cive 835: gis in water resources exercise 6: estimating

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CIVE 835: GIS in Water Resources Exercise 6: Estimating Evapotranspiration from Satellite

Prepared by Ayse Kilic, Revised 11/8/2016

PURPOSE

The purpose of this exercise is to utilize Landsat 8 data to calculate a vegetation index (NDVI) and

then to estimate evapotranspiration (ET) from the NDVI in the ArcGIS environment. The

Normalized Difference Vegetation index (NDVI) is calculated from Landsat 8 TM. This exercise

will also apply the EEFlux Google Earth Engine Application to produce an ET map using a Surface

Energy Balance that is based on the METRIC model.

COMPUTER AND DATA REQUIREMENTS

The data provided below have been prepared in a lab data folder under Exe6Data.Zip for the

North Platte River above Pathfinder Reservoir, Wyoming. It includes the following:

1. A toolbox, “DNtoRadiance”, has four models in it that will assist in the calculation of

reflectances, NDVI, surface temperature, and evapotranspiration. (i.e. converts the

“digital number” of the Landsat bands into actual reflectance units, estimates LST from

band 10, etc.).

A second toolbox named "DNtoSurfaceTemperature" is also included and contains a

second model named "SurfaceTemp_2016" that calculates Surface Temperature in a

more simple, decoupled way. We are going to build the "SurfaceTemp_2016" model in

class so that you learn how to build models.

2. NorthPlatte_1.gdb. This database contains

a. Reflectance bands for bands 4 and 5 are the same that you should have calculated for

Exe 5, but I am providing it here.

b. B10 digital number from Landsat 8 for June 20, 2013.

c. NDVI_toa: That you should have calculated for Exe 6, but I am providing it here.

d. Etrf24_ef_06202013_P35R31_L8_NP_Color.img . This is a raster file that contains

evapotranspiration in the form of Fraction of reference ET (or relative ET). It has

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been derived from a land surface energy balance model (METRIC). It will be

compared with the relative ET map obtained from NDVI. We will also run the

Googe EEFlux application to produce ETrF data for the same scene and date.

3. Sample_points_AK.shp. A shape file composed of a number of ‘points’ that will be

used for sampling data for the purpose of creating statistics for land use classes.

4. ETrF_frmNDVI.breaks2.xml. You can use this xml file to load the color for each class

of ETrF

SETTING UP YOUR DATA

First, make a directory (Exe6).

Copy the files above into this directory

The raster images for reflectance (ref_b4, ref_b53), and the DN b10 and NDVI should

each contain the two rows (31 and 32 of path 35) where you mosaicked these together

during exercise 6. Again, I have provided all of these to you in NorthPlatte_1.gdb

PART 1: MOSAICKING TWO LANDSAT ROWS FOR THE THERMAL BAND

As you have seen in Exercise 6, Landsat data come in ‘scenes’ (one path and one row) that are

about 160 km on a side (100 miles). One single scene (Path 35 Row 31) does not cover our

basin. Therefore, we need to also bring in Path 35, Row 32. We need to mosaic the two scenes

together for band 10 for path 35. We need to do this prior to calculating radiance and LST (Land

Surface Temperature).

However, to reduce your work load, I have done the mosaicking for you and am supplying you

with the two row mosaic named “b10.” The b10 is a digital number (tiff) image that originated

from the USGS Eros Data Center that represents thermal band 10 of Landsat 8 for the June 20,

2013 image.

PART 2: CALCULATION OF LAND SURFACE TEMPERATURE FROM LANDSAT 8

I have created a toolbox (tbx) that contains models from Model Builder to calculate land surface

temperature (LST) and evapotranspiration (ET) from vegetation index for Landsat 8 data. The

name of the toolbox is: DNtoSurfaceTemperature.tbx and it is included in your exercise folder.

You should ‘add’ this toolbox to your list of tools.

Under this toolbox, the “SurfaceTemp_2016” model will calculate LST using band 10 of Landsat

8. Right click on this tool and select ‘edit’ to see the toolbox displayed.

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The SurfaceTemp_2016 model that is in the DNtoSurfaceTemperature toolbox is the one that we

will build today. That model looks like the following:

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We will build and run this model in a few minutes, but first, let’s get acquainted with how we

calculate surface temperature from Landsat.

You can click on the yellow boxes in the LST model above to see the equations that we will

cover below, as they have been implemented using the Model Builder code. Note that in the

tool, the inputs are in blue and include the b10 tif (DN image) and the bands 4 and 5 reflectances

along with four coefficients. The model computes both narrow band thermal emissivity and the

LST.

Background on Landsat surface temperature

Land surface temperature (LST) has been measured by Landsat since Landsat 4 was launched in

1982. Currently, Landsat 7 and 8 are in operation. The pixel size of thermal data is 120 m for

Landsat 4 and 5, 60 m for Landsat 7 and 100 m for Landsat 8. This compares to 30 m pixel size

for reflected short-wave data. Beginning in about 2009, all thermal data from Landsat has been

resampled to 30 m pixel size by the USGS EROS data center to conform to the 30 m pixel size of

short-wave data. Cubic convolution resampling is used. However, we should always keep in

mind the original ‘native’ resolution of the thermal pixels.

The Landsats 5 and 7 had only a single thermal band, band number 6. Landsat 8 has two thermal

bands, bands 10 and 11. Post-launch testing of the two Landsat 8 bands has indicated that band

10 has higher accuracy than band 11, and is therefore utilized here. Band 10 covers the

wavelength region of approximately 10.5 to 11.2 micrometers and band 11 covers the

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wavelength region of approximately 11.5 to 12.5 micrometers. Either band can be used to

estimate LST.

The following text describes the series of steps for calculating land surface temperature, LST,

from Landsat. Some of this text is based on the METRIC manual for determining

Evapotranspiration from Surface Energy Balance.

a) Step 1: The spectral radiance (Lb) for thermal radiation (W/m2/sr/m)

Similar to the procedure used for short-wave data, the spectral radiance (Lb) for thermal radiation

is computed from the digital number of each individual 30 m pixel. Spectral radiance is the

outgoing radiation energy of the band as observed at the top of the atmosphere by the satellite. It

is calculated as:

Step 1: LMIN)QCALMINDN(QCALMINQCALMAX

LMINLMAXLb

(1)

where

DN is the digital number of each pixel (recall that DN is used to efficiently “pack” data for

transmission from the satellite),

LMAX and LMIN are calibration constants found in the header file. These represent the

maximum and minimum radiance values used to scale the DN. LMAX and LMIN will have the

same dimensions as Lb and W/m2/sr/m.

