**Develop A Remote Sensing Tool To Estimate Evaporative
Loss From Reservoirs**

**Project Number:****W-06-9**

**P****rincipal Investigator: Theodore W. Sammis, New
Mexico State University at **

**Co-PI’s: Junming Wang and Vincent P.
Gutschick, New Mexico State University,
**

**Introduction**

According to the ^{3}). Evaporation from the reservoir is
estimated to be as much as 1/3 of the approximate average inflow, which would
be approximately 250,000 acre-feet per year (280 million m^{3} per
year) (Herting , et al. 2004).

Due to drought conditions
from 1992 to 2002,

Reservoir evaporation measurements from an inflow–outflow water
balance method, pan measurement method, or eddy covariance method are time- and
labor-intensive (Herting , et al. 2004; Sammis , et al. 2004; Linacre 1994).
Additionally, the accuracy of the water balance method may be affected by bank
storage and release, silting, percolation, and precipitation (Heather , et al.
2004). Pan E measurements assume that E is uniform over the entire surface area
of the reservoir and the pan / reservoir evaporation coefficient is
correct. Pan E measurements are affected
by extra heat conductance through the walls of the pan (Herting , et al. 2004; Linacre 1994) which results in a reservoir pan
coefficient around 0.7. The accuracy of the eddy covariance measurements can be affected

by environmental factors such as fetch distance and wind
direction, etc. In addition, a point E measurement
cannot provide accurate spatial estimates over reservoirs that exhibit underwater currents,
upwelling, and significant changes in depth. Furthermore,

__ __

*Remote Sensing Methods To Estimate ET*

Different methods have
been developed to estimate spatial ET on land or water, based on satellite data
(Courault , et al. 2003). There are two main methods: direct and
indirect. Direct methods mainly use thermal infrared data (TIR) and the energy
budget equation. Indirect methods use the assimilation procedure and
Soil-Atmosphere Transfer models. These methods use different wavelength data
and obtain ground surface characteristics such as albedo, emissivity, and leaf
area index (Courault , et al. 2003).

*Direct **M**ethods*

The Direct Simplified
Methods, which are empirical methods, are often used to estimate ET. The
methods assume the daily ET is linearly related to the cumulative temperature
difference, T_{s}-T_{a }(surface temperature minus the air
temperature) (Courault , et al. 2003). On a local scale, accuracy could reach 85–90% (Steinmetz , et al. 1989). However, if the method is used at the regional
scale, the accuracy will be around 70–80% because the input parameter (air
temperature) must be interpolated from local measurement and the satellite
remote measured surface temperature may be as must as 1.5- 2 degrees C in
error.

SEBAL is a method of computing
E as a residual in the energy budget. It
was developed by Bastiaanssen , et al. (1998). It combines
empirical and physical parameterization. Inputs include local weather data
(mainly wind speed) and satellite data (radiance). From the input data, the R_{n}
(net solar radiation), NDVI (normalized difference vegetation index), albedo,
roughness length, and G (soil heat flux) are calculated. The sensible heat flux
is calibrated by contrasting two points (wet, well-irrigated vegetation and dry
ground). Then the ET is calculated as the residual of the energy budget (Bastiaanssen , et al. 1998). The accuracy can be 85% on a daily basis and
95% on a seasonal basis (Bastiaanssen , et al. 2005) at a single site. Based on the contrast between
wet and dry areas, similar models such as SEBI, -S-SEBI and SEBS were developed
(Menenti and Choudhury
1993; Roerrink , et al. 2000; Su 2002).

In the residual models,
a two-source model (Kustas and Norman 2000) divides the energy
calculation into two parts. One is the canopy and the other is the soil. The
model estimates ET with an accuracy of about 90% (Kustas and Norman 2000). However, the model is
more complicated than SEBAL, and accurate surface temperature data are needed, which
requires better atmospheric correction algorithms than are currently available.

*Indirect **M**ethods*

Indirect methods deal
with soil and plant energy exchange with the atmosphere with a fine time step
of 1 s to 1 hr (Courault , et al. 2003). Indirect methods accurately describe crop
functioning, and can allow access to intermediate variables such as soil
moisture and LAI (leaf area index), which are related to the physiological and
hydraulic processes that can be linked to other meteorological and hydrologic
models (Courault , et al. 2003)**.**

**Research Objectives**

The general objective of the project is to develop a remote
sensing tool to estimate evaporation (E) loss from reservoirs to aid US-Mexico
border water delivery management.

**Research
Methodology/Approaches**

A Remote Sensing ET (RSET) model for plants was developed and
validated (Wang , et al. 2005). This model was
developed to use ASTER data and has a
90-m resolution of ET output.
Because ASTER data are available only on a 16-day time scale, the model was
modified to use the MODIS L1B data to output 1-km land surface ET on a daily
time scale.

The following material first briefly describes the RSET
model. Then, the modification for MODIS L1B data is described.

*RSET **Model Theory*

The RSET model general
flowchart is shown in Figure 2. The model
inputs are satellite data (ground surface reflectance and temperature) and
local weather data (solar radiation, humidity, and wind speed). ASTER provides
temperature and reflectance data. The model then calculates NDVI, the soil heat
flux (G) and sensible heat flux (H). Finally, the model outputs the spatial ET
(mm d^{-1}) according to the energy budget equation.

