In a humid region like Japan, it seems that the radiation term in the energy balance equation plays a more important role for evapotranspiration then does the vapor pressure difference between the surface and lower atmospheric boundary layer. A Priestley-Taylor type equation (equilibrium evaporation model) is used to estimate evapotranspiration. Net radiation, soil heat flux, and surface temperature data are obtained. Only temperature data obtained by remotely sensed techniques are used

Kai, K.

Kotoda, K.

Nakagawa, S.

Seki, K.

Takeda, K.

Yoshino, M. M.

English

NASA Technical Reports Server

PART II
Application of Equilibrium Evaporation Model
to Estimate Evapotranspiration
by Remote Sensing Technique
K. Kotoda, S. Nakagawa, K. Kai and M. M. Yoshino
Environmental Research Center
University of Tsukuba
Sakuramura, Ibaraki, Japan
K. Takeda and K. Seki
National Institute of Resources
Science and Technology Agency
Chiyoda-ku, Tokyo, Japan
INTRODUCTION
In the process of evapotranspiration, there are three things required to occur: (1) energy for the
change of phase of the water; (2) a source of the water; (3) a sink for the water. Remote sensing
can contribute information to the first two of these conditions by providing estimates of solar insolu-
tion, surface albedo, surface temperature, vegetation cover, and soil moisture content (Schmugge
and Gurney, 1982). Many methods for estimating of evapotranspiration have been studied (Rosema
et al., 1978; Soer, 1980; Carlson et al., 1981; Elkington and Hogg, 1981; Gurney and Camillo,
1982), which are reviewed by Schmugge and Gurney (1982).
In a humid region like Japan, it seems that radiation term in energy balance equation plays an
important role for evapotranspiration rather than vapor pressure difference between the surface and
lower atmospheric boundary layer. For this reason, the present study adopted a Priestley-Taylor type
of equation which is called in this paper an "Equilibrium Evaporation Model" to estimate
evapotranspiration. This method acquires the data of net radiation, soil heat flux and surface
temperature, but not necessary to get the data of vapor pressure and wind speed. Carlson et al.
(1981), Thomas and Kanemasu (1981) and Kanemasu (1982) used the GOES data to estimate daily
solar radiation. However, in the present study, such data were not available, and we used only
temperature data obtained by remotely sensed techniques.
LAND USE AND VEGETATION
Compared with continental countries, the land use of Japan is very minute and tremendously
complicated. Topography around the test area can be divided into two parts, one is diluvial upland
(altitude 25-30 m) and the other is alluvial lowland (altitude 10-15 m). The most part of the alluvial
lowland is occupied mostly by rice field_ Therefore, the vegetation of lowland is fairly monotonous
and landscape is relatively simple. On the contrary, diluvial upland is a complex of farms, residen-
tial quarters, forest, orchards and others. Most parts of the upland are occupied by farms (mainly
vegetable fields), residential quarters and towns, red pine forests and grass land.
115
https://ntrs.nasa.gov/search.jsp?R=19850014923 2020-03-22T18:39:58+00:00Z
A test study was carried out at the Tsukuba Academic New Town on 21st and 22nd Jan., 1983
during which field survey provided ground truth data for comparison with satellite (LANDSAT)
imagery and airborne sensing information. In the present study, however, the satellite image from
LANDSAT was inadequate for identifying the land use and vegetation categories required. This is
due to the facts that one is cloud cover when LANDSAT flew over the test area on 21st Jan. and
the other is due to the spectral resolution which could not provide inadequate intervegetation
differentiation.
Fig. 2-1 shows an example of the actual vegetation map of Tsukuba Academic New Town and
its surrounding districts (Nakamura et al., 1980) obtained by field survey.