The QCALMAX and QCALMIN are the highest and lowest range of values for rescaled

radiance in DN and are also found in the header file.

Eq. 1 essentially reverses the process used on the satellite to convert the Lb radiance into the DN

values for transmission to Earth.

The units for Lb are W/m2/sr/m. “sr” stands for ‘sterradian’ and “m” represents micrometers (1

millionth of a meter). Landsat 5 and 7 images are stored using one eight bit integer for each

pixel and band. Eight bit integers have the range of 0 to 28 –1, where 28 - 1 = 255. Landsat 8

images, being more modern, are stored as 16 bit data and have the range of 0 to 216 -1 = 65,535.

The values of LMAX and LMIN can vary from image date to image date. Therefore, it is

important to confirm, for each scene (path/row), the specific coefficients for each band from the

header file.

In the case of thermal data for Landsat 8, these LMAX and LMIN, QCALMAX and QCALMIN

values are found in the header file and are termed “Radiance_maximum_band_10” and

“Radiance_minimum_band_10”.

These following two values represent LMAX and LMIN for June 20, 2013 for path 35, row 31

for band 10:.

RADIANCE_MAXIMUM_BAND_10 = 22.00180

RADIANCE_MINIMUM_BAND_10 = 0.10033

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Therefore, LMAX = 22.0018 W/m2/sr/m and LMIN = 0.10033 W/m2/sr/m.

The values of QCALMAX and QCALMIN are found in the header file of the June 20, 2013

image for path 35, row 31 for band 10

QUANTIZE_CAL_MAX_BAND_10 = 65535

QUANTIZE_CAL_MIN_BAND_10 = 1

Therefore QCALMAX = 65535 and QCALMIN = 1.

b) The surface temperature (Ts, Kelvin)

The surface temperature is computed using the following modified Plank equation:

Step 6:

1R

K

KT

c

1NB

2s

ln

(2)

where: Ts is surface temperature (K), Rc is the corrected thermal radiance from the surface and

K1 and K2 are constants for Landsat 8 image. Units for Rc must be the same as those for K1

(W/m2/sr/μm).

For Landsat 8, constants may change from date to date, and therefore need to be read from the

header file for the specific image date. In the case of the June 20 image date for path 35, row 31

(over SE Wyoming):

K1_CONSTANT_BAND_10 = 774.89




Therefore, we will use K1 = 774.89 and K2 = 1321.08.

It is the K1 and K2 for band 10 that should be used for this exercise 7.

The K1 and K2 coefficients for Landsat 5 (L5) and Landsat 7 (L7) are also located in the header files, but

do not change for specific image date for L5 and L7. Table 1 provides the K1 and K2 for L5 and L7.

Table 1. Constants K1 and K2 for Landsat 5 (Markham and Barker, 1986; Chander et al., 2009) and

Landsat 7 (Landsat 7 Science User Data Handbook Chap.11, 2002; Chander et al., 2009) in

W/m2/sr/μm

K1 K2

Landsat 5, Band 6 607.76 1260.56

Landsat 7, Band 6 666.09 1282.71

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c) Surface emissivity (

Surface emissivity (is required to estimate LST. Surface emissivity is defined as the ratio of

the thermal energy radiated by the surface to the thermal energy radiated by a blackbody at the

same temperature. Two surface emissivitiesare used in energy balance work. The first is an

emissivity representing surface behavior for thermal emission in the relatively narrow band 6 (or

band 10) of Landsat (10.4 to 12.5 μm for band 6 of Landsat 5 and 7 and 10.5 to 11.2 μm for band

10 of Landsat 8). This narrow band emissivity is expressed as NB.

A second emissivity term is an emissivity representing surface behavior for thermal emission in

the broad thermal spectrum (6 to 14 μm), and is expressed as . The is used during energy

balance work to calculate total longwave radiation emission from the surface and is not used

here.

The surface emissivities are computed using the following empirical equations developed by

Tasumi et al. (2003c) using leaf area index (LAI) based on soil and vegetation thermal spectral

emissivities from the MODIS UCSB Emissivity Library1. NDVI is used to filter soil and

vegetation (NDVI > 0) from water and snow (NDVI ≤ 0).

Step 4:

For NDVI > 0

NB = 0.97 + 0.0033 LAI; for LAI 3 (3)

0.98 for LAI 3

For NDVI ≤ 0

Water, Albedo ( < 0.47, = 0.99 and o = 0.985

Snow, Albedo ( ≥ 0.47, = 0.99 and o = 0.985

The values for water and snow are set to the same values, but can be modified based on locally

calibrated emissivities and user preference.

d) Step 3:The leaf area index (LAI)

The leaf area index, LAI, is the ratio of the total area of all leaves on a plant (one side of the

leaves) to the ground area represented by the plant and ranges from 0 to 6. LAI is dimensionless

(m2/m2) and is an indicator of biomass and canopy resistance to vapor flux.

1 http://www.icess.ucsb.edu/modis/EMIS/html/em.html

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The LAI is computed using the following empirical equation stemming from Trezza et al.

(unpublished):

Step 3: 0.817 SAVIfor ;6

0.817 SAVIfor ;11 3

>

LAI

SAVILAI (4)

where SAVI is the SAVI calculated from Equation (5) using a value for L of 0.1. The maximum

value for LAI is 6.0, which corresponds to a maximum SAVI of 0.817. The relationship between

SAVI and LAI can vary with location and crop type.

e) Soil adjusted vegetation index (SAVI)

The SAVI is the soil adjusted vegetation index, and is similar to the NDVI. SAVI attempts to

“subtract” the effects of background soil from NDVI so that impacts of soil wetness are reduced

in the index. SAVI is computed as:

Step 2: SAVI = (1 + L) (t,NIRt,RedL + t,NIRt,Red (5)

where L is a constant for SAVI. If L is zero, SAVI becomes equal to NDVI. A value of 0.5

frequently appears in the literature for L. However, a value of 0.1 was found by Tasumi et al.

