There are three things required for evapotranspiration to occur: (1) energy (580 cal/gm) for the change of phase of the water; (2) a source of the water, i.e., adequate soil moisture in the surface layer or in the root zone of the plant; and (3) a sink for the water, i.e., a moisture deficit in the air above the ground. Remote sensing can contribute information to the first two of these conditions by providing estimates of solar insolation, surface albedo, surface temperature, vegetation cover, and soil moisture content. In addition there have been attempts to estimate precipitation and shelter air temperature from remotely sensed data. The problem remains to develop methods for effectively using these sources of information to make large area estimates of evapotranspiration

Gurney, R.

Schmugge, T. J.

English

NASA Technical Reports Server

EVAPOTRAi_SPIIIATIOI._ and REMOTE Sk_iSI!IG
by
T.Schmugge and R. Gurney
I;AZA/GODDA!ID SPACE l_LIGiiTCEI',TER
Creenbel_, Haryland 2C77
ADDTRACT
Evapotranspiration is the process in which water in the
liquid phase is extracted from the soil and transferred to the
atmosphere in the vapor phase. This transfer can occur as
evaporation directly from the soil surface or transpiration from
plant leaves. There are three things require_ for the process to
occur: I) energy (580 cal/gm) for the change of phase of the
water; 2) a source of the water, i.e. adequate soil moisture in
the surface layer or the in the root zone of the plant; and 3) a
sink for the water, i.e. a moisture _eficit in t_e air above the
grounG.
Remote sensing can contribute information to the first two of
these conditions by proviaing estimates of Solar insolation,
surface albedo, surface temperature, vegetation cover, and soil
moisture content, in addition there have been attempts to
estimate precipitation and shelter air temperature from remotely
sensed data. The problem remains to develo_ mezhods for
effectively using z_ese sources of information to make large area
estimates of evapotranspiration.
8
https://ntrs.nasa.gov/search.jsp?R=19830020245 2020-03-21T02:02:10+00:00Z
INTRODUCTION
Evaporation and transpiration (evapotranspiration or ET)
estimates are required for several purposes, includin_ irrigation
scheduling, water balance calculations, run-off prediction and
me_eoroloLical ana climatological studies. Long-term estimates of
evapotranspiration may be made using water balance methods, as in
lysimetry or evaporation pans, or at a larger scale in river
basins, ilowever, there is considerable uncertainty in these
measurements, particularly for short time periods, and it is often
difficult to generalize the measurements to large areas. Many
areas do not have any hydrologic measurements and for these areas
Thornthwaite (1947) suggested an empirical approach to estimate
long-term evaporation from routine meteorological observations,
principally monthly mean temperature. Penman (1948) derived a
more p_ysically-based expression which uses standard
meteorological data to estimate potential evapotranspiration.
Trois was for a short grass cover with an adequate water supply.
This moeel has served as the theoretical basis for most of the
currently used models, iiowever their application is still limited
to locations where standard meteorological data are available. In
principle, rez,otely sensed measurements, offer methods for
extending these models to much larger areas including those where
data nay be sparse. Although no method is as yet operational the
utility of such measurements is sufficiently great Zo encourage
further work.
To understand better how remote sensing may contribute it is
instructive to examine first the basic energy and moisture balance
equations.
Energy and Moisture Balance
The energy balance of a soil or ve£ezazed surface is 6overne_
by a conservazion equation. In the absence of precipitaZion or
advection, we may write
Rn + G + II + LE = 0 (I)
where Rn is the net radiation flux, G the soil heat flux, and h
the,sensible heat flux and LE .the latent heat flux in the
atmospheric boundary layer. The latter is the product of the
_: and the latent heat of vaporization of waterwater vapor flux, ,
per unit mass, L.
The net radiation flux is the sum of the incoming and
outcoin_ short and long wave radiation fluxes:
q
[: = ( 1 - _._) :-_s + eL ([{I. - _-5'c ) ( 2 )
where Hi is the incomin 6 short-wave radiation and R L is incozlin6
long-wave radia_ipn, o_is Zhe shorZ-wave soil/crop reflectivity or
albedo, eL is _he long-wave emissivity, 0-- is the Btefan-Boltzrnan
constant and Tc is the soil surface temperature in Eelvins.
The soil heat flux is related to the temperature gradient in
the soil dT/dz and to the thermal conductivity
10
This relationship leads to a form of the diffusion equation
d(,A_'Z/dz)/dz = Cv d'_-Id-_ (4)
where C V is the volumetric heat capacity of the soil. This
equation may be solve numerically to give a temperature profile in
the soil, taking either Tc or G as the upper boundary condition
and some known temperature at a given depth in the soil as, the
lower boundary condition.
