Remote Sensing Introduction

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Remote Sensing (Introduction)
Estimation of Regional Evapotranspiration through
Remote Sensing
"I can foretell the way of celestial bodies, but
can say a little about the movement of a small
drop of water"Galileo Galilei, 1564-1642
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Materials used for the introduction part
Textbook Jensen, John R., 2006, Remote Sensing
of the Environment An Earth Resource
Perspective, 2nd Ed., Upper Saddle River, NJ
Prentice Hall, 592 pages
Online Teaching Materials (ppt slides)
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Overview of Data Acquisition by Satellite RS
Jensen 2000
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• Geometric corrections compensate for systematic
  effects, including  panoramic distortion,
  Earth's rotation and curvature, and variations in
  the satellite's orbital altitude relative to the
  reference ellipsoid

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Remote Sensing Raster (Matrix) Data Format
Jensen 2000
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Atmospheric Windows (AW) in the Electromagnetic
Spectrum
AW are caused when atmospheric gases/particles do
not absorb the radiation
Jensen 2000
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Diurnal Temperature Cycle of Typical Materials

• Beginning at sunrise, the earth begins
  intercepting mainly short wavelength energy (0.4
  - 0.7) from the Sun
• From 600 am to 800 pm, the terrain intercepts
  the incoming short wavelength energy and reflects
  much of it back into the atmosphere where we can
  use optical remote sensors to measure the
  reflected energy
• Some of the incident short wavelength energy is
  absorbed by the terrain and then re-radiated back
  into the atmosphere as thermal infrared long
  wavelength radiation (3 - 14)
• The outgoing longwave radiation reaches its
  highest value during the day when the surface
  temperature is highest
• This peak usually lags two to four hours after
  the midday peak of incoming shortwave radiation
  (to heat the soil)
• Reflected short wavelength energy and emitted
  long wavelength energy causes an energy surplus
  to take place during the day
• Both incoming and outgoing shortwave radiation
  become zero after sunset, but outgoing longwave
  radiation continues all night

Jensen 2000
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Peak Period of Daily Outgoing Longwave Radiation
the Diurnal Radiant Temperature of Objects
Jensen 2000
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Thermal Band Sensors
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ETM Enhanced Thematic Mapper Landsat 7
TM Thematic Mapper Landsat 7
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Chronological Launch and Retirement History of
the Landsat Satellite Series
Jensen 2000
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NOAA AVHRR Advanced Very High Resolution
Radiometer
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Global Normalized Difference Vegetation Index
(NDVI) Image Produced Using (AVHRR) Imagery
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Earth Observing System Measurements with TERRA
ASTER/MODIS
Atmosphere Cloud Properties
MODIS, ASTER Radiative Energy
Fluxes MODIS Aerosol Properties MODIS Atmo
spheric Temperature MODIS Atmospheric
Humidity MODIS
Land Land Cover/Land Use Change MODIS,
ASTER Vegetation Dynamics MODIS,
ASTER Surface Temperature MODIS, ASTER Fire
Occurrence MODIS, ASTER Volcanic
Effects MODIS, ASTER
Ocean Surface Temperature MODIS Phytoplankton
MODIS Dissolved
Organic Matter MODIS
Cryosphere Land Ice Change ASTER Sea Ice
MODIS, ASTER Snow Cover MODIS, ASTER
Jensen 2000
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ASTER Advanced Spaceborne Thermal Emission and
Reflection Radiometer
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MODIS Moderate Resolution Imaging
Spectroradiometer
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Normalized Difference Vegetation Index (NDVI)

• Estimating net primary production over varying
  biome types (e.g. Lenney et al., 1996)
• Identifying ecoregions (Ramsey et al., 1995)
• Monitoring phenological patterns of the earths
  vegetative surface
• Assessing the length of the growing season and
  dry-down periods (Huete and Liu, 1994).

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Soil Adjusted Vegetation Index (SAVI)

• Take advantage of calibrated hyperspectral sensor
  systems such as the moderate resolution imaging
  spectrometer - MODIS (Running et al., 1994)
• The improved indices incorporate a soil
  adjustment factor and/or a blue band for
  atmospheric normalization
• The soil adjusted vegetation index (SAVI)
  introduces a soil calibration factor, L, to the
  NDVI equation to minimize soil background
  influences resulting from first order soil-plant
  spectral interactions (Huete et al., 1994)
• An L value of 0.5 minimizes soil brightness
  variations and eliminates the need for additional
  calibration for different soils (Huete and Liu,
  1994)

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Soil and Atmospherically Adjusted Vegetation
Index (SARVI)

• Huete and Liu (1994) integrated the L function
  from SAVI and a blue-band normalization to derive
  a soil and atmospherically resistant vegetation
  index (SARVI) that corrects for both soil and
  atmospheric noise
• Requires prior correction for molecular
  scattering and ozone absorption of the blue, red,
  and near-infrared remote sensor data, hence the
  term p.