(2002) to better represent soils of southern Idaho. The value for L can be derived from analysis

of multiple images where vegetation does not change, but surface soil moisture does. ρt,NIR and

ρt,Red are the top of atmosphere reflectances for the NIR and red bands (bands 4 and 3 for

Landsats 5 and 7 and bands 5 and 4 for Landsat 8).

f) Thermal radiance from the surface (Rc, W/m2/sr/m)

Thermal radiance at the surface (Rc) is calculated from the thermal radiance sensed at the

satellite following Wukelic et al. (1989) as:

Step 5: skyNB

NB

pb

c RRL

R

1 (6)

where

Lb is the spectral radiance of the thermal band (6 or 10) (W/m2/sr/m),

Rp is the path radiance in the 10.4 – 12.5 or 10.5 – 11.2 μm band (W/m2/sr/m),

NB is the narrow band emissivity (unitless),

Rsky is the narrow band downward thermal radiation from a clear sky (W/m2/sr/m), and τNB is the narrow band transmissivity of air (10.4 – 12.5 or 10.5 – 11.2 μm).

Eq. 6 essentially ‘corrects’ the Lb radiance for atmospheric attenuation. The units for Rc are

W/m2/sr/m. The corrected thermal radiance (Rc) represents the actual radiance emitted from

the surface whereas Lb is the radiance that the satellite “sees”. In between the surface and the

satellite two things occur. First, some of the emitted radiation is absorbed and reflected by the

atmosphere (transmissivity) and does not reach the satellite. Second, thermal radiation is emitted

by the atmosphere in the direction of the satellite (path radiance) and the satellite “thinks” that

this is from the surface.

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For the surface_temperature_model2, we use Rp = 0.91, τNB = 0.866 and Rsky = 1.32 for low

aerosol conditions suggested by Allen et al. (2007). τNB = 0.866 means that on average the

atmosphere is 87% transparent to the thermal photons in band 10. The rest of the 14% of the

photons are absorbed or scattered in the atmosphere before they reach to satellite. Rsky = 1.32

means that the sky is emitting 1.32 W/m2/sr/m to the surface that is being reflected to the

satellite. The values for Rp of 0.91 mean that the sky is emitting 0.91 W/m2/sr/m directly to the

satellite. The values for Rp and τNB require the use of an atmospheric radiation transfer

simulation model such as MODTRAN and radiosonde profiles representing the image and date.

Fortunately, the effects of the three parameters on Rc are largely self-canceling and use of Lb

alone provides general atmospheric correction for most clear sky atmospheric conditions and

appears to be accurate for Ts of about 290-300 K. The result of no correction to Lb will be a

general underestimation of surface temperature (Ts) by up to about 5o C for warmer portions of

an image Allen et al. (2007).

Now let’s run the temperature model and see just how hot things are in Wyoming .

If you double click on the SurfTemp_2016 model that we just created, you will see the

followings inputs and the outputs:

Inputs are b4 reflectance (red band), band 5 reflectance (NIR) that are used to calculate SAVI and

LAI, and thermal band (b10) that we mosaicked above. Note that the b4 and b5 images must be

mosaicked also. You should have done that for exercise 6. Outputs are LST (SurfTemperat). Other

inputs that can be changed for each image are the Lmax, Lmin, K1 and K2 constants that are provided in

the Meta file that comes with each Landsat image. You can change the input file names and the constants

each time you run. You will need to assign a unique name to the SurfTemperat "output" image each run. The following menu shows an 'error' for the SurfTemperat image since it already exists (I ran the model

once already). I can either delete that file to correct the error, or provide a new name for the output image.

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Remember that the Lmax, Lmin, K1 and K2 are coefficients that come from the header file for the specific

Landsat image date. These will almost always be the same value for two rows that appear in the same path

on the same date.

Note that at the end of the running of the model, we are getting some warnings about the

difference in projections between the “WGS84” used by the Landsat L1T TIFF images and the

Albers that is used by the Data Frame:

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Now we should be able to see both the original ‘tif’ files of Digital numbers for band 10 (b10)

and the new “LST” (land surface temperature) layers. Your viewer may look like this:

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Notice that LST varies from 268 to 333 Kelvin, and the emissivity is 0.98 and did not vary across the

scene (the emissivity model is not quite working in the 2014 model (Surface_Temperature_Model2). If anyone would like to ‘fix’ it, they can have 3 pts extra credit ).

We should colorize the LST map to help us visualize areas of high and low surface temperature and to improve its appearance. Let’s select a color scheme that shows hottest as red and coolest as blue.

The resulting two screenshots below show the Pathfinder Reservoir where the temperature of the water

surface is much cooler than the surrounding rangeland. The mountains in the south part of the screenshot are also cool.

Question: Why are the mountains cool? Give two reasons. (turn in your answer)

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We can zoom in on some of the irrigated areas in the center part of the basin and see individual

fields (below). We can see that the center pivot fields are somewhat out of shape because of the

use of WGS84 projection with the Landsat L1T images from the USGS, whereas the native

projection system for the ‘basin’ layer is Albers Equal Area. For a ‘serious’ product, we would

want to reproject the Landsat and associated products into the Albers Equal Area, or vice-versa.

Note that the fields are a little ‘fuzzy’ due to the 100 m native pixel size of the thermal band of

Landsat 8. The same fields presented later in NDVI are more clear because of the 30 m pixel

size of the short wave bands.

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Turn in: Colorized surface temperature map and legend for the entire basin area and for a close up showing some irrigated agriculture.

PART 3. ESTIMATING EVAPOTRANSPIRATION from NDVI

The normalized difference vegetation index, NDVI, is a quantity used to assess the presence of live green

vegetation. The NDVI was computed in Exercise 6 using the formula: 𝑁𝐷𝑉𝐼 =(𝑁𝐼𝑅−𝑅𝐸𝐷)

(𝑁𝐼𝑅+𝑅𝐸𝐷) and will be

used for this exercise.