The method uses the
energy budget equation to calculate each pixel (instant latent
heat loss) at the time of the satellite overflight.

(1)

where lET_{ins} is the
instant latent heat loss (W m^{-}^{2}), l is the
latent heat of vaporization of water (J kg^{-}^{1}), ET_{ins}
is the instant hourly evapotranspiration (mm hr^{-}^{1},^{ }or kg hr^{-}^{1}^{ }m^{-}^{2}), which is calculated as a residual of the energy budget, R_{n}
is net solar radiation (W m^{-}^{2}), G is soil heat flux into the soil (W m^{-}^{2}), and H is sensible heat into the air W m^{-}^{2}); R_{n} is calculated according to the
local solar radiation data (R_{s}, W m^{-}^{2}) (Walter , et al. 2002).

(2)

Here, R_{nl}
is net long-wave radiation (W m^{-2}), a is albedo, and a is
calculated by the equation in (Liang 2000)
from ASTER surface reflectance data.

(3)

Here, a_{i} is the reflectance for ASTER data band i.

According to (Walter , et al. 2002),

(4)

where T_{s} is the mean absolute
surface temperature (K), which is obtained from the satellite data, s is theStefan-Boltzmann constant (2.042x10^{-10}
MJ K^{-4} m^{-2 }hr^{-1}), and

e_{a} is the actual vapor pressure (kPa),

(5)

Here, e_{s}(T_{a}) is
saturation vapor pressure (kPa), T_{a} is air temperature (ºC) and RH
is the relative humidity (%).

(6)

(7)

dT is the difference
between surface temperature and air temperature (K, equation 15).

Based on (Bastiaanssen , et al.
1998),

(8)

where c is a
coefficient

Based on data from
Clothier , et al. (1986), Choudhury (1989), Kustas and Daughtry (1990), Van Oevelen (1993) and Bastiaanssen , et al. (1998), the following equation
was obtained
(R^{2} =0.66, Figure 3).

(9)

where NDVI is the normalized difference
vegetation index calculated from satellite data.

NDVI is
calculated as the following:

(10)

where α_{3} and α_{2} are the reflectance data of
bands 3 and 2 respectively.

For the
sensible heat flux calculation, two pixels are chosen from the satellite data.
One pixel is a wet pixel that is a well-irrigated crop surface with full cover
and the surface temperature (T_{s}) is assumed to be close to air
temperature (sensible heat flux H is assumed to be 0). The second pixel is a
dry, bare agricultural field where lET_{ins} is
assumed to be 0. The two pixels tie the calculations for all other pixels
between these two points.

At the dry
pixel, assume lET_{ins}=0; then,
according to equation 1,

(11)

Then
we can get dT_{dry}, which is dT at the dry spot (K) according to the
following equations (12-14),

(12)

Here, r is the air density (mol
m^{-3}), c_{p} is the specific heat of air (29.3 J mol^{-1}
K^{-1}), dT is the near surface temperature difference (K), and r_{ah}
is the aerodynamic resistance to heat transport (s m^{-1}), where

(13)

Here, z_{1} is a height just above the zero plane displacement
height of a plant canopy, set to 0.1 m for each pixel, z_{2} is the
reference height just above the plant canopy, set to 2 m for each pixel, u* is
the friction velocity (m s^{-1}), and k is the von Karman constant
(0.4). We calculate u* from the observed
windspeed as

(14)

where u(z) is the wind speed at height of z, d is the zero plane displacement
height (m), and z_{m} is the roughness length (m) (Campbell and Norman 1998). The calculations of these two variables are described
in the paragraph after Eq. (15). From Eqs. (11-14) and the input data, dT_{dry}
can be calculated. At the wet spot, we assume H=0 and dT_{wet}=0 (dT at
this wet spot). Then according to the surface temperature at the dry and wet spots
(T_{s,dry} and T_{s,wet}, both in K), we can get one linear
equation for each pixel,

(15)

With these calculated values of dT,
the H at each pixel can be calculated according to Eqs. (12-14). The value of u* can be
solved for each pixel by assuming that at 200 m above the surface the wind
speed is the same. The wind speed at 200
m can be calculated from the weather station data (Equation 14). For example, assuming that
the wind speed (u(z)) and measurement height (z), roughness length (z_{m})
and zero plane displacement height (d) are given at a weather station, Eq. (14) can be solved for u* at
the weather station. Then, the wind speed at 200 m can be solved by using Eq. (14) again, given u_{*}, z=200 m, z_{m }and
d. The parameter d in Eq, (14) for other pixels is set to 0
because it is negligible when z=200 m. The z_{m} for each pixel is
calculated by a regression equation according to the pixel NDVI value. The
equation is obtained by fitting z_{m} to a linear equation in NDVI. For
example, if we know that pecan has z_{m} =1.2 m and NDVI =0.57, alfalfa has z_{m} =0.07 m and NDVI
=0.42, and a bare agricultural field has z_{m} =0.003 m and NDVI =0.18,
then we can obtain a regression equation for z_{m} (Figure 4).