The sensible and latent heat fluxes may both be estimated
from the transport equations. The sensible heat flux li may be
expressed as
= (T .- " )/r.. (S)
where ? is theair density, Cp is the specific heat of the air and
r_ is the aerodynamic resistance. The latent heat flux may be
exFressed as
where _ is the psyohrometric constant, e_ is the atmospheric vapor
pressure in the boundary layer, e_ is the saturated vapor pressure
at the temperature To, and r5 is the stomatal diffusion
resistance to water vapor transport.
11
Equations (2), (_), (5) and (6) are a mathematical model of
the surface energy balance in terms of the soil surface
temperature and a set of meteorological measurements, which at
least in principle, are measured or estimated routinely. The
energy and moisture fluxes are intimately related because the
latent heat flux L_ives the az_ount of water evaporate_ and
transpired.
One formulation of potential evapotranspiration that lends
itself to remo_e sensing inputs is that developed by Priestly and
Taylor (1972). They obtained for saturated surfaces
Lz :c<[s/(s - Q)
where s is the slope of vapor pressure versus temperature curve,
and o< is an empirical evaporation constant which they determined
to be I.26. Barton (1979) and Davies and Allen (I975 ) have
modified the result for unsaturated surface by treating G<,as a
function of she surface layer soil moisture. _arton used airborne
microwave radiometers to remotely sense soil moisture in his study
of evaporation from bare soils and grasslands.
Remotely Sensed Measurements
Several types of remotely sensed observations may be made by
measuring _he electromagnetic radiation in a particular wavebana
either reflected or emitted from _he earth's surface. The
incoming solar radiation can be estimated from satellite
12
observations of cloud cover primarily from _eosynchronous orbits.
For clear sky concitions, the surface albedo may be estimated by
measurements covering the entire visible and near-infrareu
wavelengths, while measurements at narrow spectral bands can be
used to determine vecetative cover. The surface temperature can
be estimated fro_ measurements at thermal infrared wavelengths,
i.e. the I_.5 to 12.5 micron waveband, of the emitted radiant flux
and from some estimate of the surface emissivity (for actual
surfaces e is usually close to unity). The microwave emission and
reflection or backscatter from soil, primarily for wavelengths
between 5 and 21 cm, is dependent on the dielectric properties of
the soil which are strong functions of the soil moisture content.
There are uncertainities in the determination of soil moisture
values from microwave measurements introduced by factors such as
surface roughness and vegetative cover but it appears that
microwave methods can estimate the soil moisture content in the
surface 5 cm layer of the soil with 4 or 5 levels of
discrimination.
Remotely sensed measurements may be made from a variety of
piatforms. Operational work is largely conducted using
satellites, which offer repetitive coverage but suffer from
reduced spatial resolution con_pared to aircraft. Currently only
visible ano infrared data are available from satellite. Aircraft
platform, s are i_ri_iarily used for experin_ental stuuies because of
the Lreater control possible.
Applications of Remotely Sensed Measurements
13
In recent years there has been much progress made in the
remote sensing of a number of parameters which can contribute to
the estimation of evapotranspiration. These include: surface
temperature, surface soil moisture, surface albedo, vegetative
cover, and incoming solar radiation. There has been little
progress made in the direct remote sensing of the atmospheric
parameters which affect evapotranspiration such as: surface air
temperature, water vapor gradients, and surface winus. Thus
approaches for evapotranspiration estimation using remotely sensed
data will have to work around these missing data.
The evaporation from the soil surface is directly related to
the vapor pressure difference between the surface and lower
atmospheric boundary layer, and the water supply in the soil.
Assuming that there is a suitable supply of water in the soil, the
most important controlling factor is the difference [e_ - e s ] in
equation (6), which is largely determined by the corresponding
_em_era_ure difference. Thus there is a strong relationship under
certain conditions between the canopy and air temperature
differences and the evapotranspiration rate. This comparison of
canopy and air temperatures has been used to detect crop water
stress for irrigated crops in the southwestern part of the United
States ana these relationships have been described in a series of
papers from the group at the US Water Conservation Laboratory in
Pl_oenix (Jackson et al., 1981 and references cited _herein).
Estimates of the net radiation from geostationary satellite
data are used by Kanemasu (1902) in _ Priestly-Taylor type of
equation to estimate evapotranspiration. The resulting moisture
14
flux is then used to drive a water balance model for predicting
crop yields. They also used Landsat multispectral data to obtain
vegetation indices.