The RED and NIR stand for the spectral reflectance measurements acquired in the red and near-infrared

regions of electromagnetic spectrum, respectively. NDVI takes values from -1 to 1. The higher the NDVI, higher the fraction of live green vegetation present. Band 5 (0.77-0.90 µm) of Landsat 8 measures the

reflectance in NIR region and Band 4 (0.63-0.69 µm) measures the reflectance in the Red region.

When we estimate ET from NDVI, we are relying on a proportional relationship between the amount of vegetation and the amount of transpiration (ET = evaporation from soil and transpiration from the

vegetation). We need to appreciate that this is only an approximate relationship. If the soil is dry in the

root zone, as is common in rangeland and forested areas during parts of the year, then transpiration, T, will be less than expected, due to water stress. Conversely, if a recent rain event has wetted the upper soil

layer, then there will be some evaporation, E, in addition to T, so that total actual ET may exceed the ET

estimated from NDVI.

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Therefore, we need to appreciate that this estimate for ET is an approximation, however, it is generally in

the ‘ballpark’ and can be useful as a first and rapid estimate. We will compare our estimate of ET with a more accurate ET derived from surface energy balance in a later section.

ET not only varies with vegetation, it also varies with weather. ET will be greater under dry, windy

conditions than under humid, calm conditions, and under sunny skies as compared to cloudy skies. Therefore, we will couple our estimate of relative ET from NDVI with the reference ET that takes into

account the effects of weather. We will call the relative ET from NDVI the “fraction of reference ET”

(ETrF). ETrF is, as described above, generally proportional to NDVI. ETrF generally ranges from 0 to about 1.0. A value of 1.0 means that the fraction of reference ET is 1.0, so that the ET for that pixel

equals the reference ET value. The reference ET in this case is the “tall” or alfalfa reference ET that is

usually calculated using the ASCE Penman-Monteith equation (ASCE 2005).

We use the following conditional statement to estimate ETrF:

If NDVI>0, ETrF = 1.25 NDVI

Else, ETrF = 0.7

The 0.7 estimates ETrF for any water bodies that may exist in the image. Water usually has a negative

NDVI. The 0.7 value assumes that evaporation from water is about 70% of the tall reference ET value.

The statement translates into following in the raster calculator used in Model Builder.

Con("%ndvi_toa%" > 0.0, 1.25*"%ndvi_toa%", 0.7)

Double click on ETrF_from_NDVI tool, specify the input by locating NDVI_toa under NorthPlatte.gdb. Save your output into the same geodatabase:

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The result should look like that below.

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We should colorize the ETrF map to help us visualize areas of high and low ET and to improve its

appearance.

We have made a color scheme for ETrF that can be imported and used. That scheme, named

ETrF_frmNDVI.breaks2.xml can be imported in the properties / symbology menu as shown below.

You will want to select “Classified” in the “Show:” window on the left. Then right click on the “Sym.. Range” list in the center of the window and select “Load Class Breaks”. You can direct to the

ETrF_frmNDVI.breaks2.xml scheme. You may want to left click on the “Label” tab and select “Format

Labels” to reduce the number of digits displayed in the label.

The ETrF_frmNDVI.breaks2.xml scheme will assign a different color each 0.1 ETrF. You can select the

color scheme as shown that goes from yellow as low to green and blue as high.

The resulting map should look like:

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Differences in ETrF (relative ET) are clearly visible between various land uses, and various

fields. If we zoom in on the irrigated areas in the center of the basin, we can see the higher ETrF

associated with the irrigated parcels:

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Again, we can see that the center pivot fields are somewhat out of shape because of the use of

WGS84 projection with the Landsat L1T images from the USGS, whereas the native projection

system for the ‘basin’ layer is Albers Equal Area. For a ‘serious’ product, we would want to

reproject the Landsat and associated products into the Albers Equal Area, or vice-versa.

Turn in: Colorized ET from NDVI map and legend for the entire basin area and for a close up showing

some irrigated agriculture (see above).

PART 4. ESTIMATING EVAPOTRANSPIRATION with EEFlux

EEFlux (Earth Engine Evapotranspiration Flux) is a version of METRIC (Mapping

Evapotranspiration at high Resolution with Internalized Calibration) that operates on the Google

Earth Engine system. EEFlux has been developed by the consortium of University of Nebraska-

Lincoln (Ayse Kilic, Doruk Ozturk, Sam Ortega, Babu Kamble, Ian Ratcliffe), Desert Research

Institute and University of Idaho with funding support by Google. EEFlux processes individual

Landsat scenes from any period from 1984 through present and for nearly every land area on the

Globe. EEFlux uses NLDAS and GridMET gridded weather data in the US and CFSV2 gridded

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weather data globally to calibrate the surface energy balance for the image. Actual ET is

calculated as a residual of the surface energy balance as ET = Rn - G - H where Rn is net

radiation, G is soil heat flux and H is sensible heat flux. EEFlux utilizes the thermal band of

Landsat to drive the surface energy balance and short wave bands to estimate vegetation

amounts, albedo, and surface roughness.

Level 1 of EEFlux employs automated calibration of the image. ET is expressed in terms of

ETrF which represents ET as a fraction of reference ETr. In EEFlux, ETr is calculated using the

"tall" alfalfa reference as defined with the ASCE Standardized Penman-Monteith equation. ETrF

is similar to the traditionally used 'crop coefficient'. ETrF = ETact / ETr.

To apply EEFlux, the user first goes to the EEFlux level 1 web site:

https://eeflux-level1.appspot.com/

You should see the following screen:

You can move the orange pin on the Google map into the area of interest (where you want to

find and process a Landsat scene) and specify start and stop dates to search in the date window.

In our case, we are going to process two rows in one path. In the current version of EEFlux, we

need to process the two rows separately. First, we move the pin to near the PathFinder Reservoir

of SE Wyoming and select the date range that contains Landsat 8 for June 20, 2013. You can

zoom in to get better accuracy in dragging the pin. We should see something like:

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Note that I have specified a one month period in June 2013 to search in. When I am ready, I will

press the “Search For Images” button. When the 'search image' button is pressed, EEFlux will

search the Google archive for those Landsat images that reside within the date range and that

cover the area identified by the pin. For areas that are covered by two paths, images from both

paths will be presented. EEFlux will then provide a list of images for the location and permit the

user to select one image to process. EEFlux processes one image (scene) at a time (this will

change in the future). The list of images available includes an assessment of percent cloud cover

for the entire scene as determined by the USGS EROS data center.