RADIATION REGIME AND SURFACE TEMPERATURE
Surface temperature was measured every two minutes by a Barnes Infrared Thermometer (model
PRT-5) from 0800h to 1600h for the purpose of ground truth of surface temperature.
As shown in Fig. 3-1, it was fine on January 22nd, when the flight was done. However, there
existed fluctuations in incoming short-wave radiation (Rsl) and net radiation (Rn) from l150h to
1430h (Fig. 3-2). The fluctuations were caused by scattered cloud which shaded sunshine for a short
time interval.
Surface temperature (Ts) showed fluctuations in response to those of Rs (Fig. 3-3). Before
1100h, Ts showed a smooth increase and the fluctuations of Ts were within 2 °C. After that, it
became partly cloudy by the traveling of scattered cloud. As a result, Ts changed rapidly and
greatly. The maximum change of Ts was about 9 °C in two minutes. These large and rapid fluctua-
tions were true in the case of 10-min average Ts (Fig. 3-3).
In contrast to this, fluctuations in air temperature (Ta) were very small and responded slowly to
the changes in Rsl.
SURFACE TEMPERATURE MEASUREMENTS BY REMOTE SENSING
Remotely sensed measurements of surface temperature were made from two types of platforms:
One was a satellite, i.e. LANDSAT, the other was an aircraft. The LANDSAT offers large
coverage but suffers from reduced spatial resolution compared to aircraft. The aircraft platform was
primarily used for experimental studies because of the greater control and resolution possible.
Airborne remote sensing was conducted over the Tsukuba Academic New Town at 8:52, 12:02
and 15:09 JST on 22nd Jan., 1983. Fig. 4-1 shows a flight course and coverage area. The flight
height was about 2000 m, and the ground resolution (one pixel) was an area of 15 m x 15 m. Fig.
4-2 shows synoptic conditions on 22nd. The day was clear, and the wind was northwesterly at 1 to 4
m/s at the height of 1.6 m from the ground surface. The clearing sky provided moderately favorable
conditions for the remote sensing.
Airborne MSS information was used to estimate the surface temperatures and to give maps of
them. The surface temperatures were estimated from measurements at thermal infrared wavelengths,
i.e. the 8.0 to 14.0 micron meters, of the emitted radiant flux, and were calibrated by the ground
truth. Fig. 4-3 shows distributions of the surface temperatures at 8:52, 12:02 and 15:09 JST on the
22nd.
116
METHOD OF ANALYSIS
As described in Part I of this paper, evaporation equation can be expressed as
c¢ A -) ( Rn-G ) (1)E = --_--( A+-y
where A is the slope of the saturation water vapor pressure curve, and -y is the psychrometric con-
stant. Using the data of temperature, the values of A and y can be estimated as follows:
A = 0.4476 + (0.030669+0.000493T+0.000023T2)T
(mb"°C-1) (2)
Cp P (mb.*C-1) (3)3/- 0.622X
k = (2501-2.37T) x 103 (JKg-_) (4)
where T (°C) is the surface temperature obtained by remote sensing, Cpis the specific heat of air
(=1005 Jkg -1°C-l), P is the atmospheric pressure (= 1013 mb), and k is the latent heat of
vaporization.
The net radiation flux, the result of incoming and outgoing radiation fluxes, is given as
Rn + (1-as)Rs + _(LI-oF 4) (5)
where Rs is the incoming total short-wave radiation flux, a_ is the albedo, LI is the downward long-
wave radiation flux, T is the surface temperature, _ is the emission coefficient of the surface
(=0.98) and a is the Stefan-Boltzmann constant [(5.67 x 10 -8) Wm-2K-4].
The heat flux into the soil is proportional to the temperature gradient and the heat conductivity
in the soil depending on the soil moisture content. In this study, the following relationship was used.
G = arRn (6)
where ar is the proportional constant depend on the soil conditions. The values of as and ar vary with
surface condition and time of day. In this study daily mean values are used to estimate the
evapotranspiration. So, it is necessary to convert the momentary evapotranspiration data into 24-hr