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ESTIMATION OF REGIONAL EVAPOTRANSPIRATION IN THE
GREAT PLAINS
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Evapotranspiration

• Loss of water due to evaporation from the land
• surface and loss of water from the plant due to
• transpiration plays a significant role in
  regional and
• global climate through hydrological cycle

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Why Evapotranspiration?

• 80 of the consumptive use of water by
  agriculture
• 8.2 million acres of irrigated lands in NE
• 90 of the precipitation goes back to the
  atmosphere
• Great American desert ? Grains Bowls of world ?
  ?
• Water is an important issue in Great Plains.

Water level changes in the Ogallala Aquifer
(Cunningham, William P. et. al., Environmental
Science, 7th edition, McGraw Hill 2003.)
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REGIONAL/STATE WATER PROBLEM

• The long-term viability of this natural resource
  is threatened by several consecutive years of
  drought and over-pumping of groundwater supply
• Reduced well-output and falling groundwater
  tables in much of the Ogallala aquifer
• Litigation between downstream and upstream
  users has placed restrictions on the amount of
  water available to growers in several major
  watersheds
• A recent United States Supreme Court ruling to
  resolve a conflict between Colorado, Nebraska,
  and Kansas will restrict groundwater withdrawals
  for agricultural irrigation in Nebraska
• These constraints require limits on the amount of
  irrigation water that can be pumped by farmers,
  restrict drilling of new wells, and require
  flowmeters on existing wells in NE
• Local, state and federal water management
  regulatory agencies need good quality water use
  estimates by different crop surfaces to assess
  short and long-term water management, planning,
  and allocations

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NEBRASKA WATERSHEDS
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Why Quantify ET?

• Net Depletion from Ground-water Pumping (if
  unmeasured)
• Compare actual ET with Water Rights
• Evaluate ET with and without Irrigation
• Calculate Natural and Irrigation-Induced Recharge
  to Aquifers (i.e., close a water balance for a
  watershed)
• Land Use Planning
• Determine Actual ET for Developing Better Crop
  Coefficient Curves

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High Frequency Evapotranspiration
MeasurementsSouth Central Agricultural Lab Near
Clay Center, NE
Why Remote Sensing?
Eddy correlation system Measurement of
evapotranspiration during the growing season
Bowen Ratio Energy Balance System Measurement
of evaporative losses during the non-growing
season
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Why Remote Sensing?

• Synoptic coverage
• Temporal resolution
• Spatial domain
• GIS compatible
• Cost effective

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ET mapping with SEBAL and METRICtm

• Surface Energy Balance Algorithm for Land
• Mapping EvapoTranspiration with high Resolution
  and Internalized Calibration

Dr. Wim Bastiaanssen, WaterWatch, The
Netherlands beginning in 1990 SEBAL is
commercially applied in the U.S.A. by SEBAL-North
America
METRICtm is energy-balance-based ET mapping tied
down and partly calibrated using ground-based
reference ET (from weather data) METRICtm is
designed to work well in advective conditions of
the western U.S.
Allen and Tasumi, University of Idaho, Kimberly
beginning in 2000 rooted in SEBAL2000
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UNL Projects

• What is the impact of riparian vegetation on
  water use and groundwater surface water
  interaction?
• How much do different vegetation functional types
  and species transpire?
• What are the impacts of change in riparian
  vegetation type on evapotranspiration?
• Republican River Basin
• Project PIs Derrel L. Martin, Ayse Irmak, Suat
  Irmak, Shashi Verma
• Two graduate students Ramesh Singh, Octavio
  Lagos

• Accurately quantify net Consumptive Water Use
  (CWU) for different crops and range
• Turn them into immediately usable products for
  planning, managing and regulating groundwater
  resources
• The North Platte NRD, and the South Platte NRD
• Project PIs Gary W. Hergert, Derrel L. Martin,
  Ayse Irmak, Suat Irmak, Shashi Verma
• Partners Richard G. Allen, Masahiro Tasumi

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SEBAL Principle
Rn - G - ?ET - H ? 0
Where Rn Net radiation G Soil heat
flux ?ET Latent heat flux H Sensible heat
flux ? Change in storage

