, photosynthetically active
radiation (PAR) intensity, and temperature. Plant
diseases and herbivores may retard or damage vegetation
growth. In Danish agricultural crops adequate amounts of
nutrients are supplied and pesticides and herbicides are
used whenever necessary. The result is that
micro-climatological parameters may be regarded as
Green leaves and stems contain
chlorophyll, which absorbs part of the incoming PAR and
converts it to biochemical energy used in
photosynthesis. During photosynthesis plants capture
CO2 from the air through open stomata in
leaves and stems. Whenever the stomata are open water
vapor will diffuse out through the stomata and be lost
as transpiration to the atmosphere. This means that the
photosyntetic rate is in linkage with the amount of
water loss. When the photosyntetic rate is low (near
zero) due to lack of PAR, soil water, optimal growth
temperature or nutrients the stomatal guard cells will
close the stomata and thereby diminish the transpiration
loss (Rosenberg et al., 1983).
Photosynthetic active green
leaves absorb most of the PAR resulting in low
reflectivity of visible light. Furthermorelive
vegetation reflects a high proportion of NIR radiation.
Non-active leaves and soil have a much greater
reflectivity in PAR and a lower reflectivity in NIR than
Consequently the difference between reflectionof PAR and
NIR is widely used to discriminate vegetated from
non-vegetated areas (Poulsen & Svendsen,1992). In
this study, though, the focus was on relative
photosynthetic rates. According to two-way radiation
transfer theory (Sellers 1989),the Normalized Difference
Vegetation Index (NDVI) is positively and linearly
correlatedwith photosyntetic rate. NDVI is defined as
Remote sensing-derived NDVI of
live vegetation is useful as a measure of canopy
resistance in deterministic evaporation models (Hope et
al., 1988, Hope, 1988, Nemani & Running, 1989). The
higher the density of active leaves, the lower the
canopy resistance will be. Well-watered vegetation is
able to transpire a significant amount of water and
thereby reduce the heating of the canopy. The surface
temperature of such a canopy will be low relative to a
dry, low-transpiring canopy. NDVI is within the range of
such a temperature difference proportional to the number
of open stomatas (Nemani & Running, 1989).
Inputs to a simple canopy
resistance model are surface temperature, Ts,
and NDVI. According to Nemani & Running (1989)
TS/NDVI can be used as a proxy for canopy
resistance. And as canopy resistance in vegetated areas
is a controlling factor of evapotranspiration, a simple
regression model may be used as a first approximation
A limitation of the model is
that agricultural crops and forest may have different
relations between NDVI and canopy resistance. Therefore
evapotranspiration estimates within a region may need to
be calculated individually for different vegetation
Spatial ET estimation from
satellite data has previously been carried out in the
Foulum area (Søgaard, 1992) using Landsat TM and NOAA
AVHRR satellite data. A different approach was used
namely the simplified relationship. This approach is
based on the difference between evapotranspiration and
net radiation (mm) and difference in surface and air
temperature (Celsius) (Seguin & Itier, 1983).
The analysis is
based on ground reference data measured
on a 15 ha large springbarley
field located at Research Center Foulum near Viborg.
Micrometeorological data sampling from two masts as well
as reflective radiometer measurements were carried out
from April to August 1990 and were also used by Søgaard
(1992). From Dept. of Agrometeorology, Research Center
Foulum micrometeorological data of precipitation,
soilwater content, reflective radiometer data, and Leaf
Area Index data were obtained.
For the data
analysis a number of NOAA-11 satellite
recorded with the radiometer AVHRR (Advanced
High Resolution Radiometer) were used. Twentytwo
images were obtained from Dundee, Scotland. The
images were recorded between April 30 and August 3,
1990. All images were geometrically corrected to UTM
coordinates. Information from channel 1 (0.58-0.68
VIS), channel 2 (0.72-1.10 ,urn, NIR), channel 4
fim, TIR) and channel 5(11.5-12.5 /mi,
TIR) were used in
the analysis. Pre-flight
calibration data from Updates to
(1979/1990) and post-flight calibration data
Che & Price (1992) were used.
One Landsat-5 satellite image
recorded with the radiometer TM (Thematic Mapper) on
July, 15, 1990 was obtained from Kiruna, Sweden.