estimates of evapotranspiration. For the practical purpose, if we know the relationships momentary
data and daily mean data, it may be estimated from the daily mean evapotranspiration.
In order to avoid the complexity of calculation, land use / land cover are classified in 8 cate-
gories. Using the data of Rs = 180 Wm-2, LI = 403 Wm-2 which are the typical summer
climatological conditions at Tsukuba, and assuming T = 20 °C, ot = 1.0, evapotranspiration from
various surface covers can be calculated (Table 5-1). It seems that the results are reasonable com-
pared with the lysimeter data obtained in August, 1979 (Kotoda, 1980) and the data of lake evapora-
tion obtained at Lake Kasumigaura in August and September 1981 (Takeda et al., I982).
117
CONCLUDING REMARKS
Using the surface temperature obtained by airborne remote sensing and other necessary data
measured at experimental field at ERC, University of Tsukuba (Kotoda et al., 1983), evapotranspira-
tion from various surfaces were calculated. The result (point A) is shown in Fig. 6-1 with other data
of evapotranspiration from grass land which were measured by lysimeter at ERC. Calculated daily
mean evapotranspiration on 22nd Jan., 1983 is approximately 0.4 nun/day, however the differences
of evapotranspiration with the vegetation types were not detectable, because the magnitude of
evapotranspiration is very little in winter.
The future subject of remote sensing techniques to the estimate of evapotranspiration will depend
on establishing correlations between land surface reflectance and hydrometeorological parameters
related to heat balance terms, such as Rn, H or G. As a satellite monitoring, more frequent opera-
tional satellite, like a geostationary satellite is desired, because the present LANDSAT can operate
nothing but one or two days measurement per month.
The future use of remote sensing techniques to the estimate of evapotranspiration will depend on
establishing correlations between land surface reflectance and hydrometerological parameters related
to heat balance terms, such as Rn, St or G. As for satellite monitoring, more frequent operational
satellite coverage, like geostationary, is desired, because the present LANDSAT can operate nothing
but one or two days of measurements per month.
118
Figure 2-1. An example of the actual vegetation map of Tsukuba academic New town and
its surrounding districts. E • ERC
W/mZ "C
600 20
1983 JAN. 22 1983 JAN, 22
o-.o Rsl Ts
o--e Rst
500 _ LI u_
_ LI =:
Z o--o Rn 15
O 400
_- 300 =: lo
I i '
< uJ
-- 200 a.
O 5
uJ
< 100 _-
€€
0 0
I I I i I I I
-100 -3 ' 10 12 14 IB
6 12 18 24 h
T I M E T I M E
Figure3-1. Diurnalvariationsof radiationbalance Figure 3-2. Hourly variationsof surfacetempera-
com )onents observed at the Aerological Obser- ture (Ts)and air temperature (Ta)at ERC. Broken
vatory, Tateno. line in Ts andTa are 10-min averagedvalues. Ta
Rs ! : incoming short-wave radiation was observed at a height of 1.6 m.
Rst : reflected short-wave radiation
L_ : downward long-wave radiation
L! : upward long-wave radiation
Rn : net radiation
|
400 1983 JAN. 22 ]
!
Rsl
o 300
< 2o0
o
10o
-5O
6 10 12 14 16 h
T I M E
Figure 3-3. Hourly variations of incoming short-
wave radiation (Rsl) and net radiation (Rn) at
ERC. Rs! and Rn are 10-min averaged values.
120
ERC =Environmental Research Center
UT =University of Tsukuba
NRCDP=National Research Center for Disaster Prevention
_ iGSI =Geographical Survey Institute
AO =Aerological Observatory
MRI =Meteorological Research Institute
NIES =National Institute for Environmental studies
NIAS =National Institute of Agricultural Sciences
\
.
:_ L. KAS UMIGAURA
¢
Figure 4-1. Flight course and coverage area.
..! )_sr___,_._
,..-_'---_83J_ 22isJsT!
(a) (b)
Figure4-2.Synopticonditionson 22nd January,1983.
(a) Surface weather map at 15 JST 22 January, 1983
(b) GMS VIS imagery at 15 JST 22 January, 1983
122
oc (a) 8:52 JST
°c (b) 12:02 JST
i/
q.q
