Land Color
description
Transcript of Land Color
Remote Sensing Presentations
Matthew Drew Lidar (Land Surface)Alvan Chao MODIS
Jeff Deppa Polarization RadarGeof Heidelberger
Eric Nielsen CalipsoJoel BerenguerCharles Thomson
Courtney Tait CloudSat/GOES or SODARStephanie Winter
Christina Speciale Nexrad
Kevin Romero CloudSatLauren Jefferson George Orpanides
Alexander Harrison MODIS/AQUA NDVI
Danielle Holden MODIS AQUANicole Peterson
Chris Sheridan Aurora Borealis
Tez Ames Sea-Surface Temperature
Matt Niznik TRMM
Benedetto Shiraldi Lightning Detection
Lalitha KommajosyulaBrian Marmo
Land ColorMay 2, 1996North of Denver, CO
August 16, 1995Central Brazil
•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.
Measuring Vegetation
violet - blue - green-yellow-orange - red - near IR
What colors do we need to observe?
Ocean Plants Soils
Attenuation in the Visible Wavelengths
(molecular/no aerosol)
Grant Petty, 2004
Blu
e a
nd lig
ht
blu
esc
att
ere
d
ozone
765 nm
865 nm
Blue and Light Blue
Daytime Visibility
Distant Dark ObjectsAppear Brighter
“Clear” Day
Hazy Day
Daytime Visibility
White Sunlight
Top of Atmosphere
Color and Intensity
Distance to the Dark Object
consider scattering by aerosols
Daytime Visibility
White Sunlight
Top of Atmosphere
Increased contribution ofwhite light
Object appears lighterwith distance
Longer Distance to the Dark Object
Daytime Visibility
Distant Dark ObjectsAppear Brighter
“Clear” Day
Hazy Day
What the satellite sees
White Sunlight
Top of Atmosphere
molecular and aerosol scattering 400→ 500 nm
ocean water 450-480 nmplants 500-600 nm
near IRtransparent
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?
RV Ron Brown
Central Pacific
AOT=0.08
Sea of Japan
AOT=0.98
Aerosols over Ocean
Atmospheric Aerosol Correction Procedure
Blue Green Red Near-IR
Ln (Optical Thickness)
Cloudy
Cloudless-Polluted
Molecular Scattering
Aerosols
Satellite Channels
Aerosol
Molecules
Atmospheric Aerosol Correction Procedure
Blue Green Red Near-IR
Cloudy
More Polluted
Ln (Optical Thickness)
Black-dashed: Aerosol ScatteringBlue-dashed: Molecular Scattering
Over 90% of the satellite measured radiance is contributed by atmospheric aerosols and molecular scattering
Atmospheric Aerosol Correction Procedure
Blue Green Red Near-IR
Cloudy
More Polluted
Ln (OpticalThickness)
Black-dashed: Aerosol ScatteringBlue-dashed: Molecular Scattering
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
1 1
2 2
( )
( )
(765 ) 765
(865 ) 865
(865 )ln
(765 )
765865
A
A
A
A
A
A
nm nm
nm nm
nmnm
nmnm
0 clouds
increases with increases in aerosol load
Miller, Bartholomew, Reynolds
Neg
Over-Ocean Aerosol Optical Thickness
NDVI
• NDVI is calculated from the visible and near-infrared 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
NDVINDVI = (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)
NOAA 11AVHRR
1980 200019901985 201020051995
NOAA 7AVHRR
NOAA 9AVHRR
NOAA 14AVHRR
SeaWiFS
SPOT
MODIS
NOAA-16
NPP
NOAA 9 NOAA-17
Satellite Satellite NDVI NDVI data data
sourcessources
NOAA-18
C. Tucker
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 1981-2006Average NDVI 1981-2006
~40,000 orbits of ~40,000 orbits of satellite datasatellite data
C. Tucker
Marked contrasts between the dry and Marked contrasts between the dry and wet seasonswet seasons
(~300 mm/yr @ Senegal)(~300 mm/yr @ Senegal)C. Tucker
Beltsville USA winter wheat biomass
C. Tucker
NDVI vs. total dry biomass
Explained 80% of biomass
accumulation
C. Tucker
Species mapping with physiological indices
Meg Andrew
Spectral Indices: NDVI
redNIR
redNIR
RR
RRNDVI
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)
500 nm
RV Ron Brown
Central Pacific
AOT=0.08
Sea of Japan
AOT=0.98
AMF
Niamey, Niger
AOT=2.5-3
Sky Imaging
Nighttime Visibility
Distant Bright Objectsare dimmer
Attenuation in the Visible Wavelengths
Grant Petty, 2004
ENVI-1200 Atmospheric Physics
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
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
Blue Green Red Near-IR
UpwellingRadianceAt Satellite
Cloudy
Cloudless-PollutedClear H2O
Biological
History of the NDVIHistory of the NDVI& Vegetation Indices& Vegetation Indices
Compton TuckerCompton TuckerNASA/UMD/CCSPONASA/UMD/CCSPO
Index Formula Details Citation
Simple Ratio Green vegetation cover.Various wavelengths,depending on sensor. (e.g.NIR = 845nm, R=665nm)
Pearson, 1972
NormalizedDifference
Vegetation Index
Green vegetation cover.Various wavelengths,depending on sensor. (e.g.NIR = 845nm, R=665nm)
Tucker 1979
EnhancedVegetation Index
C1 =6; C2=7; L=1; G=2,5Huete 1997
PerpendicularVegetation Index
Perpendicular distance fromthe pixels to the soil line.
Richardsonand Wiegand
1977
Soil AdjustedVegetation Index
L = soil adjusted factor Huete 1988
Modified SoilAdjusted
Vegetation Index
L = (1-2a x(NIR-aR) x NDVI)Self adjusting L:f on tooptimize for soil effects.Higher dynamic range.
Qi et al 1994
Transformed SoilAdjusted
Vegetation Index
a=slope of soil lineb=intercept of soil line
Baret andGuyot 1991
Soil andAtmospherically
ResistantVegetation Index
More independent of surfacebrightness
Huete et al1997
BRNIR
RNIR
.7615.2
) 1 ( 08 . 0 ) ( 2 a b NIR a R
b aR NIR a
R
NIR R
R
RNIR
RNIR
RR
RR
22 )( NIRvNIRsRvRs
LLRNIR
RNIR
1
Vegetation Indices from Susan UstinVegetation Indices from Susan Ustin
C. Tucker
Winter wheat biomass “harvest”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]