Because atmospheric stability may have effects
on H, an atmospheric correction is conducted (Figure 5). First, u^{*} and
wind speed at 200 m at the local weather station are calculated. Then, z_{m},
u^{*}^{,} and dT
for each pixel are computed. The values of r_{ah }and H without the
atmospheric correction can then be obtained.

For atmospheric correction, the stability
parameter, or Obukhov length, L (m), is calculated. Using the
stability parameter, u^{*}, r_{ah}, and H are corrected. Then an
iteration is conducted for the L, u^{*}, r_{ah}, and H calculations until H
does not change more than 5%.
The correction equations are as follows (Campbell and Norman 1998; Stull 2001).

(16)

When L<0, H is positive and heat
is transferred upwards under unstable conditions; when L>0, H is negative
and heat is transferred downwards under stable conditions; when L=0, no heat
flux occurs under neutral conditions. Because the daytime satellite overflight
occurred at local noon time at our

The momentum correction term is

for L=∞ (17)

for L<0 (18)

(19)

(20)

We use z=200
m and d is taken as negligible (d=0).

The
correction term for the heat transfer is

for L<0 (21)

for L=∞
(22)

(23)

After H
is corrected by the atmospheric effects, lET_{ins} for each pixel is calculated using Eq. (1). The daily ET (ETdaily, mm d^{-1}) is calculated by assuming that the evaporative fraction
is constant over the day:

(24)

where ET_{r,daily }is the daily reference
ET for well-irrigated alfalfa. The ET_{r,daily} can be obtained from the FAO Penman-Monteith equation
(weather.nmsu.edu). The value of lET_{ins} (W m^{-2}) is the instantaneous lET for a
well-irrigated alfalfa field calculated from Eqs. (1-8), using a=0.23,
c=0.04, and T_{s}= T_{s,wet}).

*Model **T**heory
for MODIS **D**ata*

The MODIS model basically used all the same equations and
algorithms as for the ASTER except for NDVI, albedo, temperature and
reflectance calculations. For ASTER data, NASA provides the surface temperature
and reflectance products already.
Therefore, they were not calculated in the ASTER model.

Although MODIS data provides reflectance
and temperature data, the products have geo-registration problems, in which a
pixel in a product may not correspond to the pixel in another product even
though the geo-location (longitude and latitude) values of the two pixels are
the same (Sung-Ho Hong, personal communication,

*Reflectances** C**alculated
from MODIS L1B **D**ata***
**When using MODIS data (L1 B 1-km data), land surface reflectances
(inputs for albedo and NDVI calculation) can be calculated from bands 1, 2, 3,
4, 5 and 7. Bands 1 and 2 are the 250-m aggregated 1-km bands. Bands 3, 4, 5,
and 7 are the 500 m-aggregated 1-km
bands. (MODIS
Level 1B Product User’s Guide 2005):

* *

*r _{i }= reflectance_scale (SI – reflectance_offset ) * (25)

where r_{i }
(unitless) is the reflectance of band i, SI (scaled integer) is the raw L1B
data value for the corresponding pixel, and reflectance_scale and
reflectance_offset are the conversion factors to convert SI to reflectances. The L1B data product provides
the conversion factors.

*Ground **S**urface
**T**emperature **C**alculated
from MODIS L1B **D**ata*

Bands 31 and 32 in L1B
data are the 1-km thermal bands. They
can be used to calculate temperature after calculating the radiance value. Using the radiance for bands 31 and 32 (MODIS Level 1B Product
User’s Guide 2005), we have:

*L = radiance_scale
(SI – radiance_offset ) ** *(26)

where L is the radiance (W m^{-2} mm^{-1} sr^{-1}), SI is the raw L1B data value, and L1B data provide the
radiance conversion factors (radiance_scale and radiance_offset).

The land temperature (T_{s}, K) can be calculated based on
the Eq. (6.6 ) (Planck’s law) in Morse , et al. (2000) and Eq. (2) in Jiménez-Muñoz and
Sobrino (2003). Because there are two thermal bands, 31 and 32,
temperatures for both bands 31 and 32 (T_{s,31} and T_{s,32},
K) are calculated and the average of the two temperatures (T_{s,31} and
T_{s,32}) are used as the to
calculate ground surface temperature:

.

*T _{s,31}*= (27)

*T _{s,32}=* (28)

where L_{31}
and L_{32} are the radiances calculated from MODIS bands 31 and 32 and

* = 1.009 + 0.047 ln (NDVI) * (29)

for NDVI >0.
Otherwise, ε_{0}
(emissivity, unitless) is assumed to be 0.95—for example, for water (Van de
Griend and Owe 1993).

**NDVI ****C****alculation**

The NDVI (unitless) can be calculated using Eq. (4.2) on page 54 in Morse
, et al. (2000), i.e.,

*NDVI=(r _{2}-r_{1})/(r_{2}+r_{1})*

where r_{1} and r_{2} are band 1 reflectance and
band 2 reflectance, respectively.

*A**lbedo **C**alculation*

The albedo
can be calculated as (Liang , et al. 2002):

*albedo =** 0.160r _{1} +
0.291r_{2} + 0.243r_{3} + 0.116r_{4}+0.112r_{5}
+ 0.081r_{7}- 0.0015*

where r_{1}, r_{2}, r_{3}, r_{4},
r_{5}, and r_{7} are the corresponding reflectances of band 1,
2, 3, 4, 5 and 7.