A more complete but less practical approach is that aaopted
by Goer(1980), [_osema et al. (1978), and Carlson (19_I), amongst
others, in which1 the energy balance (equations I - 6) is modelled
and t_le moisture balance is inferreU from the heat flow in the
soil. Equations (2), (4), (5), and (6) can be manipulated to
obtain an expression for the surface energy balance in terms of
the surface temperature and a set of meteorological measurements
which, at least in principle, are measured or estimated routinely
The initial estimate of the soil/crop surface temperature T will
almost certainly not satisfy the continuity requirement of
equation (I) because of numerical simplifications and so it is
necessary to use an iterative numerical optimization technique to
ensure that the condition in equation (I) is satisfied. The
technique may be adapted to give estimates of the surface
temperature T C at every time step at which routine meteorological
observations are available. The gergra (Soer, 1980) model uses a
Businger-Dyer (Eusinger et ai.,1971 and Dyer, i967) approach to
estimate ¢he turbulent diffusion resistance r_. This gives r_
as a function of wind velocity and the stability of t_e
atmospheric boundary layer just above the surface. A simple
emgirical parameterization of the stomatal resistance _ and an
explicit finite _ifference soil'heat flux model are used.
The Tellus _:odel (ilcsema, et al., 157_) is somewhat similar
|
except that the model is calibrated against both a daytime and a
15
nlghtime surface temperature. A Newton-Haphson numerical
integration scheme is used to optimize two parameters: the surface
relative humidity , effectively related to the resistance to water
transport across the surface boundary layer, and the thermal
inertia of the soil. The finite difference scheme uses an
expanding _rid with thinner soil layers near the surface (Kosema
et al.,1978). This model has been used to create look-up tables,
which were used with airborne thermal infrared surface temperature
data and albedo estimates to _ive maps of cumulative daily
exapotranspiration (Dejace et al., 1979). Other studies (Gurney
1978) have shown that the TerGra and Tellus models £ive results
relatively close to those estimated or measured by other means.
Carlson (1981) developed a similar model, with a simple soil heat
flux component. Using satellite data from the Heat Capacity
Mapping Mission he obtained reasonable results under non-stressed
conaitions from vegetated surfaces. Elkin_on and i{o_(1981) used
an approach based on the Tergra model to estimate
evapotranspiration, ilowever _hey usd a simple set of inputs ,
estimating net radiation from the Brunt equation and the other
boundary conditions from routine meteorological observations taken
at nearby standard weather stations. Their results are
sufficiently encouraging to suggest that the simplifications whict_
I
[ i
would be required for an operational approach are indeed possible.
Bernard et a1.(19_I) in a simulation study have examined the
use soil moisture estimates from a radar to calculate
evapotranspiration rates. The microwave estimates of the surface
soil moisture were used as the upper boundary condition in a water
16
balance model. The study provides a good indication of the
utility of microwave soil moisture sensing for estimating
evapotranspiration.
A more physically based and realistic model has been
developed by Camillo and Dchmugge (1951). Both energy and
moisture fluxes are modelled. The space-time dependence of
temperature and moisture content are described by a set of
diffusion type partial differential equations, and the model uses
a predictor/corrector method to numerically integrate them,
yielding both moisture and temperature profiles in the soil as
functions of time. The model was used to simulate energy and
moisture fluxes under field conditions using data from Phoenix,
Arizona. The qualitative agreement between the observations and
the model was very good, indicating that the model is a reasonably
accurate representation of the physical processes involved. A
further example of the use of the model is given in Gurney and
Camillo (1982). This model, because it simulates the moisture
profile explicitly in addition to the temperature profile,
requires an initial moistureprofile. At least in principle this
may also be derived usin_ remotely sensed microwave measurements.
As the model is most sensitive to changes in the surface soil
temperature and soil moisture, which is that part of the profile
accessible to t_e microwave t_ermal infrared n:easurements, this is
a very realistic application of remotely sensed data. If it is
possible to take initial conditions from remotely sensed
measurements and the boundary conditions either froz_ routine
meteorological observations or remotely sensed measurements, the
17
estimation of actual evapotranspiration from remotely sensed
measurements becomes feasible.
There are various other measurements which have either been
suggested as inputs ¢o evapotranspiration estimates or in
princiI_le could be used as additional model inputs. Multispec%ral
information may be used to estimate the health of vegetation and
the biomass (Tucker, 1980, Holben, et al.,1980, and Kanemasu,
1982) and hence to give an indication of the evapotranspiration
rate.