Information from channel 3 (0.63-0.69 Mm, VIS), channel
4 (0.76-0.90 /mi, NIR) and channel 6 (10.4-12.5 /mi,
TIR) was calibrated (Markham & Barker, 1987, Wukelic
et al., 1989) and analysed. The image was geometrically
corrected to UTM coordinates.
METHODS USED TO DERIVE NDVI
TEMPORALLY AND SPATIALLY
Ground-reference NDVI was
calculated from measurements made with a Milton
Multiband Radiometer (MMR) in the spring barleyfield and
simoultaneously calibrated with Kodak Grey Cards. The
observations were in close agreement with NDVI measured
with a CROPSCAN radiometer in a nearby plot of spring
barley by Dept. of Agrometeorology, Research Center
Foulum. CROPSCAN was calibrated with hemispherical scans
of incoming radiation (Thomsen, 1992). Evaluation of
NDVI development during the growing season is shown
NDVI was also calculated from
a Landsat TM satellite image using channels 3 and 4. The
Landsat-derived NDVI in the Foulum region on July 15th.,
1990, 10.30 local time, is shown (Fig. 3). The Landsat
pixel size is 30 m by 30 m. The lakes of Viborg and
Tjele Langsø are shown in black in the image. Research
Center Foulum is visible as a dark semicircle north of
"Foulum". The white area in the northern direction is a
grass field, and, the springbar-
Fig. 2. Development in NDVI of spring
barley during the growing season 1990 at Foulum.
Spectral data obtained with ground radiometers
(CROPSCAN, MMR) and satellite radiometers (NOAA AVHRR,
Fig. 3. Spatial distribution of
vegetation in the Viborg region from Landsat 5 TM, July
15, 1990, at 10.30 local time
ley field refered to in this
experiment, is situated south of the buildings. The
barley field covers 164 Landsat pixels with a NDVI mean
value 0.684 and a 95% confidence interval of
[0.682,0.686]. The field is very homogeneous and the
NDVI values in the Landsat image are in very close
agreement with ground radiometer observations (Fig. 2).
The Foulum region is dominated by barley and wheat. Less
frequently found are grass, rape, rye, beets, peas, and
The Landsat NDVI of the
agricultural crops clearly shows the field pattern (Fig.
3). Dark grey areas show low photosynthetic activity
whereas light grey and white areas show higher
photosynthetic activity. NDVI typically variesfrom
0.4-0.8. The Nørre Å Valley winding from West to East in
the southern part of Fig. 3, and the valley southwest of
Tjele Langsø are visible with relatively high
typical field pattern mosaic. The urban environmentof
Viborg is characterised by areas of very low
satellite images are, in a relative sense, expensive and
only available every 16 days. Therefore, cheaper
low-resolution NOAA satellite images available twice
daily are preferred for multitemporal studies. NDVI was
calculated from NOAA images using channels 1 and 2.
NOAA-derived NDVI on July 15, 1990, 13.44 local time, in
the Foulum region is shown (Fig. 4). The pixel size is 1
km by 1 km. The coarse resolution makes it possible to
deduce only the more dominant features like low NDVI
near Viborg and higher NDVI values in the agricultural
areas. Visual comparision of Landsat TM and NOAA AVHRR
satellite data from the same area (Fig. 3 and 4) clearly
shows the difference between high and low spatial
The barley field
covers only a fourth of one NOAA pixel.
of the pixels that included the barleyfield
spatially registrered for all NOAA images (Fig. 2). It
Fig. 4. Spatial distribution of
vegetation in the Viborg region from NOAA 11 AVHRR, July
15, 1990, at 13.44 local time
may be noted that the
NOAA-derived NDVI is slightly higher than the ground
radiometer observations. This may be due either to
subpixel "noise" from the surroundings of the
barleyfield (which are inevitably included in the
registered pixel values) or to the type of atmospheric
correction performed. To determine the actual mechanism,
a test was carried out. NDVI from Landsat and NOAA
images recorded on the same day were compared
statistically. The result indicates that the
atmospherical correction algorithms used (Goward et al.,
1991, Justice et al., 1991) with input data of
precipitabel water vapor content from radiosondes at
Aalborg and Jægersborg, Danish Meteorological Institute,
and aerosol loading at Karup, DMI, yielded correction
values 0.09 NDVI-units too high in the NOAA image. In
subsequent analysis this finding was taken into account.