Figure 4-3. Surface temperature distributions at 8:52, 12:02 and 15:09 on
22nd, January, 1983
123
F,J
'79.5/4 '82.5/15 '79.5/22 '81.6/16 0 20 40 60 80 100 120 140 160 180 200
Photo 1. Superimposed Image of Snowcover Areas Photo 2. Maximum Snow Water Equivalent Map of
Extracted from Landsat Data Okutadami-gawa Bain in 1982
'82.4/25 5/2 5/9 5/16 5/23 5/30 '82.4/25 '82.5/22
Photo 3. Snowcover Daily Variation Map Estimated From the Photo 4. Snowcover Areas Extracted
Maximum Snow Water Equivalent Map Shown in Photo 2 From NOAA-AVHRR Data
Table 5-1
Evaportranspiration from various surfaces
NO. categories a_ a_ Rn G AE EWm-_ mmd-I
1 open water 0.06 0.20 153.8 30.8 84.1 2.96
2 urbanized area 0.30 0.30 110.6 33.2 52.9 0.47
3 tall vegetation 0.15 0.08 137.6 ii.0 86.5 3.05
(ever green)
4 tall vegetation 0.15 0.08 137.6 ll.0 86.5 3 05
(deciduous)
5 short vegetation 0.18 0.i0 132.2 13.2 81.3 2.86
(sparse)
6 short vegetation 0.15 0.05 137.6 6.9 89.4 3.15
(dense)
7 bare soil (dry) 0.25 0.30 119.6 35.9 57.2 2.02
8 bare soil (wet) 0.20 0.30 128.6 38.6 61.5 2.17
Value of Y
!
_- •
j • •
I o •e •
4.0 I'- "- '"
I " • oi
i I _IQ-
r-
_I i ° • --
e _I o .
E i • - 3
E 3.0 F- " " "
I olinZ u
O 1 " '" "
I-- 1 " " ".
<
2.0 ; " - -, • N
O_ i
I • o o = • •
I
I'lr" t • o
O I "o 3 .o • e
n 1.0 "- I.X I ' "m j o m ee • •
• o| • •
I o
; -
0;_ I I I 1 I I I
0 2 4 6 8 10 12 14
NET RADIATION ( MJ m-1 day -1 )
Figure 6-1. Relationship betweenevapotranspiration and net radiation. The value
of point A is estimated evapotranspiration observed on 22nd, Jan., 1983 and the
others are actual evapotranspiration obtained by lysimeter at ERC, Univ. of
Tsukuba through the year of 1982 except rain day.
126
REFERENCES
Carlson, J. N., Dodd, J. K., Benjamin, S. G. and Cooper, J. N. (1981): Satellite estimate of the
surface energy balance, moisture availability and thermal inertia. J. Appl. Meteorol., 20, 67-87.
Elkington, M. D. and Hogg, J. (1981): The characteristics of soil moisture content and actual
evapotranspiration from crop canopies using thermal infrared remote sensing. Proc. Remote
Sensing Soc., Reading.
Gurney, R. J. and Camillo, P. J. (1982): The effects of soil and atmospheric boundary layer
variables on evapotranspiration and soil moisture studies. The First U.S.-Japan Evapotranspira-
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Kanemasu, E. T. (1982): Use of satellite information to estimate evapotranspiration. The First U.S.-
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Kotoda, K. (1980): Comparison of evapotranspiration measurement using weighing lysimeter and
energy balance method. Bull. Environmental Research Center, Univ. of Tsukuba, 4, 1-9. (in
Japanese)
Kotoda, K., Kai, K. and Nakagawa, S. (1983): On the real time data processor for meteorological
and hydrological measurements. Bull. Environmental Research Center, Univ. of Tsukuba, 7,
75-85. (in Japanese)
Nakamura, T., Namekawa, H. and Chow, K. (1980): The actual vegetation map of Tsukuba
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Rosema, A., Bijleveld, J. H., Reiniger, P., Tassone, G., Blyth, K. and Gurney, R. J. (1978):
TELL-US, a combined surface temperature, soil moisture and evaporation mapping approach.
Proc. 12th ERIM Symp. on Remote Sensing of Environm. Manila, 2267-2276.
Schumugge, T. and Gurney, R (1982): Evapotranspiration and remote sensing. The First U.S.-Japan
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reflected radiation. Remote Sens. Env., 11, 157-167.
127


Estimation of Regional Evapotranspiration Using Remotely Sensed Land Surface Temperature.  Part 2:  Application of Equilibrium Evaporation Model to Estimate Evapotranspiration by Remote Sensing Technique

Estimation of Regional Evapotranspiration Using Remotely Sensed Land Surface Temperature. Part 2: Application of Equilibrium Evaporation Model to Estimate Evapotranspiration by Remote Sensing Technique

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