• Principle of Energy Conservation
• Energy arriving at the surface Energy leaving
  the surface
• Only vertical fluxes considered (Advection
  ignored)
• Minor energy components also ignored
  (photosynthesis, metabolic activities of crops)

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• DN to Radiance

Where L Spectral radiance at the sensor
aperture (watt m-2 ster-1 µm-1) Lmax Spectral
radiance scaled to Qcalmax (watt m-2 ster-1
µm-1) Lmin Spectral radiance scaled to
Qcalmin (watt m-2 ster-1 µm-1) Qcal Quantized
calibrated pixel value Qcalmin Minimum
quantized calibrated pixel value corresponding to
Lmin Qcalmax Maximum quantized calibrated
pixel value corresponding to Lmax
2. Radiance to Reflectance
Where r Planetary reflectance (unitless) L
Spectral radiance at the sensor aperture (watt
m-2 ster-1 µm-1) d Earth-sun distance
(astronomical unit) Esun Mean solar
exoatmospheric irradiances (watt m-2 µm-1) ?
Solar zenith angle (degree)
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3. Surface albedo at the top of atmosphere
Where atotal Surface albedo at the top of
atmosphere (unitless) c Weighing coefficient
for each band (unitless) r Planetary
reflectance (unitless)
4. Surface Albedo
Where apath Path radiance (0.025 - 0
.040) tsw Transmittance (unitless) tsw 0.75
2 10-5 Z z Elevation above mean sea
level (m)
5. Normalized Difference Vegetation Index
Where r4 Near infrared band
reflectance r3 Red band reflectance
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6. Thermal Infrared Surface Emissivity
Van de Griend and Owe, 1993
Where es Thermal infrared surface
emissivity (unitless)
7. Surface Temperature
Where Ts Surface temperature (K) K1 K2
Calibration constants
8. Incoming Shortwave Radiation
Where Rs? Incoming shortwave radiation
(watt m-2) Gsc Solar constant (1367 watt
m-2) dr Inverse square of relative sun-earth
distance factor (unitless)
(J Julian day)
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9. Incoming Longwave Radiation
(Allen et al., 2000)
(Bastiaanssen et al., 1998)
Where Rl? incoming long wave radiation (watt
m-2) s Stefan Boltzmann constant (5.67
10-8 Watt m-2 K-4) Tref Reference temperature
(Well watered pixel surface temp) (K)
10. Outgoing Long Wave Radiation
Where RL? Outgoing shortwave radiation
(watt m-2)
11. Net Radiation
Where Rn Net radiation (watt m-2)
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Net Radiation
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12. Soil Heat Flux
(Allen et al., 2000)
Bastiaanssen, 2000
Melesse and Nangia, 2005
Moran et al., 1989
Bastiaanssen et. al, 1998
Kustas and Daughtry, 1990)
Bastiaanssen, 2000
UNL, 2006
July to December
July to December
January to June
January to June
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13. Surface Roughness for Momentum Transfer
(Allen et al., 2000)
Bastiaanssen et al., 1998
Moran 1990
Where Zom Surface roughness for momentum
transfer (m)
14. Friction velocity
Where U Friction velocity (m s-1) Ux
Wind velocity (m s-1) Zx Elevation of wind
measurement (m) K Von-Karmans constant
(0.41)
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15. Aerodynamic Resistance to Heat Transport
Where rah Aerodynamic resistance (s
m-1) Z1 Z2 Height above the ground surface
(2 m and 0.01 m respectively) U Friction
velocity (m/s) K Von-Karmans constant
16. Differential Temperature
dT Temperature difference between Z1 and Z2 (K)
a b are constants determined by dry and wet
pixels selection.
17. Air Temperature
Where Ta Air temperature (K)
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18. Atmospheric Pressure
Where P Atmospheric pressure (kPa) Ta
Atmospheric temperature (K), Z Elevation above
the mean sea level (m)
19. Air Density
Where ?air Air density (Kg/m3) P
Atmospheric pressure (kPa), Ta Air temperature
(K)
20. Sensible Heat Flux
Where Cair Heat capacity of air (1004 J
kg-1 K-1) dT Differential temperature
(K) rah Aerodynamic resistance to heat
transfer (s m-1)
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21. Monin-Obukov Length Parameter
Where g Acceleration due to gravity (9.81
m s-2) Cair Heat capacity of air (1004 J kg-1
K-1) U Friction velocity (m/s) Ts
Surface temperature (K)
22. Correction height parameters
Where Height 200 m for momentum correction
and 2 m for heat correction
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23. Stability Correction Factor for Momentum
Transfer
For Llt0
For L0
Where ?m(200) Stability correction factor for
momentum transfer
24. Stability Correction Factor for Heat
Transfer
For Llt0
For L0
Where ?h(2) Stability correction factor for
heat transfer
25. Modified Friction Velocity
Where ?h(200) Stability correction factor
for momentum transfer
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26. Modified Aerodynamic Resistance to Heat
Transfer
Where ?h(2) Stability correction factor for
heat transfer
27. Instantaneous Evaporative Fraction
Where EF Instantaneous evaporative fraction
(unitless)
28. Latent Heat of Vaporization
Where ? Latent heat of vaporization (J
kg-1) Ts Surface temperature (K)
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29. 24-hour Extraterrestrial Radiation
Where Ra24 Daily extraterrestrial radiation
(watts m-2) Gsc Solar constant (1367 watts
m-2) ?s Sunset hour angle
(radian) d Solar decimation
(radian) f Latitude (radian)

