U.S. Department of the Interior

U.S. Geological Survey

Matt Schauer1, Gabriel Senay2, MacKenzie Friedrichs3

1Innovate!,Inc., contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, USA. 2USGS EROS Center3Stinger Ghaffarian Technologies (SGT) Inc. contractor to the USGS EROS Center, Sioux Falls, South Dakota, USA.

*Work performed under USGS contract G15PC00012.

Jim Rowland

Jim Verdin

Gabriel Senay

SGT Inc.:

Mac Friedrichs

Stefanie Bohms

Innovate!, Inc.:

Matt Schauer

Sioux Falls, SD

USGS Earth Resources Observation and Science (EROS) Center

The Landsat Archive Landsat 5 launched in 1984; ended mission in 2013

“longest-operating Earth observation satellite”

Landsat 7 in 1999 and Landsat 8 in 2013

Both continue to provide daily global data

USGS Fact Sheet 2015–3081, November 2015

USGS Fact Sheet 2015–3081, November 2015

Field Flux Tower

Landsat 8

Evapotranspiration =

transpiration + evaporation

Operational Simplified Surface Energy

Balance (SSEBop) Modeling Approach

LST (Ts)

Tmax

ETfraction ETo

ETa

Air Temp

Weather

Data

Radiation,

Temp, Wind,

RH, Pressure

Senay et al 2013.

Adapted the “hot” and “cold” pixel concept from SEBAL (Bastiaanssen et al., 1998) and METRIC

(Allen et al., 2007) to calculate ET fraction and combine it with ETo.

Land Surface Temp

ETf

0.0

1.0

Ts coldTs

Well-watered

fields/pixels

Bare/dry

fields/pixels

(80 F)

Monthly, seasonal, and annual totals.

4 path/rows

1984-2014

Landsat 5

Landsat 7

Landsat 8

This is a “false

color” image of a

Landsat 8 scene

for PVID from July

30th, 2014.

This image is created

by combining the

Landsat 8 bands 5, 4,

and 3.

red

This same Landsat 8

scene for PVID from July

30th, 2014 calculated to

show land surface

temperature (LST).

One of the primary

inputs for SSEBop!

DN to at-sensor radiance,

then at-surface temp,

atmospheric correction

including emissivity

NDVI is calculated by

using the Near Infrared

(NIR) band (which is

Band 5 in Landsat 8)

and the Red band (Band

4 in Landsat 8).

Potential ET

Maximum Air

Temp (Tmax)

Median dT dataset:predefined temperature

difference between Th

and Tc for each pixel

Varies seasonally and across

space but not year to year

Tc is a fraction of

Maximum Air

Temperature Tmax

multiplied by c

We calculate c

through a Tcorr

raster (a ratio of

LST and Tmax) for

every scene

Calculation of c

Tcorr raster is

masked by high

NDVI areas and

several other

conditions

c is the Tcorr

value at the

“coldest”,

wettest, well-

vegetated pixel.

n 872

mean 0.964

SD 0.009

n 799

mean 0.973

SD 0.007

Ready for SSEBop!𝑇𝑐 = 𝑐 ∗ 𝑇𝑚𝑎𝑥

𝑇ℎ = 𝑇𝑐 + 𝑑𝑇

𝐸𝑇𝑓 =𝑇ℎ − 𝐿𝑆𝑇

𝑑𝑇

𝐸𝑇𝑎 = 𝐸𝑇𝑓 ∗ 𝑃𝐸𝑇 ∗ 𝑘

Legend

Tc = cold pixels

Th = hot pixels

c = C-factor

Tmax = maximum air temperature

LST = land surface temperature

ETf = ET fraction

PET = potential ET

k = scaling factor (we use 1.25)

ETa = actual ET

The

preliminary

result of

SSEBop: the

ET fraction

This is the

factor used to

multiply the

potential ET

data in order to

estimate the

ET signal

Final ET output for

scene overpass

ET calculation using

Python and Arcpy

Pixel gaps

due to clouds

or Landsat 7

scan lines

Gap-filling

with

Python

Gap Filling

ET Fraction

1.05

0

Gaps are filled with

interpolated values from

valid pixels in temporally

adjacent scenes

through a simple linear

interpolation equation

ET fractions are

multiplied by daily

potential ET rasters

and aggregated to

create monthly,

seasonal, and

annual outputs

Monthly Sum Seasonal (May-Sept) Sum

Annual Sum Monthly

Aggregation

using Python

1984 1989 1994 1999

2004 2009 2014

Annual Sums

of PVID

every 5 years

Actual ET

in mm

2000 mm

0 mm

1000 mm

Spatial average for PVID for both annual and seasonal sums

Demonstrates a general decline in ET over time

PVID Monthly Averages over 31 years (1984-2014)

Crop Data types from the USDA NASS at the

same spatial resolution of Landsat

ET estimates based on crop types show

Alfalfa is the predominant crop in water

usage based on ET

HUC8 Area of

Interests in San

Joaquin Valley

Eddy-covariance

Flux Tower

Investigating Hydrological Boundaries

in the San Joaquin Valley, CA

Validation of ET results using

Independent Field Measurements

Conclusions

Acknowledgements

http://water.usgs.gov/watercensus/