In our case, we will select (from the “Select your Landsat image” dropdown) the image for path

35 row 31:

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Once an image is selected, EEFlux will show that image on the basemap window for you to

confirm that you want to process that image. If yes, you can press the “Run EEFlux” button:

Once an image has been run, EEFlux will present a list of data layers that can be displayed in the

map window. These include the:

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a) base map showing the standard Google roads and cities and major land uses for the area. The

user can zoom in and out with the mouse or touch screen.

b) A true color image where red, green and blue bands are displayed as red, green and blue

colors.

c) A 'false color' image where the infrared, red and green bands of Landsat are displayed as red,

green and blue colors.

d) A 'false color' image where the two short-wave bands of Landsat and red band are displayed

as red, green and blue colors.

e) Albedo - Integrated reflectance across the entire solar spectrum based on Tasumi et al., 2008,

ASCE J. Hydrologic Engineering.

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f) NDVI - normalized difference vegetation index where ~0.15 indicates bare soil and ~0.75

indicates full, dense vegetation. NDVI is computed from the red and near infrared bands using

at-surface reflectance computed based on Tasumi et al. 2008.

g) DEM - digital elevation model from the National Elevation Dataset (30 m) for the continental

United States (CONUS) and based on the SRTM data set for the rest of the globe. The DEM is

used to lapse-adjust surface temperature prior to estimation of sensible heat flux.

h) Land Cover - the National Land Cover Dataset (NLCD) from 2011 for CONUS (30 m) and

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from the European Space Agency (1 km).

i) Surface Temperature - 30 m image of surface temperature in Kelvin.

j) Reference ET - The 'tall' (alfalfa) reference ETr calculated from the Gridmet data for the day

of the image using the ASCE Standardized Penman-Monteith equation. The alfalfa ETr is

generally 1.2 to 1.4 times grass reference ETo, depending on wind speed and humidity

conditions.

k) ETrF - ET as a fraction of reference ETr where ETrF = ET/ETr where ET is ET for each

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pixel as produced by EEFlux and ETr is the calculated ETr for the associated 12 km NLDAS (for

CONUS) or CFSV2 (global) grid cell at the Landsat overpass time. ETr is the tall

reference. ETrF can be converted to EToF by multiplying by the ratio of ETr to ETo where ETo

is grass reference ET.

l) Actual ET -- ET for the day of the Landsat overpass expressed in mm/day.

The ETrF and ET-actual images can be downloaded as geo-tiff files using the hot links on the

EEFlux site.

A reference for general equations for EEFlux, based on those of METRIC is available at:

http://www.intechopen.com/books/evapotranspiration-remote-sensing-and-modeling/operational-

remote-sensing-of-et-and-challenges which is an Intech book chapter compiled by Dr. Ayse

Kilic (Irmak) of the Univ. Nebraska-Lincoln and associates at the University of Idaho and Desert

Research Institute in 2012. An original reference for METRIC is Allen et al., 2007 published in

the ASCE J. Irrigation and Drainage Engineering.

We will download the TIFF to our computer by pressing the “Download ETrF” button. Note,

you may have to tell your browser to “allow popups” from the EEFlux site. This will produce a

zipped file having a name like: dd0572ac270e1a3bcac2b2ca423e81df.zip

You can save this to your hard drive and unzip it to access the geotiff file containing the ETrF.

This will be a 280 MB file, approximately, so ‘be patient’ .

We now need to repeat this process for the row 32 that is south of row 31 and that covers the

southern part of the Basin. We will rerun EEFlux from the start to get the pin back. Just go up

to the URL bar in the browser and press the enter key. Move the pin to northern Colorado in the

North Platte Basin:

http://www.intechopen.com/books/evapotranspiration-remote-sensing-and-modeling/operational-remote-sensing-of-et-and-challenges

http://www.intechopen.com/books/evapotranspiration-remote-sensing-and-modeling/operational-remote-sensing-of-et-and-challenges

28

And process and download that image.

You can then mosaic the two ETrF rasters together using the instructions from exercise 5 (first

create the raster data set)

Now we can load the EEFlux-ETrF into our ArcMap project and compare it to the ETrF based

on NDVI only.

Frequently Asked Questions on EEFlux (FAQ):

1. Can more than one Landsat scene and date be processed at the same time? Answer: No,

29

not at this time. Each date and scene are processed independently. They can be downloaded and

stitched together in image processing or ArcMAP applications. Future EEFlux will have means

to select multiple scene areas or multiple dates and process them as a group.

2. Can an ETrF image be 'tuned' to modify the relative wetness or dryness level for the low

ET areas or for the high ET areas? Answer: Level 1, which is the freely distributed version

of EEFlux, does not provide the means to adjust the calibration of the ETrF image. That is done

via the Level 2 version, which can be licensed from EEFlux. The annual license fee covers costs

for continued development on EEFlux. You are welcome to contact EEFlux at: xxxxxxxx

3. How is EEFlux calibrated? Answer: EEFLux is calibrated by assigning values for ETrF for

the 'hot' and 'cold' parts of the surface temperature spectrum of the scene. Currently a single

calibration is performed for the scene. However, we are working on a spatially distributed

calibration within a scene that will improve accuracy. Details on the calibration are given in the

reference above (Kilic article in Intech.).

4. What do I do with an ETrF image once I download it? Answer: You can use the ETrF

image as is to show areas of high and low relative ET. These can be useful to assessing areas of

water stress, areas of irrigation, and spatial distribution of ET. The ETrF image can be

'colorized' using image processing systems such as ERDAS or ArcMAP. The ETrF images can

be used to develop crop coefficient curves and to derive estimates of soil moisture. If total

(integrated) ET over a time period, such as one month or one growing period or one year is

desired, then one will need to process multiple images and then conduct a time-integration using

a spline or similar model as described in Allen et al., (2007) (ASCE J. Irrig. Drain. Engrg.) and

Kilic in Intech. This is done by using the spline function to interpolate between the

instantaneous ETrF images over the time period of interest, producing an ETrF estimate for

every day during that period from the spline, and multiplying that ETrF value by the reference

ET (tall reference) for every day during that period. The daily ET values are then summed over

the period of interest, for example, over one month. Prior to the splining, mitigation for clouds

may be required (see FAQ 5).