The major problems with all remote sensing methods of
evapotranspira¢ion are: I) the process of transpiration is still
not well understood and parameterized for structured crops such as
cereals or complex vegetation_ such as trees; 2) in the presence
of vegetation the surface temperature Te estimated by a thermal
infrared sensor is at an unknown level within the vegetation; and
3) the most appropriate use of microwave observations of surface
soil moisture in the presence of vegetation needs to be
determined. }'or the future we expect that the most practical
method will probably use a multi-spectral approach including
reI_etitive observations at the visible, near and thermal infrared,
and microwave wavelengths. This will afford the possibility of
estimating solar insolazion, surface vegetazive cover and/or
albedo, surface temperature, and surface soil moisture from
remotely sensed data and incorporating them into models of the
type _escribed here.
AC KNOW LEDGEMENT
18
_
°
REFERENCES
Barton,I.J., 197_. A case study comparison of microwave radiometer
measurements over bare ana vegetated surfaces. J. Geophys.
Res.,b3:9513-3517
Bernard,R., [_. Vauclin, aria D. Vidal-Madjar, 1981. Possible use
of active microwave remote sensing data for prediction of
regional evaporation by numerical simulation of soil water
movement in the unsaturated zone. Water Resources Res.,17:
1603-I 610.
Businger, J.A., J.C. Wyngaard, Y. Izumi,and E.F. Bradley, 1971.
Flux profile relationsaipsin the atmospheric surface layer.
J. Atmos. Sci.,28: 181-189.
Camillo, P. and T.J. Schmugge, 1981. A computer program for tae
simulation of heat and moisture flow in soils. NASA Technical
Memorandum TN-62121.
Carlson, T.h., J.k. Doad, S.G. Benjamin and J.N. Cooper, 19_I.
Remote estimation of surface energy balance, moisture
availability and thermal inertia over urban and rural areas.
J. Appl. _leteor.,20: 67-87.
Davies, J.A. and C.D. Allen, 1973. Eqquilibrium, potential and
actual evaporation from cropped surfaces in southern Ontario.
J. Appl. _,_eteor.,12: 6#9-657.
Dejace, J., J. r[egier, M. Mohl, G. _aracci, P. Reiniger, G.
2O
Tassone, J. Huygen, 1979. Mapping thermal inertia, soil
moisture and evaporation from aircraft day and night thermal
data. 13in ERIM Symposium on Remote Sensing,
Manila:t015-1024.
Dyer, A.J.,1967. The turbulent transport of heat an_ water vapor
in an unstable atn_osphere. Quart. J. Roy. Me_eor.Soc.,93:
501-508.
Elkington, M.D. and J. Hogg, 1981. The characteristics of soil
moisture content and actual evapotranspira_ion from crop
canopies using thermal infrared remote sensing. Proc. Remote
Sensing Soc., Reading.
Gurney, R.J. and P.J. Camillo, 1982. The effects of soil and
atmospheric boundary layer variables on evapotranspiration
and soil moisture studies. This meeting.
Gurney, H.J., 1978. Estimation of soil moisture content and
actual evapotranspiration using thermal infrared remote
sensing. Proc. Remo_e Sensin6 Society: 101-109.
}lolben, B._., C.J. Tucker, and C.J. Fan,1950. Spectral assessment
of soybean leaf •area and leaf biomass. Pho_ogramme_ric
Engineering and Remote Sensing,46: 651-656.
Jackson, I<.D., _.B. Idso, R.J. Reginato,and P.J. Pinter, 1961.
Canopy temperature as a crop water stress• indicator. Water
Eesources I_esearch,17:1133-113_.
Kanemasu, E.T., 1982. Use of satellite information to estimate
21
evapotranspiration. This meeting.
Penman, }i.L., 1948. _atural evaporation from open water, bare
soil and Brass. Proc. Roy. Soc., London,At93: 129-145.
Price, J.C.,1930. The potenial of remotely sensed thermal
infrared aata to infer surface soil moisture and evaporation.
Water _iesources Reaearch,16: 787-795.
Priestly, C.II.B. and R.J. Taylor, 1972. On the assessment of
surface heat flux and evaporation using large-scale
parameters. Eon.Wea.Rev.,100: 81-92.
Rosema, A., J.li. Bijleveld, P. Reiniger, G. Tassone, R.J. Gurney,
K. Blyth, 1978. Telius, a combined surface temperature, soil
moisture and evaporation mapping approach. Proc. 12th ERIE
Symp. on Remote Sensing, Ann Arbor, pp10.
Soer, G.J.R., 1980. Estimation of regional evapotranspiration and
soil moisture conditions using remotely sensed crop surface
temperatures. }_emo_e Sensing of Environment,9: 27-45.
Thornthwaite, C.W., 194_. An approach toward a rational
classification of climates. Geophysical Review,38: 55.
Tucker, C.J., 1980. Remote sensing of leaf water content in the
near-infrared. Remote Sensing of Environment,tO: 23-32.
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Evapotranspiration and remote sensing

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