*MODIS **M**odel
ET **C**ompared to
ASTER’s*

First, the ASTER RSET model (Wang , et al. 2005) was evaluated for accuracy using
plant ET data. Then, the MODIS land ET was compared
with ASTER ET to evaluate the MODIS model ET.

*The **S**ite
for ET **M**easurements
by Eddy **C**ovariance*

The

*ASTER **L**and ET
and MODIS ET **C**omparison*

Different locations at

The ET from ASTER data is at 90-m resolution. ASTER calculations aggregated to 11 pixels in each
direction to get equivalent 1-km data. The average ET was compared to the
corresponding daily 1-km MODIS ET.

ASTER data sets for four days were used. They were taken on June 8, 2005, September 7, 2003, May 18, 2003, and September 4, 2002.

*Calibration and **V**alidation*

*Evaporation **D**ata*

*Roosevelt** **Reservoir*

In order to
apply the remote sensing calculation to a reservoir the *c* coefficient in Eq. (8) for calculation of G into a reservoir must
be derived. By measuring the reservoir evaporation by a water balance
technique, the sensible heat flux from remote sensing data, and net radiation
from solar radiation, the energy balance equation can be solved for heat flux G
into a reservoir. The water balance data
as inflow, outflow and change in storage were acquired for the Roosevelt
Reservoir system of reservoirs in the Salt River Project in *c* in future work will be related to the
turbidity and temperature of the reservoir. The values currently reported in
the literature are for clear cold reservoirs and are not appropriate for reservoirs
at lower elevations with high turbidity in the southwest.

*Elephant *

The E loss (mm d^{-1}) data at the Elephant Butte Reservoir was used to evaluate the remote
sensing model by comparing the daily reservoir evaporation to that calculated
from the long term inflow outflow change in storage water balance of the reservoir.

The Bureau of Reclamation at

*E **W**ater **B**alance
**D**ata **C**alculation*

E (mm day^{-1})
data is calculated based on inflow (Q_{in}, m^{3 }s^{-1}), outflow (Q_{out}, m^{3} s^{-1}), and storage (V, m^{-3}), and the water body area (S, m^{2}).

*E= (3600×24×Q _{in}- 3600×24×Q_{out}-*

where ΔV is the daily storage change.

Data that resulted in negative values or
values of E greater than 20 mm d^{-1 } were assumed in
error and discarded. Monthly averages were then calculated for the time period
used in the analysis.

*Distinguishing **Reservoir**
area from **L**and
and **C**loud area*

NDVI and surface temperature are used to
distinguish reservoir from land and cloud. When NDVI is close to 0 or negative,
then the pixels are deemed to be water bodies or clouds. However, the
temperature of water bodies is much higher than the temperature of clouds and
this separation technique was used in the MODIS algorithms calculations to
separate the pixel calculations into the equation governing vegetation vs. reservoir
evaporation. .

*R _{n}
Calibration*

Weather stations usually measure R_{s},
so that R_{n} for the reservoir surface needs to be calculated. The
2005 measured daily R_{s} and R_{n} data for Elephant Butte Reservoir
in (Almy 2006) were used to deduce a regression relationship between the two
variables (Figure 8). Based on Eq. (2), the intercept in the regression
equation is the long wave radiation. Because the regression equation in Fig. 8
is for daily radiation, the intercept was divided by 24 to derive the hourly
radiation function.

R_{n} = 0.887R_{s} - 2.6497/24=0.887R_{s}-0.11 (33)

*G **C**alibration*

Calculation of G for MODIS was calibrated
using the Roosevelt Reservoir data and Eq. (1). R_{n} was calculated
based on Eq. (33). H was calculated by the ASTER RSET model for the calibration.
The value of lET_{ins} was calculated by scaling the
corresponding monthly,

(34)

(35)

Here, is the daily evaporation (mm d^{-1}), (mm d^{-1})
is the monthly average during 1973-1977 for the Roosevelt Reservoir
area (data at Desert Ridge weather station in ^{-}^{1},^{ }or kg h^{-}^{1}^{ }m^{-}^{2}),

(36)

Using Eq, (1), G can be solved.
In each month of 2005 and 2006, May-September, 5 G data points were calculated.
Then the corresponding values of G/R_{n} were calculated and the
average G/R_{n} was obtained.

*Temperature **C**orrection
of **C**old
**S**pots
with **E**levation*

We also tried to determine the effects
of elevation and consequent adiabatic cooling of air on the land surface
temperature for cold spots. This information can be used for temperature
correction for a cold spot if the spot has a different elevation from the area
of E or ET calculations. The

Several days in the months of April, May and June were
selected because during those months
moisture in the soil profile has been recharged from snow melt and moisture
does not limit ET. For these locations temperature values were recorded in a
table. The lowest elevation point was noted, and temperature at this point was
used to calculate elevation difference for all locations by subtracting it from
other elevation values. The temperature difference and elevation difference
data were represented by a scatter plot and a regression was obtained.

**Problems/Issues Encountered**

There were
some days when there were clouds over the area of interest. Sometimes, a cold
spot may not be available around an area of interest. A weather station may not be available around an area of interest.