5. What do I do with clouded areas of an image? Answer: Generally, all clouds and cloud

shadows in an image need to be identified and 'masked out.' EEFlux is unable to determine ETrF

for clouds and cloud shadows, as well as for jet contrails. The 'holes' left by the masked areas

should be 'filled in' prior to the splining process described in FAQ 4. This can be done by

interpolating between 'adjoining' image dates that have valid ETrF values for the pixel of

interest. During the interpolation, some adjustment for background evaporation caused by

rainfall events may be needed to produce a more 'seamless' patching of ETrF data, as described

in Kjaersgaard et al., 2011 (Hydrological Processes). If adjustment for background evaporation

is not needed, then the spline or linear interpolation can be run, where it is forced to pass through

the cloud and cloud shadow 'holes' and to the next available image.

6. Can the ETrF be converted to a grass-reference based EToF? Answer: Yes. ETrF can be

converted to EToF by multiplying the ETrF image by the ratio of ETr to ETo where ETo is grass

reference ET. The ETr and ETo can be general values for the day or month of the image or they

can be gridded data derived from gridded weather data.

30

7. What if EEFlux does not successfully produce ETrF (it 'times out' and gives a

"something went wrong on the server. Please try again." Message)? The ETrF algorithms

in EEFlux are quite complex and require substantial numbers of calculations. Sometimes the

Google Earth Engine cloud is unable to complete all of the tasks in a specific allotment of time

that is fixed by Google and that depends on the 'traffic' on Google. In that case, the

computations "time out" and you may be given the message above. In that case, try to run the

ETrF process again (select the same image date from the drop down list of images, and select

ETrF). Google Earth Engine keeps a 'cache' of calculated variables and rasters for several hours

in their system, and it is smart enough to use recent calculated variables and rasters for

subsequent runs. Therefore, if you retry several times, it can eventually work. Sometimes, it

might take six or more attempts. Please be patient and keep retrying. We apologize for this, and

we are working to make the EEFlux code more streamlined.

Part 5. Comparing ETrF from NDVI with ETrF derived by Surface Energy Balance

A growing area for remote sensing is the determination of ET using what is called a surface

energy balance (SEB). Some details on SEB are given in the lecture entitled “Lecture on Energy

Balance”. The advantage of SEB is that actual ET is determined, including when ET is reduced

by lack of soil water availability and when ET is increased due to recent rain events that wet the

soil surface and by irrigation. One such SEB method that is used by a number of western and

midwest states including Nebraska is the METRIC process (Mapping ET at high Resolution

using Internalized Calibration). The disadvantage of METRIC is that it takes some expertise in

ET physics to review and calibrate images. This is becoming more and more automated over

time, however. The other disadvantage of SEB methods is that they require a thermal band on

the satellite. Therefore, many SEB methods use Landsat images to get ET at the field scale using

thermal and reflected imagery.

We have applied METRIC to this June 20, 2013 image and have derived an ETrF map. We will

now compare the ETrF from METRIC with the ETrF using some spatial statistics and sampling.

We will do the same thing with the EEFlux created map. The EEFlux ETrF map should look

similar to the METRIC ETrF map, except that it was created using automated calibration and it

uses the “flat” METRIC algorithms that do not make fully correct radiation computations for

mountains. For mountains, METRIC mountain model is preferred.

Let’s first open the METRIC ETrF (and EEFlux ETrF) map and colorize it. The name of the

ETrF image from METRIC is: etrf24_ef_06202013_P35R31_L8_NP_Color.img. That image is

in ERDAS Imagine format and is located in the Exercise 7 data folder provided by the instructor.

ArcMap is able to read the img format.

Add the etrf24_ef_06202013_P35R31_L8_NP_Color.img image to the ArcMap and colorize it

using the same ETrF_frmNDVI.breaks2.xml scheme to colorize it. The result should look

like the following:

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Notice the similarity of the METRIC ETrF map (on left) to the ETrF from NDVI map (on right)

created earlier. However, there are differences, as we will explore below.

The yellow and blue colors outside the Landsat path represent values of -2 and 2 that were

intentionally embedded by METRIC. Let’s get rid of these by excluding them in the symbology

menu. Go to symbology for the ETrF from METRIC and select the “Classify” button. Then

select “Exclusion…” and then specify the values or ranges of values to exclude. In our case, we

exclude -2 and 1.9 – 2.1. These are separated by a semicolon: “-2; 1.9-2.1”. The result should

look like the following side-by-side. Note that you may have to reload the ETrF_frmNDVI.breaks2.xml scheme a second time.

33

Turn in: A screenshot of the side-by-side ETrF from the two methods, similar to that shown

above.

Part 5. Statistical Analyses and Comparisons of ETrF and Surface Temperature

Generally, an engineer or scientist needs to use statistics and quantitative comparisons to

demonstrate differences between two products or estimates, rather than just showing ‘pretty

pictures.’ In the following, we will conduct some sampling of about 180 points in our basin.

Those points were selected by the instructor and are distributed in agricultural (NLCD classes 81

and 82), rangeland (NLCD classes 52 and 71), forest (NLCD classes 41 and 42), wetlands (90

and 95) and water (NLCD class 11).

I will first show you how to make a shape file composed of a number of ‘points’ that will be used

later for sampling. I will provide you with a shape file of about 182 points, of which 150 points

lie within the basin. That shape file is named “Sample Points.” You are welcome to use this

shape file for sampling, but I encourage you to make and use your own for practice.

We create a shape file to enter our sampling point into. Go to ArcCatalog and to the

NorthPlatte.gdb. Right click on the name of your exe6 folder and select “new” and “shapefile.”

34

Give the shapefile the name “Sample_points” and make sure the type is ‘point.’ Also, we need

to define a coordinate system. We will use the WGS1984 coordinate system of Landsat since we

will be sampling the Landsat based image products. Our zone is UTM Zone 13N:

Once the Sample_points.shp is created, add it to the ArcMap project.

