Remote Sensing Presentations
Matthew Drew
Alvan Chao
Lidar (Land Surface)
MODIS
Jeff Deppa
Geof Heidelberger
Polarization Radar
Eric Nielsen
Joel Berenguer
Charles Thomson
Calipso
Courtney Tait
Stephanie Winter
CloudSat/GOES or SODAR
Christina Speciale
Nexrad
Kevin Romero
Lauren Jefferson
George Orpanides
CloudSat
Alexander Harrison
MODIS/AQUA NDVI
Danielle Holden
Nicole Peterson
MODIS AQUA
Chris Sheridan
Aurora Borealis
Tez Ames
Sea-Surface Temperature
Matt Niznik
TRMM
Benedetto Shiraldi
Lightning Detection
Lalitha Kommajosyula
Brian Marmo
Land Color
May 2, 1996
North of Denver, CO
August 16, 1995
Central Brazil
Measuring Vegetation
•By carefully measuring the wavelengths and intensity of visible and
near-infrared light reflected by the land surface back up into space a
"Vegetation Index" may be formulated to quantify the concentrations
of green leaf vegetation around the globe.
Normalized Difference Vegetation Index (NDVI)
•Distinct colors (wavelengths) of visible and near-infrared sunlight
reflected by the plants determine the density of green on a patch of
land and ocean.
•The pigment in plant leaves, chlorophyll, strongly absorbs visible light
(from 0.4-0.5 and from to 0.6-0.7 μm) for use in photosynthesis.
The cell structure of the leaves, on the other hand, strongly reflects
near-infrared light (from 0.7 to 1.1 μm).
•The more leaves a plant has or the more phytoplankton there is in the
column, the more these wavelengths of light are affected, respectively.
violet -
blue - green-yellow-orange
- red -
near IR
What colors do we need to observe?
Ocean
Plants
Soils
Blue and light blue
scattered
Attenuation in the Visible
Wavelengths
(molecular/no aerosol)
Grant Petty, 2004
ozone
765 nm
865 nm
Blue and Light Blue
Daytime Visibility
Distant Dark Objects
Appear Brighter
“Clear” Day
Hazy Day
Daytime Visibility
consider scattering by aerosols
White Sunlight
Top of Atmosphere
Color and Intensity
Distance to the Dark Object
Daytime Visibility
White Sunlight
Top of Atmosphere
Increased contribution of
white light
Object appears lighter
with distance
Longer Distance to the Dark Object
Daytime Visibility
Distant Dark Objects
Appear Brighter
“Clear” Day
Hazy Day
What the satellite sees
White Sunlight
Top of Atmosphere
molecular and aerosol
scattering 400→ 500 nm
near IR
transparent
plants 500-600 nm
ocean water 450-480 nm
Ocean Color
• Locates and enables monitoring of regions of
high and low bio-activity.
– Food (phytoplankton associated with chlorophyll)
– Climate (phytoplankton possible CO2 sink)
• Reveals ocean current structure and behavior
– Seasonal influences
– River and Estuary influences
– Boundary currents
• Reveals Anthropogenic influences (pollution)
• Remote sensing reveals large and small scale
structures that are very difficult to observe
from the surface.
Ocean Color
Haze
Bloom?
Aerosols over Ocean
RV Ron Brown
Central Pacific
Sea of Japan
AOT=0.08
AOT=0.98
Atmospheric Aerosol Correction Procedure
Cloudy
Ln (Optical
Thickness)
Aerosols
Cloudless-Polluted
Molecular
Scattering
Satellite Channels
Molecules
Blue
Aerosol
Green
Red
Near-IR
Atmospheric Aerosol Correction Procedure
Black-dashed: Aerosol Scattering
Blue-dashed: Molecular Scattering
Cloudy
Ln (Optical
Thickness)
More Polluted
Blue
Green
Red
Near-IR
Over 90% of the satellite measured radiance is contributed by
atmospheric aerosols and molecular scattering
Atmospheric Aerosol Correction Procedure
Black-dashed: Aerosol Scattering
Blue-dashed: Molecular Scattering
Cloudy
Ln (Optical
Thickness)
More Polluted
Blue
Green
Red
Near-IR
Over 90% of the satellite measured radiance is contributed by
atmospheric aerosols and molecular scattering
Atmospheric Aerosol Correction
Procedure for Ocean Color
Near IR Wavelengths
Angstrom Exponent
 A (1 )  1 
 