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30. Daily Net Radiation
Where Rn24 Daily net radiation (watts
m-2) tsw Transmittance (unitless) tsw
0.75 2 10-5 Z

31. Daily Actual Evapotranspiration
Where Etc Daily actual evapotranspiration
(mm day-1) ? Latent heat of
vaporization (J kg-1)
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SEBAL/METRIC Methodology
INPUT
Wind Velocity
Multispectral Satellite Data
Sensor Calibration
U
a
NDVI
?air
Zom
e
?
Ts
dT
Ta
RS?
P
RL?
?air
RN
rah
Hini
G
L
EF
Hfinal
Rn24
Ra24
SEBAL/METRIC
G24
OUTPUT
ETa
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LANDSAT Image Coverage Path-29, Row-32
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Landsat Path-29 Row-32 Image Footprint
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Landsat 5 TM August 07, 2005 (Bands 4,3,2)
SOUTH CENTRAL AGRICULTURAL LAB NEAR CLAY CENTER
CLOUD COVER
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Surface Temperature
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• ET Map
• May 19, 2005

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ET Map June 20, 2005
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ET Map July 22, 2005
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ET Map Aug. 07, 2005
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ET Map Sept. 08, 2005
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ET Map Sept. 16, 2005
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• ET Map
• Oct. 18, 2005

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Landsat 5 TM August 07, 2005
SOUTH CENTRAL AGRICULTURAL LAB NEAR CLAY CENTER
CLOUD COVER
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LANDSAT 5, TM August 07, 2005 at South
Central Agricultural Laboratory
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Instrumentations for Bioatmospheric measurements
installed at South Central Agricultural
Laboratory (SCAL), Clay Center, Nebraska
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LANDSAT 5, TM August 07, 2005 ET Field at SCAL,
UNL, Clay Center
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Comparison of measured and modeled energy flux
parameters
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Comparison of Measured and Modeled
Evapotranspiration
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July 22, 2005
May 19, 2005
June 20, 2005
August 07, 2005
Sept 08, 2005
Oct 18, 2005
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Observed vs. Estimated ET
June 20, 2005
May 19, 2005
Average 6.13 mm/day Observed 6.09 mm/day
Average 2.11 mm/day Observed 2.18 mm/day
July 22, 2005
August 07, 2005
Average 6.52 mm/day Observed 5.41 mm/day
Average 7.06 mm/day Observed 7.99 mm/day
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Observed vs. Estimated ET
September 08, 2005
September. 16, 2005
Average 4.45 mm/day Observed 3.37 mm/day
Average 4.72 mm/day Observed 6.60 mm/day
October 18, 2005
Average 1.53 mm/day Observed 0.95 mm/day
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ET Maps Are Valuable

• Determining actual ET
• Refining Crop Coefficient Curves
• Water Rights Conflicts
• Ground-water Management
• Consumption by Riparian Vegetation

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Imperial Valley, CA via Landsat 7

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Tampa Bay,Florida Impact of Ground-water
pumping by City on Natural Vegetation
Agriculture
Water (Gulf of Mexico)
The brighter the pixel, the greater the
Evapotranspiration
Forests Wetlands
Tampa Bay City
24-hour Evapotranspiration - July 10, 1999
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Aggregation of ET for Hydrologic units
SEBAL Tampa Bay area, Florida July, 1999
ET, inches/week
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Seasonal ET for SE Idaho
Idaho from Landsat
Major Irrigated areas in Idaho and areas of
METRIC application