Now we will add points to the shape file using the ‘editor.’ Go to ‘editor’ and select “start

editing”. If the “editor” is not present at the top, you can load it by right-clicking the ‘grey’ area

on the menu bar and selecting ‘editor’ from the tool list.

35

Before you start the editor, you may want to zoom in on the ETrF or LST image (or even the

color composite image) so that you can see the features, and then start the editor.

When you select ‘start editing’, choose ‘sample points’ from the menu:

Hit “OK”. Then, from the upper menu bar, select the “Create Features” tool (see below):

36

Then, in the “Create Features” Window, select the name of your point file (i.e., Highlight it):

This should make the “point” tool ‘active’ on the menu bar:

Now, everywhere we click on the map, ArcMap and Editor should create a point. Go crazy

clicking on points in ag fields, rangeland, water and forests in the mountains. Collect about 200

points. You can move around the map using the scroll buttons.

When you are finished, you need to save your points by first select “Stop Editing” from the

“Editor” menu. It will ask you if you wish to save the edits. Say “yes.” You should now have a

“sample points” file that contains those points. The result should look something like (you can

make the symbols larger and colored using symbology):

37

The total number of sample points in the Sample_points_AK shp file is shown below. Note that

about 30 of the points lie outside of the basin. Therefore, when this shp file is used later for

sampling of the NLCD feature, which has been clipped to the North Platte basin, the 30 points

that lie outside the basin will not have any values for NLCD and therefore will not be retained in

the resulting table of sampled values that come from the zonal statistics. This is a good learning

exercise on the behavior of sampling and tables.

38

The total number of sample points in the Sample_points_AK shp file is shown above.

Turn in: Location map of your sample points (over lay onto the NLCD). Note: you can chose to use my

sample_points_AK shape file. However, still please turn in the location map.

39

Zonal Statistics on the Sample points

We will sample the two or three sets of ETrF (from NDVI and from METRIC and from EEFlux),

the surface temperature, the NDVI, and the Land Use (NLCD) for all of our sample points. We

will use Zonal Statistics to do this.

Go to the “zonal statistics as a table” tool:

We will need to sample each of the five images one at a time. Let’s start with the NLCD:

40

Note that we are using the “FID” field from the Sample_points feature, since these are numbered

from 0 to 180-something. We are sampling the NLCD_2011 feature and placing the results into

a table named sampled_NLCD. You will need to browse to find the NLCD_2011 from exercise

6. When the sampling is completed, we can open the sampled_NLCD table and we should see

statistics for all of the points:

41

As mentioned above, about 30 of the 182 locations are omitted from the above table because

they lie outside the North Platte River basin (above Pathfinder Dam) and therefore, they do not

have entries in the table.

Note that the values for min, max, mean are all the same because we are sampling points.

Therefore, to make this more efficient, let’s run again, and select ONLY “Mean”:

43

Now, let’s repeat this process for the two or three ETrF images, the NDVI and the LST. For

consistency, let’s call the tables as sampled_NDVI, sampled_LST, sampled_ETrF_from_NDVI

and sampled_ETrF_from_METRIC and sample_ETrF_from_EEFlux.

44

We should now have five TABLES in our data frame TOC:

45

If we open the sampled_NLCD, we should see about 8 to 10 classes. The NLCD classes for our

image are listed in the table below that is taken from NLCD attribute table (this will have one

more column if EEFlux ETrF is added in addition to METRIC ETrF):

46

Ultimately, we are going to create Excel plots to evaluate our data. To do this, we have to:

1. Join the tables

2. Export to a dbf

3. Import to Excel

4. Create the Graphics and statistics

Step 1. Joining the five tables:

We will join the five tables using sample NLCD as a target table. The FID will be used as

common field that the join will be based on.

48

If you open the sampled_NLCD table, you will see that the table has been expanded to include

the five columns from NDVI table. However, the column heading for NDVI is mean just like the

NLCD heading. Lets rename the NDVI heading by going to its properties as shown below.

Lets change Alias to NDVI.

Also, if the “create index” window pops up, you should select “YES”:

49

Lets join the ETrF from METRIC to the NLCD table also. Repeat the same joining process for

the NLCD table. However, this time you need to select the second “FID” column from the list. It

will have an ‘*’ next to it in the table above. In the join list below, it will be the second FID

entry.

50

Change the column header ‘Mean’ to ETrF_METRIC. Repeat this joining process for the

sampled_ETrF_METRIC and sampled_LST tables. Each time, select the ‘last’ “FID” in the

drop down list for the common field, and each time, rename “Mean” to a unique name.

When we are finished with the joining of the five tables, the final table for Sample_NLCD

should look like the following:

51

Step 2. Exporting the Table to Excel

Now we can export the sampled_NLCD table to a dbf so that we can open it in Excel.

From the open sampled_NLCD table, click in the upper lefthand corner and select “export” and

specify a ‘.dbf’ file type and name. Place the exported file into your project folder so that you

can find it. In the second menu that shows up, you will want to export ‘all records.’

I named my dbf file “Sampled_NLCD_NDVI_ETrF_LST.dbf”.

52

Now, open Excel, and go to the folder where the dbf is stored: (make sure that the “All files

(*.*)” is selected so that the ‘.dbf’ file shows up

The table will be opened in Excel, and it will look like a mess:

53

So, the first thing we should do is to format columns so that they are easier to look at (NLCD

should have no decimals, NDVI and ETrF should have three decimals and LST one decimal).

Secondly, we have columns that we don’t need, namely the RowID, COUNT, AREA columns

that appear five times, plus the extra four FID columns. Therefore let’s delete those columns so

that the result looks like (I renamed the “MEAN” column “NLCD”):

Also, note that in the ArcMAP table, we renamed the “MEAN” headings to NDVI, etc. These

have reverted to “Mean_1”, “Mean_12”, etc. when we exported it. We need to rename these

again. (sorry).

54

Let’s save this file using “Save as” an Excel file.

Step 3: Now we can go to work creating some scatter plots to see relationships.

We are interested in the following questions, for example:

a. How does ETrF from NDVI compare to ETrF from METRIC energy balance?

b. How does the ETrF comparison vary with landuse type?

c. How does ETrF from METRIC or EEFlux relate to NDVI? (how does that vary by land

use type?)

d. How does ETrF from METRIC or EEFlux relate to LST? (and how does that vary by

land use type?)