Currently, downloading satellite
and weather data are not automated completely, nor is processing the data. For
example, an operator must manually choose a cold and a hot spot; then, E or ET
can be processed. The automation need to be completed in the future.

**Research**** F****indings**

*Temperature **C**orrection
of **C**old **S**pots with **E**levation*

Figure 10 shows the effects of
elevation on the land temperature of cold spots. It shows every 1000 m
difference of elevation, two cold spots at the different elevations can have
4.4 K difference.

*Reservoir **E**vaporation
from **W**ater **B**alance*

Figure 11 shows the monthly
average evaporation for Elephant Butte and Roosevelt Reservoirs from the water
balance methods. As expected, the evaporation rate follows the solar radiation
curve, low in the winter and increasing reaching a maximum at the summer
solstice, June 21. Roosevelt Reservoir had
higher evaporation in the winter time than
Elephant Butte Reservoir because the air temperature in the
surrounding desert at Roosevelt Reservoir is higher than at Elephant Butte Reservoir;
contributing more sensible heat advection transferred to Roosevelt Reservoir
compared to Elephant Butte Reservoir. In the summertime (June-August) the
evaporation was about 5.2-5.8 mm d^{-1}.

*Modeled E and ET *

Figures 12 and 13 show modeled ET maps for the

The ET comparisons (Fig. 14) show that the ET values from MODIS and ASTER were very close.
MODIS calculations were 3% lower than the
composite ASTER calculated ET. Because the ASTER ET model was validated for plant
ET (85% accuracy; Wang , et al. 2005), the model
for MODIS plant ET also has about 85% accuracy for plant areas.

Table 1 shows the comparison of measured and modeled E for Elephant Butte
Reservoir during summertime (June to August) in 2006 and August in 2005. The
difference between the measured and modeled E was within 1.4 mm
d^{-1} and the average bias is
-0.24 mm d^{-1}. The average
measured E was 5.6 mm d^{-1} in the
summer time vs. 5.9 mm d^{-1} of the
modeled.

**Conclusions
**

A model for
estimating evapotranspiration using MODIS data was developed and evaluated for both
plant areas and water bodies. It has a
1-km spatial resolution and a daily temporal resolution. The remote sensing model is capable of estimating
water body evaporation in summertime and capable of calculating
evapotranspiration over land. For the summertime E estimate, the accuracy is
within 1.5 mm day^{-1} and the
average bias is only -0.24 mm day^{-1}. The
evapotranspiration accuracy is about 85%. The average evaporation of Elephant
Butte Reservoir in summer time was 5.6 mm day^{-1}. The model accuracy is acceptable and it
is capable of aiding international water delivery management.

Reservoir G calculation was
calibrated in this project. It is proportional to net solar radiation and the
relation was calibrated using Elephant Butte Reservoir data. Cold spot
temperature variation with height was obtained in this study.

**Recommendations
for Further Research**

The model
needs to be improved. When a vegetation cold spot is not available around an
area of interest, then a calculated cold spot based on reference ETshould be
used, Weather data from a weather station are inputs for this model.
Improvements may be done to use less weather data or forgo using all ground
data.

The model needs to be completely
automated, including downloading and processing of data. In addition, the model
can be modified to be a Web-based software package. Then, a person can just
click at the website and get the information he/she wants.

The model was calibrated and
validated for the US-Mexico border areas. It needs to be improved and evaluated
for other areas.

The calculation of heat into the reservoir (G) should be improved and G should be resolved as a function of turbidity of a reservoir. The method of calculating the daily E (the scaling up of a midday energy balance to a daily energy
balance) will need to be refined for the pixels over the
reservoir because it may be different over the reservoir
compared to the land.

**R****research**** B****benefits**

This model can improve the
international water delivery management to evaluate the evaporation loss from
reservoirs. For example, we have trained the cooperator Mr. Ramiro Lujan at the Mexican
Section of the International Boundary and Water Commission to run the model and estimate the
evaporation loss of Mexican reservoirs (See the sub-report at the end of the
main report).

In addition, this model may be
used for reservoir or lake evaporation estimate for other areas. This model can
also estimate other energy components at a reservoir or lake, namely, the heat
flux to water and sensible heat flux to air.

The model can be used for
estimating evapotranspiration on land surfaces also. This can help farmers,
water managers, and researchers to know the water use by crops and other
plants.

The model can be used to
determine the savings in lake evaporation water loss that would occur if water
is stored longer in upstream reservoirs at higher elevation which will have
less lake evaporation through out the year.

Documentation
for the model theory, installation and compilation procedures was prepared.
More documentation for help in finding weather data was also prepared. A Website
was made which provides access to the remote sensing tool and the
documentation. The documents and Website enable other researchers and related
users to access, use, and revise the model. http://hydrology1.nmsu.edu/Lake%20evaporation/Lake%20Evaporation%20Remote%20Sensing%20Model.htm

Two journal manuscripts have been
submitted for publication:

1.

Wang, J., T.W. Sammis, and V. P.
Gutschick. The sensitivity and accuracy of a Remote Sensing
Evapotranspiration algorithm (RSET) using Aster satellite data. Submitted to Remote Sensing of Environment.