The information content of
NDVI in the NOAA scene relative to the Landsat scene was
statistically evaluated. It was found that 72 % of the
information content variation in NDVI in the Landsat
scene was accounted for in the
Fig. 5. Ground observations of ration
of surface temperature/ NDVI versus evapotranspiration
(ET) for spring barley, Foulum, July 9to August 1, 1990,
with linear regression line (R2 =
NOAA scene. The
indication is that NOAA AVHRR can
be a reliable
source of NDVI data when attention is focused
dominant features of the landscape.
Ground observations of surface
temperature measured with a Heiman 17K infrared
thermometer were found to be in agreement with surface
temperature derived from Landsat and NOAA satellite
images. Landsat TM data from channel 6 was calibrated
and transformed to surface temperature (Søgaard, 1992,
Wukelic et al., 1989) and NOAA AVHRR data from channel 4
and 5 were used with a split window technique (Price,
1983) to obtain surface temperature.
ESTIMATION OF SPATIAL
Fig. 6. Spatial distribution of
evapotranspiration in the Viborg region from Landsat 5
TM, July 15, 1990
observations of surface temperature, ground-derivedNDVI,
and calculated evapotranspiration from
ground observations of
roughness, wind and temperature profiles obtained during
July 1990 in the barley field in Foulum, were used to
obtain regression values of a and b according to .
The following relation was found
The data and
regression line are shown (Fig. 5) with R2R2
of 0.69 based on 22 observations
result was applied to Landsat data to
evapotranspiration in the Landsat TM satellite
and the result is shown (Fig. 6).
Results of spatial
evapotranspiration estimation Actual evapotranspiration
(ET) in the region of Foulum calculated from the Landsat
satellite image ranges from (slightly) less than zero to
more than 4 mm per day (Fig. 6). In the agricultural
area, ET is mainly between 2 and 4 mm per day. In the
springbarley field ET is around 3 mm. This is in
accordance with ground measurement data. The field
pattern is clearly visible. The phenomena may be
assigned to crop specific relations of
TS/NDVI. This effect is not present in NOAA
images where average ET of several crop types are
The forested areas of Sødal
Skov and Viskum Skov show relatively high ET values on
the Landsat image. Evapotranspiration in forest and
non-vegetated areas may not be properly calculated as
 is valid for the agricultural area. Areas with ET
less than zero is obviously (slightly) too low. The
lakes generally show high ET but some black spots (ET
less than zero) is a non-correct classification of
evapotranspiration rates. Low ET is encountered in the
Viborg urban area.
Discussion of spatial
Comparision of the spatial
variation on ET estimation between the results from
canopy resistance theory  evaluated here and
simplified relationship Søgaard's (1992) work shows
close agreement for the agricultural area. For example
the river course of Nørre Å with high ET can be seen to
cross through Vejrum village with low ET (dark areas) in
both cases. A difference between the results is that the
field pattern is clearly distinguished by ET estimation
from canopy resistance theory whereas this is not the
case of ET estimation using the simplified relationship
used by Søgaard.
The regression result on the
simplified relationship yielded a R2R2 value
of 0.62 based on 23 observations (Søgaard, 1992) which
is slightly lower than the canopy resistance method.
Statistical testing including non-linear relations may
improve ET estimation from canopy resistance
Information on the spatial
distribution of crop types, forest and non-vegetated
areas may be obtained from Landsat satellite images or
from a GIS (Geographical Information System) database.
This information, in the author's opinion, would likely
improve spatial estimation of evapotranspiration from
canopy resistance theory because the relation
TS/NDVI may vary for different landcover
The spatial distribution of ET
calculated from satellite images using canopy resistance
theory is encouraging. The results are in agreement with
ground reference observations as well as with regional
ET estimates from the simplified relation used by
Søgaard (1992). Further development and evaluation of
different approaches for satellite evapotranspiration
estimation will require a dense net of regional ground
observations of ET or model simulations.
Che, N. & J.C. Price
(1992): Survey of Radiometric Calibration Results and
Methods for Visible and Near Infrared Channels of
NOAA-7, -9, and -11 AVHRRs, Remote Sens. Environ, 1, pp
Goward, S.N., B. Markham,
D.G. Dye, W. Dulaney & J. Yang (1991): Normalized
Difference Vegetation Index Measurements from the
Advanced Very High Resolution Radiometer, Remote Sens
Environ, 35, pp 257-277.
