Land and Ocean Color

•Though we often take the plants and trees around us for granted, almost every aspect of our lives depends upon them. •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 to 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 (NDVI, EVI, and Ocean Color)

Attenuation in the Visible Wavelengths

Grant Petty, 2004

Attenuation in the Visible Wavelengths

Grant Petty, 2004

Daytime Visibility

Distant Dark ObjectsAppear Brighter

“Clear” Day

Hazy Day

Nighttime Visibility

Distant Bright Objectsare dimmer

Revisiting Daytime Visibility

White Sunlight

Top of Atmosphere

Horizon Color and Intensity

Horizon alwaysWhitened relative toSky above

Distance to the Dark Object

Henceforth, we shall only consider scatteringby clouds and aerosols

Revisiting Daytime Visibility

White Sunlight

Top of Atmosphere

Increased contribution ofwhite light

Object appears lighterwith distance

Longer Distance to the Dark Object

Daytime Visibility Revisitied

Distant Dark ObjectsAppear Brighter

“Clear” Day

Hazy Day

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

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

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)

NDVI

• NDVI is calculated from the visible and near-infrared light reflected by vegetation.

• Healthy vegetation (left) absorbs most of the visible light that hits it, and reflects a large portion of the near-infrared light.

• Unhealthy or sparse vegetation (right) reflects more visible light and less near-infrared light.

• Real vegetation is highly variable.

Advanced Very High Resolution Radiometer (AVHRR)

• NOAA has two polar-orbiting meteorological satellites in orbit at all times, with one satellite crossing the equator in the early morning and early evening and the other crossing the equator in the afternoon and late evening. Morning-satellite data are most commonly used for land studies, while data from both satellites are used for atmosphere and ocean studies.

NOAA 11NOAA 11AVHRRAVHRR

1980 200019901985 201020051995

NOAA 7NOAA 7AVHRRAVHRR

NOAA 9NOAA 9AVHRRAVHRR

NOAA 14NOAA 14AVHRRAVHRR

SeaWiFSSeaWiFS

SPOTSPOT

MODISesMODISesNOAA-16NOAA-16

NPPNPP

NOAA 9NOAA 9 NOAA-17NOAA-17

Satellite Satellite NDVI NDVI data data

sourcessources

NOAA-18NOAA-18

C. Tucker

EVI (Enhanced Vegetation Index)• In December 1999, NASA launched the Terra

spacecraft, the flagship in the agency’s Earth Observing System (EOS) program. Aboard Terra flies a sensor called the Moderate-resolution Imaging Spectroradiometer, or MODIS, that greatly improves scientists’ ability to measure plant growth on a global scale. Briefly, MODIS provides much higher spatial resolution (up to 250-meter resolution), while also matching AVHRR’s almost-daily global cover and exceeding its spectral resolution.

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

Beltsville USA winter wheat biomassBeltsville USA winter wheat biomass

C. Tucker

Winter wheat biomass “harvest”Winter wheat biomass “harvest”

C. Tucker

NDVI vs. total dry biomassNDVI vs. total dry biomass

Explained 80% of biomass

accumulation

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

Average NDVI 1981-2006Average NDVI 1981-2006

NDVI = NDVI = (ir- red)(ir- red)(ir+red)(ir+red)

~40,000 orbits of ~40,000 orbits of satellite datasatellite data

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 AndrewMore info: cstars.ucdavis.edu/classes/ECL290/docs/Andrews.ppt

Global Vegetation Mapping

SeaWiFS Ocean Chlorophyll Land NDVI

5 SeaWiFS land bands

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.

Tasmanian Sea

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]

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)

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

Miller, Bartholomew, Reynolds

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.