2.

Wang, J., T.W. Sammis, and V. P.
Gutschick. Review of satellite
remote sensing use. Submitted to Journal of Applied Remote Sensing.

Two conference papers have been published and presented:

1.

Wang, J., T.W. Sammis, and V. P. Gutschick. A Remote Sensing Model
Estimating Water Body Evaporation. 2008
International Workshop on Earth Observation and Remote Sensing Applications.
June 30-July 2, 2008.

2.

Wang, J., T.W. Sammis, and V. P.
Gutschick. A Remote Sensing Model
Estimating Reservoir Evaporation. 2008 IEEE International Geoscience &
Remote Sensing Symposium. July 6-11, 2008 |

** Acknowledgements**

This project was made possible by a grant from the Southwest Consortium for
Environmental Research and Policy (SCERP) and the New Mexico State University
Agricultural Experiment Station,

**E****ndnotes**

References

Almy, C.S. 2006. “Investigation Of Evaporation And Heat Storage At
Elephant

Bastiaanssen, W. G. M., M. Menenti, R. A. Feddes, And A. A.
M. Holtslag. 1998. “A Remote Sensing Surface Energy Balance Algorithm For Land
(SEBAL). 1. Formulation.” Journal Of Hydrology. 212/213: 198-212.

Bastiaanssen, W. G. M., E. J. M. Noordman, H. Pelgrum, G.
Davids, B. P. Thoreson, And R. G. Allen. 2005. “SEBAL Model With Remotely
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**Sub-report
from Mexican Cooperator:**

**Improving Water Management Along
the **

**Ramiro Lujan, Mexican Section of
the International Boundary and Water Commission Between **

**Junming Wang, New
Mexico State University at **

**Theodore W. Sammis, New Mexico State
University at **

**Narrative
Summary**

Remote sensing techniques, models, and
instruments are constantly developed and applied to many branches of human
knowledge, providing multiple valuable services and products for social,
agricultural, environmental, and hydrologic sciences.

During the last five decades there has
been significant growth in the field of remote sensing, particularly in soil
and water related sectors. Leading governmental agencies, academic
institutions, and research organizations have created and developed useful
remote-sensing models and techniques to improve the conservation and management
of water resources.

A New Mexico State University (NMSU)
team developed a satellite remote sensing model based on the Surface Energy
Balance Algorithm for Land (SEBAL) method of energy budget, validated for the
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imaging
instrument. It uses Moderate Resolution Imaging Spectroradiometer (MODIS)
satellite data, land information from terrestrial weather stations, and water
balance calibrations of evaporation to estimate water evaporation at surface
water bodies.

This paper describes some runs and
results of the NMSU model at an international water reservoir during the summer
months of 2007. Final results indicate that the NMSU model can provide useful information
about evaporation water estimates. This model constitutes a valuable additional
tool that could potentially contribute to improve water management along the

**Introduction**

Available fresh water resources are
steadily decreasing at many regions of the world, mainly due to the rise of
human population growth at an unprecedented rate (Rogers 2008). One of the
regions experiencing a constant increase in population and economic activities
is the

A recent episode clearly exemplifies
the difficulties posed by water scarcity at the

Satellite remote sensing is a major
component of the methodology, with multiple and valuable applications upon
diverse topics related to economic, social, geophysical, and environmental sciences.
Since its inception and with subsequent developments, this technique has been a
powerful tool to monitor and evaluate global surface changes caused by natural
and anthropogenic activities and interactions. Specific applications of remote
sensing models are: forest mapping, fire assessment, landscape and cultivated
land classification, biomass mapping, crop type and production data gathering,
water stress assessment, drought and flood impacts evaluations, location and
extent of water bodies, etc.

Remote sensing can be used to monitor
water loss by lake evaporation and water used by crops. Any measure to reduce
or eliminate water losses along the border will contribute to preserving the
existing limited water resources of the area, allowing managers to cover the
expected water demands of incoming years for human and natural purposes, and
ultimately, to reduce potential conflicts between the two countries. Better
estimates of water evaporation at surface water bodies will improve the use and
management of water along the

Remote
sensing systems detect, analyze, and process the radiation emitted or reflected
by objects or areas being subject to observation. Remote sensing data is processed and analyzed with
computer software, and a number of commercial and open-source applications
exist to solve the surface energy balance alghortihms for mapping regional lake
evaporation and crop evapotranspiration using remote sensing data (Gowda , et
al. 2007).

Other
methods of estimating lake evaporation include the inflow-outflow water balance
method (exposed-area method), pan measurement, and eddy covariance methods,
which are time- and labor-intensive (Wang , et al. 2008). However, these
traditional methods can not incorporate the spatial variability of a lake or
reservoir evaporation (Wang , et al., 2008). Many of the past and current
satellite remote sensing methods make use of spectral signatures or simple
indices such as the Normalized Difference Vegetation Index (NDVI) and do not
use a physically based algorithm (Wang , et al., 2008).

A NMSU team formed by Dr. Junming Wang,
Ted W. Sammis, and Vince Gutschick developed a remote sensing model to
calculate crop water use (i.e., evapotranspiration, ET) and water body
evaporation (E) in a reliable, fast and practical way, making use of specific
satellite information.