 A (2 )  2 

 A (765 nm)  765 nm 


 A (865 nm)  865 nm 

 A (865 nm)
ln
 A (765 nm)
 765 nm 
 865 nm 



  0 clouds
  increases with increases in aerosol load
Over-Ocean Aerosol Optical Thickness
Neg
Miller, Bartholomew, Reynolds
NDVI
• NDVI is calculated from the visible and nearinfrared light reflected by vegetation.
• Healthy vegetation
– absorbs visible light and reflects a large portion of
the near-IR light
• Unhealthy or sparse vegetation
– reflects more visible light and less near-IR light
• Real vegetation is highly variable
NDVI
NDVI = (NIR — VIS)/(NIR + VIS)
Calculations of NDVI for a given
pixel always result in a number
that ranges from minus one (-1) to
plus one (+1)
--no green leaves gives a value
close to zero.
--zero means no vegetation
--close to +1 (0.8 - 0.9) indicates
the highest possible density of
green leaves.
NASA Earth Observatory (Illustration by Robert Simmon)
Satellite
NDVI
data
sources
NOAA 9
NOAA 7 AVHRR
AVHRR
NOAA 14
AVHRR
NOAA 11
AVHRR
C. Tucker
1985
1990
NPP
SPOT
NOAA 9
1980
NOAA-16
MODIS
1995
NOAA-18
SeaWiFS
NOAA-17
2000
2005
2010
Terra Satellite
• December 1999: Terra spacecraft
• Moderate-resolution Imaging
Spectroradiometer, or MODIS, that
greatly improves scientists’ ability to
measure plant growth on a global scale.
• MODIS: higher spatial resolution (up to
250-meter resolution) than AVHRR
MODIS Global NDVI
Average NDVI 1981-2006
~40,000 orbits of
satellite data
C. Tucker
Marked contrasts between the dry and
wet seasons
C. Tucker
(~300 mm/yr @ Senegal)
Beltsville USA winter wheat biomass
C. Tucker
S NDVI
vs. total dry biomass
Explained 80% of
biomass
accumulation
C. Tucker
Species mapping with physiological
indices
Meg Andrew
Spectral Indices: NDVI
NDVI 
RNIR  Rred
RNIR  Rred
Creosote
Ag
NDVI = 0.922
NDVI = 0.356
Meg Andrew, UC Davis
Global Vegetation Mapping
SeaWiFS Ocean Chlorophyll Land
NDVI
5 SeaWiFS land bands
Tasmanian Sea
A break in the clouds over the Barents Sea on August 1, 2007 revealed a large, dense
phytoplankton bloom to the orbiting MODIS aboard the Terra satellite. The bright
aquamarine hues suggest that this is likely a coccolithophore bloom. The visible portion of
this bloom covers about 150,000 square kilometers (57,000 square miles) or roughly the
area of Wisconsin.
Supplements
a) The light path of the water-leaving radiance. b) Shows the attenuation of the water-leaving radiance. c)
Scattering of the water-leaving radiance out of the sensor's FOV. d) Sun glint (reflection from the water
surface). e) Sky glint (scattered light reflecting from the surface). f) Scattering of reflected light out of the
sensor's FOV. g) Reflected light is also attenuated towards the sensor. h) Scattered light from the sun which is
directed toward the sensor. i) Light which has already been scattered by the atmosphere which is then
scattered toward the sensor. j) Water-leaving radiance originating out of the sensor FOV, but scattered
toward the sensor. k) Surface reflection out of the sensor FOV which is then scattered toward the sensor. Lw
Total water-leaving radiance. Lr Radiance above the sea surface due to all surface reflection effects within
the IFOV. Lp Atmospheric path radiance. (Gordan and Wang)
Sky Imaging
500 nm
AMF
RV Ron Brown
Central Pacific
Sea of Japan
Niamey, Niger
AOT=0.08
AOT=0.98
AOT=2.5-3
Nighttime Visibility
Distant Bright
Objects
are dimmer
Attenuation in the Visible Wavelengths
Grant Petty, 2004
Aerosol Hygroscopic Growth
• Deliquescence
– Dry crystal to solution
droplet
• Hygroscopic
– Water-attracting
• Efflorescence
– Solution droplet to
crystal (requires
‘nucleation’)
• Hysteresis
– Particle size and
phase depends on
humidity history
ENVI-1200 Atmospheric Physics
Atmospheric Correction Methods
• Develop Theoretical Atmosphere. Include:
• Rayleigh Scattering - (Strongest in Blue region)
• Ozone
• Aerosols - (Absorption and Scattering Characteristics)
• Use Data from Infrared (IR) band and assume that all of this signal
comes from the atmosphere to get knowledge of aerosols.
• Solve Radiative Transfer Equation
• Geometry
• Location (types of aerosols possible)
• Other considerations:
– Sun Glint. Avoid - Use wind speed to estimate surface roughness.
– White Caps. Measure - Use wind speed to estimate coverage.
Atmospheric Aerosol Correction
Procedure
Cloudy
Clear H2O
Upwelling
Radiance
At Satellite
Cloudless-Polluted
Biological
Blue
Green
Red
Near-IR
History of the NDVI
& Vegetation Indices
Compton Tucker
NASA/UMD/CCSPO
Vegetation Indices from Susan Ustin
Index
Simple Ratio
Normalized
Difference
Vegetation Index
Formula
Details
R NIR
RR
Green vegetation cover.
Various wavelengths,
depending on sensor. (e.g.
NIR = 845nm, R=665nm)
Pearson, 1972
RNIR  RR
RNIR  RR
Green vegetation cover.
Various wavelengths,
depending on sensor. (e.g.
NIR = 845nm, R=665nm)
Tucker 1979
C1 =6; C2=7; L=1; G=2,5
Enhanced
Vegetation Index
Perpendicular
Vegetation Index
Soil Adjusted
Vegetation Index
Modif ied Soil
Adjusted
Vegetation Index
Transformed Soil
Adjusted
Vegetation Index
Soil and
Atmospherically
Resistant
Vegetation Index
C. Tucker
Citation
Hu ete 1997
Rs Rv2  (NIRs NIRv)2
NIR R
1 L
NIR R  L
a NIR  aR  b 
R  a ( NIR  b)  0.08(1  a 2 )
NIR R


2.5  
1 NIR 6R  7.B
Perpendicular distance from
the pixels to the soil line.
L = soil adjusted factor
L = (1-2a x(NIR-aR) x NDVI )
Self ad justing L:f on to
optimize for soil effects.
Higher dy namic range.
Richardson
and Wiegand
1977
Hu ete 1988
Qi et al 1994
a=slope of soil line
b=intercept of soil line
Baret and
Gu yot 1991
More independent of surface
brightness
Hu ete et al
1997
Winter wheat biomass “harvest”
C. Tucker
This figure shows four typically observed wavelength bands and the water leaving
radiance in high (dotted) and low (solid) chlorophyll waters without the atmospheric
signal (lower curves) and with the atmospheric signal (upper curves). The satellite
observes the water leaving radiance with the signal due to the atmosphere (upper
curves). [Gordon and Wang]