To answer the “by land use type” questions above, we will use different symbol types for each

land use. First, we need to sort the data by land use type to get all land uses into groups.

55

To sort the rows in Excel by NLCD, highlight all rows, including the header row. Select “Data”

from the menu bar and “Sort”. Select “NLCD” from the “Sort by” dropdown box. We will sort

by Values and from Smallest to Largest:

Recall from the table of NLCD values below the various definitions for NLCD classes. To keep

things simple, let’s group the data into the following general classes to plot with different

symbols:

Water 11

Forest 41 and 42 (deciduous and evergreen forest)

Rangeland 52 (shrub) and 71 (herbaceous) (Shrubs include sagebrush and

herbaceous is used in Wyoming for range grasses like crested wheat

grass, buffalo grass, bluestem, etc.)

Agriculture 81 and 82

56

Wetlands 90 and 95

Note that if you used different sampling points, you may have additional classes that you will

want to include in those groups. We are going to leave out out the 21 and 31 points (developed

open space and barren land) because there are only two of them.

We can colorize the “groups” of NLCD in the NLCD column for our benefit (see screen shot

below). Then we can create our first scatter plot to evaluate questions 1 and 2 above, by:

1. Plotting ETrF_from_NDVI (as Y) vs. ETrF_from_METRIC (as X)

2. Making five series in the graph, for the five groups in our list above.

The result is the graphic shown below, where we have done the following to improve its

appearance. Please make these ‘improvements’ also. It is good practice in making presentation

and report-quality graphics, and is a good reflection on UNL:

a. We made the plotting area square so that the data plot 1:1 as a 45 degree angle.

b. We made the scales of the Y and X axis the same (0 to 1.4)

c. We added a 1:1 line by adding it as a sixth series (just indicate values of 0,1.4 for each

entry) and we made it a solid black line with no symbols

d. We made each series symbol larger and with black outline and with different color and

different symbol shape so that they can be readily distinguished.

e. We made the axis labels have one decimal place and we made them 12 pt font.

f. We added titles for the axes and made them 14 pt

g. We added a title and made it 12 point and we made the title box have white fill so that the

grid lines did not show through

h. We made the total graph outline go away and we made the plotting area outlines black,

not grey.

57

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

ETrF

fro

m N

DV

I

ETrF from METRIC

Sampled Locations in North Platte RiverBasin above Pathfinder Reservoir

Water

Forest

Rangeland

Agriculture

Wetlands

1:1

58

Let’s take time out and answer some questions about the above graph:

Question: Please comment on how well the ETrF from NDVI agrees with the more accurate

ETrF from METRIC for each class. Comment on why the agreement is good for each class or

bad for the class. For example, why is the agreement good for irrigated agriculture, but not very

good for rangeland. (turn in)

Now, let’s make two more scatter graphs to see how ETrF from METRIC behaves vs. NDVI and

vs. LST for all classes. In Excel, let’s copy the graphic that we have made and then we can

modify it to plot ETrF from METRIC (as Y) vs. NDVI (as X):

Note that the 1:1 line does not fit the data anymore because we are plotting ETrF vs NDVI.

NDVI generally ranges from 0.1 for bare soil to 0.7 or 0.8 for full vegetation, and NDVI is

usually negative for water. Negative NDVI occurs for water because the red band reflects more

than the NIR band for water. In the case of soil and vegetation, however, the NIR band reflects

more than the red band.

Instead of the 1:1 line, we have added a ‘trend line’ to the AGRICULTURE points. You can do

this by right clicking on the ag. Data on the plot and selecting trend line. If you highlight the

boxes for ‘display equation’ and ‘display R2’ . We can see a relatively strong linear relationship

between ETrF from energy balance and NDVI.

y = 1.15x + 0.04R² = 0.594

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

ETrF

fro

m M

ETR

IC

NDVI


Water

Forest

Rangeland

Agriculture

Wetland

Linear (Agriculture)

60

Let’s take time out and answer some questions about the above graph of ETrF from METRIC vs.

NDVI:

Question: Please comment on how well the ETrF agrees with NDVI. Which classes show a

strong relationship between ETrF and NDVI? Which ones do not? Give some reasons why they

do or do not. (turn in)

Our last plot is ETrF from METRIC vs. Land Surface Temperature (LST):

You can copy the ETrF vs. NDVI plot and turn it into ETrF vs. LST. Add a trend line for

agriculture like you did for ETrF vs. NDVI:

Turn in the three scattered plot figures created above.

Question: Do you think you can estimate ET from LST alone for specific land use classes, for

example, agriculture, and forest? Explain your reasoning (turn in).

y = -0.035x + 11.57R² = 0.859

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

280 290 300 310 320 330

ETrF

fro

m M

ETR

IC

Land Surface Temperature


Water

Forest

Rangeland

Agriculture

Wetland

Linear (Agriculture)

61

5. REFERENCE and ACTUAL EVAPOTRANSPIRATION

The reference evapotranspiration we are going to use is the ‘alfalfa reference’ ET, generally abbreviated

as ETr. The alfalfa reference ETr contrasts with the other type of reference ET, which is for clipped grass

vegetation. Grass reference ET is usually abbreviated as ETo. Because alfalfa is taller and leafier than

clipped grass, ETr is typically about 20 to 30% greater than ETo. ETr represents the upper limit of ET as

constrained by environmental energy to convert liquid water to vapor. This energy comes mostly from

solar radiation and air temperature.

We will estimate the spatial distribution of the Actual Evapotranspiration (ET) by multiplying reference ET with the ETrF rasters:

ET = ETr X ETrF

Question: ETr for June 20, 2013 at the HPRCC Encampment weather station is 8.68 mm/day,

then what is the daily estimate for ET for the five LU classes sampled? You can use the

sampled data that are in the Excel spreadsheet to make this estimate.

The ETr for the month of June is 270 mm, then what is the monthly ET for the five LU classes

sampled? (turn in)

The final raster map to produce is one of monthly ET for the Two Row Landsat Image for the Month of

June, where alfalfa reference ETr is 270 mm for the month. Turn in.

cive 835: gis in water resources exercise 6: estimating

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