**Research Objectives **

The objectives of this project are:

To estimate water evaporation values at
a surface water body located at the

To evaluate the model input data
gathering process and the model running procedure.

**Research Methodology/Approaches**

This paper describes using the NMSU
model to estimate water evaporation for a surface water body.

The basic hypothesis is that
application of the NMSU water evapotranspiration model at a U.S - Mexican
border surface water body, using MODIS satellite data and imagery, as well as
land weather information, can provide useful water evaporation values to be
used for better water management along the referred border.

One of the most appropriate and useful
tools for forest health management, agricultural planning, and water management
is the Moderate Resolution Imaging Spectroradiometer satellite data (MODIS),
developed by NASA. MODIS data offer some useful characteristics: it has a 250
–1000 m spatial resolution and 1-2 day temporal resolution. MODIS data are
provided freely and cover up to 36 spectral bands, more than many other satellites.
The satellite data are available as raw data, and also as processed data, where
the raw data is corrected for atmospheric effects and algorithms are used to
produce specific products such as surface temperature, reflectance and images
(Wang , et al., 2006).

The NMSU evaporation model using MODIS
satellite data is based on a surface energy balance algorithm for land
(Bastiannssen , et al. 1998). The model limits errors associated with surface
temperature measurements from the satellite and is consequently a more
operational useful model for determining ET than other methods (Wang , et al.,
2008). The limitation is that a hot and a cold spot must be found in the
satellite image. The model was modified in order to handle MODIS input and was
calibrated and validated using reservoir data.

*Model
Theory *

The model is based on the principle
that water evaporation can be derived from energy balance. Energy balance is a
function of net solar radiation (R_{n}), sensible heat flux (H), latent
heat flux (water evaporation, λE), and Water Heat
Flux (G). This can be expressed by the energy balance formulas:

**λ****E _{ins}
= R_{n} – H – G, **and

**ET _{daily}
= (**

where λE_{ins} is instantaneous latent
heat flux, ET_{daily} is the amount of daily evapotranspiration, λET_{r,ins}
is instantaneous reference latent heat flux for an cultivated field, and ET_{daily}
indicates daily reference evapotranspiration, determined by the United
Nations`s Food and Agriculture Organization (FAO) Penman-Monteith Method (Wang ,
et al. 2006), using data from the nearest weather station.

Net radiation (R_{n}) is a
function of reflectance and solar radiation (R_{s}) as well as thermal
radiation; it can be estimated from solar radiation alone, using an empirical
regression. Soil (or water-body) heat flux (G) is a function of the normalized difference
vegetation index, NDVI. Sensible heat is a function of NDVI, temperature,
reflectance, solar radiation, and wind speed. For the summertime E estimate,
the accuracy of the method is 1.5 mm/day. The evapotranspiration accuracy is 85
% (Wang , et al. 2008). All data, satellite imagery and land records, have to
be obtained for the summer months of a given year.

The main objective of this model,
namely, improving international water management, will be properly achieved if
it is applied to water bodies located near or along the international boundary
between the

Amistad Reservoir is located at the ^{o}28.18 N and 101^{o}3.14 W; surface elevation: 340 m (Fig. 15). The
reservoir was formed in November 1969 by the construction of Amistad
Dam, intended to provide flood control,
water conservation, irrigation,
hydroelectric
power, and recreation to the area. It covers approximately 64,900 acres (263 km^{2})
and contains a water volume of about 5,658,500 acre–feet (6.98 km^{3}),
with significant cultivated areas lying downstream along the

*Data
gathering process of the model*

The NMSU model requires two types of
data: satellite and land data. Satellite information input data are: surface
temperature and reflectance. Land information input is solar radiation,
humidity, and wind speed.

*Satellite
data*

The MODIS web site provides periodic
images taken by NASA`s Terra satellite of the entire Earth`s surface as it
passes every day from north to south across the Equator in the morning.
Satellite images for the Amistad Dam region were obtained by the NASA web site
during the months of June, July, and August (summer time) of 2006 and 2007.

Satellite images showed cloudy skies
above the selected area during most days of June, July, and August of both
years, a fact that prevented pinpointing Amistad Dam. Also, satellite
trajectories were not directly passing above Amistad Dam during most sunny days
of the summer season of 2007, making impractical any temperature or reflectance
readings for the selected area. During the summer of 2007, the days of good
satellite images for Amistad Dam were the 4^{th }and 14^{th} of
June, and the 5^{th} and 7^{th} of August.

Having selected the appropriate
satellite images, all related 1-km resolution MODIS LB1 raw data were
downloaded. The model automatically inputted the desired bands of the data. The
model has user-friendly interfaces. As an example, a reflectance and
temperature image of the site, with adjacent blank entries to be filled in with
land weather information is shown in Fig. 17.

*Land
information *

As mentioned earlier, the model
requires land-specific information at or near the selected water body: solar
radiation (L/hr), humidity (%), wind speed (km/h), and reference daily evapotranspiration
(mm/day). Land information was obtained from two specific weather station Web
pages.