Hasager, C.B. (1992):
Anvendelse af NOAA og Landsat satellitdata samt feltdata
ved kortlægning af vegetation og overfladefluxe i
Danmark, Speciale i naturgeografi, Geographical
Institute, University of Copenhagen, Internal report.
(1988): Estimation of wheat canopy resistance using
combined remotely sensed spectral reflectance and
observations, Remote Sens. Environ., 24, pp
Hope, A.S., S.N. Goward
& D.E. Petzold(\9BS): Tersail: A numerical model for
combined analysis of vegetation canopy bidirectional
reflectance and thermal emissions, Remote Sens.
Environ,26, pp 287-300.
C.B. Jensen, T. Jensen & I. Sonne (1990):
af evapotranspiration ved 4 forskellige
satellitbilleder og jord-
projekt, Geographical Institute University
Copenhagen, Internal report.
Justice, CO., T.F. Eck, D.
Tanre & B.N. Holben (1991): The effect of water
vapour on the normalized difference vegetation index
derived for the Sahelian region from NOAA AVHRR data,
Int. J. Remote Sensing,vol 12,n0 6, pp 1165-1187.
J-P. (1991): Use of NOAA AVHRR data combined
agrometeorological model for evaporation mapping
Int. J. Remote Sensing, vol 12, no 9, pp 1853-1864.
Lauritson, L., A. Nelson
& P.M. Porto (1979): Data extraction and calibration
of Tiros N/NOAA radiometers. NOAA Technical Memorandum,
NESS 107, Updates to Appendix B foi NOAA-H/11,
& J.L. Barker (1987): Thematic Mapper bandpass
solar exoatmospheric irradiances, Int. J. Remote
vol 8, no 3, pp 517-523.
Nemani, R.R. & S.W.
Running (1989): Estimation of Regional Surface
Resistance to Evapotranspiration from NDVI and
Thermal-IR AVHRR Data, Journal of Applied
Meteorology,vol 28, pp 276-284.
Poulsen, J.N. & T.B.
Svendsen (1992): Spectral signatures of Danish crops
multitemporal signatures derived from high resolution
satellites, Proceedings from the Workshop on Remote
Sensing, Sostrup Castel, Grena.(Eds) Thomsen,A. A.Jensen
& H.E.Jensen, Landbrugsministeriet, Statens
Planteavlsforsøg, Tidsskrift for Planteavls
Specialserie, Beretning nr. S 2207-1992, 83-95.
Price, J. C.
(1983): Estimating Surface Temperatures from Satel-
N.J, B.L. Blad&A.B. Verma (1983): Microclimate.
The Biological Environment, John Wiley & Sons,
& B. hier (1983): Using midday surface temperature
estimate daily evaporation from satellite thermal
Int.J.Remote Sensing,vol 4, no 2, pp
Sellers, P.J. (1989):
Vegetation-canopy spectral reflectance and biophysical
processses. pp297-335 in G.Asrar (ed):Theory and
Applications of Optical Remote Sensing, John Wiley &
Sons New York.
Søgaard, H. (1992):
Estimation of the Spatial Variation in
Evapotranspiration basedon Landsat TM and NOAA Satellite
Imagery, Geografisk Tidsskrift 92, pp 80-85, Copenhagen
Thomsen, A. (1992):
Estimation of Leaf-Area-Index (LAI) from Radiation
Measurements.ln:Proceedings from the Workshop on Remote
Sensing, Sostrup Castel, Grenå. (Eds.) Thomsen. A.,
A.Jensen & H.E.Jensen, Landbrugsministeriet, Statens
Planteavlsforsøg, Tidsskrift for Planteavls
Specialserie, Beretning nr. S 2207-1992, pp 29-36.
Wetzel, P.J. &R.H.
WoodwardYl9B7): Soil Moisture Estimation Using
GOES-VISSR Infrared Data: A Case Study with a Simple
Statistical Method, Journal of Climate and Applied
Meteorology, vol 26, pp 107-117.
G.E., D.E. Gibbons, L.M. Manned &H.P. Foote(1989):
Radiometric Calibration of Landsat Thematic Mapper
Band, Remote Sens.Environ, 28, pp 339-347.