Humidity and wind speed data were
obtained from a weather station near the Amistad Dam. This station is a private
weather station in the vicinity of Kickapoo Caverns at ^{ }N, -100.47^{o }W.
(http://www.wunderground.com/weatherstation/ListStations.asp?selectedState=TX). Kickapoo weather
station does not provide solar radiation and ET_{r} data. Humidity and
wind speed readings at MKCPT2 station for the selected days, are indicated in Table
2.

Solar radiation (R_{s}) and daily
reference ET (ET_{r}) are parameters with small variations inside a
latitude band encompassing Amistad Dam and Las Cruces, New Mexico, which are
located about 3^{o }apart (Amistad Dam latitude: 29^{o}28.18 N,
Las Cruces N.M. Latitude: 32.34^{o} N). Therefore, a weather station
located at _{s}
and (ET_{r}). Solar radiation and ET_{r} data was provided by weather
station

*Calculation
process*

All above data were plugged into the ET
Model, which performed the expected calculations.

The next step was to determine minimum
and maximum temperature in a square area encompassing Amistad Dam. This was
done by pinpointing “cold” and “dry” pixels near the dam, representing
vegetation and bare soil temperatures. First, the “cold” pixel was pinpointed
at the upper left and lower right sides of an imaginary square inside of which
the dam was located. After pinpointing both sides of the rectangle, the data were
fed into the program using a “setting cold point” command, which incorporates
mean “coldest” temperature and site coordinates of the area into the model. A
similar procedure was performed for “dry” pixels to find the mean “hottest”
temperature. After plugging in all temperature data, the model calculates the evapotranspiration
(ET) or E (Fig. 18).

*Final
Results*

Water evaporation values on June the
4th and 14th, and August the 5th and 7th of 2007 were 6.08, 8.53, 5.67, and
5.11 mm/day, respectively. Final results
indicate a mean evaporation of 6.35 mm/day for Amistad Reservoir during the
summer sample days
of 2007. The complete specific data set and final results for all dates of are
shown in Table 4.

Site coordinates, temperature, and ET
information were finally fed into a related program, the Model program, which
yields a graphic image of the data, allowing a quick and visual comprehension
of the numerical information. An example of the graphic image provided by the
model is shown in Fig. 19.

**Problems/Issues Encountered**

To obtain
reliable data satellite information, good clear land satellite images from the
selected area need to be obtained. Satellite orbits must pass near or right
above the pinpointed area. In the
summer, frequent cloudy sky conditions can reduce opportunities to obtain good
satellite images of the selected Amistad reservoir site.

Cultivated or forested areas close or
near the site of interest are needed in order to calculate cold points.
Temperature readings would be difficult to obtain if there are not cultivated
or forested areas nearby the site.

Weather stations with good solar
radiation, daily ET_{r}, wind and humidity data are necessary to obtain
reliable final results. If weather stations are not sufficiently close, computations
can not be completed.

Pinpointing the selected site on the
temperature image can be difficult if site coordinates are not well
defined.

**Research Findings **

During the summer season of 2007, cloudy
skies were a frequent occurrence above the Amistad reservoir area, which lies
along the

Local weather conditions (i.e. clear
skies) at the selected site (Amistad reservoir) allowed the use of satellite
images during the days of 4 and 14 of June, and 5 and 7 of August of
2007. E values at

**Conclusions**

Most of the existing evapotranspiration
models require multiple data sources, involving extensive spatial and temporal
information. A simplified and practical evapotranspiration model will
contribute to enhance water evaporation estimates.

The NMSU ET Remote Sensing Model using
MODIS data constitutes a valuable and practical tool to estimate water
evaporation at surface water bodies. It provides useful practical numerical
values about water evaporation.

The NMSU ET Remote Sensing Model could
be used by U.S. and Mexican water management agencies to estimate evaporation
at surface water bodies along the border; being a practical way to check water
evaporation losses determined by regular approved methods, contributing to an
improved water management at the area.

A binational operating protocol for the
use of the ET Remote Sensing Model by

**Recommendations For Further Research **

The ET Remote Sensing Model requires
minor adjustments and refinements to improve water evaporation estimates for
surface water bodies located particularly in arid or desert areas where water
evaporation is intense and cultivated or forested areas are scarce or
nonexistent.

**Research Benefits**

Applying the NMSU ET Remote Sensing
Model to determine water losses at surface water bodies along or near the
US-Mexico border, would improve binational water management, contributing to a
better use and conservation of the existing limited water resources of the
region, and resulting in the benefit of its inhabitants.

Developing a binational protocol for
the use of the model can enhance international cooperation among governmental
agencies and academic institutions of both countries. The model represents an
additional tool for policy and decision making by water management agencies of
different countries.

**Acknowledgments**

This work was sponsored by the
Southwest Consortium for Environmental Research and Policy (SCERP) through a
cooperative agreement with the U.S. Environmental Protection Agency. SCERP can
be contacted for further information through www.scerp.org
and scerp@mail.sdsu.edu

Very special thanks to Dr. Ted J.
Sammis and Dr. Junming Wang of the Department of Plant and Environmental
Sciences of the New Mexico State University whose knowledge, expertise, and
assistance about satellite imaging, plant evapotranspiration, and water
evaporation, made possible this effort.

Special recognition goes to Ing. Arturo
Herrera, Mexican Commissioner of the International Boundary and Water
Commission between

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