An Introduction to Using Spectral Information in Aerosol Remote

Sensing

Richard KleidmanSSAI/NASA Goddard

Lorraine RemerUMBC / JCET

Robert C. LevyNASA GSFC

Revised November 2014

An Introduction to Using Spectral Information in Aerosol Remote Sensing

Richard KleidmanSSAI/NASA Goddard

Lorraine RemerUMBC / JCET

Robert C. LevyNASA GSFC

This presentation is intended to introduce somebasic concepts on how spectral information is usedin remote sensing (of aerosols) and how this information is used to construct remote sensing products.

We begin by looking at some general problems presented by looking down through

a dirty atmosphere.

In an ideal situation with no atmosphere all of the incomingradiation would reach the surface. A portion of the photonswould be absorbed at the surface. The remaining photonsreflect back up into space.

Diagrams from E. Vermote et. al, 6S manual

Measured radiance directly depends on surface properties

No Atmosphere

What does the satellite see?What information do the photons contain?

1) Backscattered photons which never reach the surface.

Signal or Noise?

… and for whom?

Diagram from E. Vermote et. al, 6S manual

With Atmosphere

2) Scattered photons which illuminate the ground.

Signal or Noise?

3) Photons reflected by the surface and then scattered by the atmosphere.

Diffuse solar radiation.

Multiple scattering events.

This is usually ignored after one or two interactions.

Diagram from E. Vermote et. al, 6S manual

From E. Vermote et. al, 6S manual

The real atmosphere complicates the signal. Only a fractionof the photons reach the sensor so that the target seems less reflecting.

Real Atmosphere Photons lost due to 1) Atmospheric absorption2) Scattering

Geometric issues of the illumination andthe measurement

Very important for surface and atmospheric signal

Solar zenith angle Sensor zenith angle

Solar view angle

Sensor view angle

From Spectra to ProductSome steps along the way from satellite observations to useful geophysical content.

Aviris Spectra MODIS Band 4

MODISCloud

Fraction

MODISAerosolOpticalDepth

One of the advantages of MODIS is its broad spectral range.The wider the spectral range the more information contentwe have when we observe the Earth - Atmosphere system.

Let’s begin simply by examining some sample spectra of different surfaces.

Aspen Leaves - very uniform

AspenGreen Leaf

AspenYellow Leaf

The signal reaching any space borne sensor is a complexmixture of surface and atmospheric components.

From Spectra to Product

A catalog of spectral responses of known surfaces can helpus establish the identity of an unknown or mixed scene we areobserving much like a fingerprint can help us establish theidentity of the person who made the print.

Product – A set of values that is used to describe, in a consistentway, physical properties of an observed geophysical phenomena.

Products can consist of:ImagesQualitative evaluations – Cloudy or clearQuantitative measurements – Amount of aerosol, percent cloud

There are many complications but let’s examine a little bitabout how we get from raw spectra to useful product.

?

Gaseous Absorption

Atmospheric gases - CO2, O2, and H2Oabsorb the solar radiation at specificlocations in the EM spectra causing the gapswe see at the left.

In most cases the absorption bandslimit our ability to obtain useful information

There are some cases where we canexploit the absorption bands to obtainadditional information.

Slant-Path Absorption of the Atmosphere & Location of Primary Atmospheric

Windows

Wavelength (µm)0.50

0.55

0.60

0.65

0.75 0.80

0.85

0.01

0.00

0.02

0.03

0.04

0.05

Ab

sorp

tion

0.70

O2 B-Band

O2 A-Band

Courtesy of Michael King

Spectral optical properties of aerosol

Dust

Smoke

from Y. Kaufman

Both dust andsmoke interactwith the shorterwavelengthsreflecting lightback to the sensor.

The larger dust particles interact with thelonger infraredwavelengthsbut not the smaller smoke particles which remain invisible.

This distinctionis madepossible by thewide spectralrange of theMODIS sensor.

Spectral optical properties of aerosol

Dust / Sea Salt

Smoke / Pollution

wavelength in µm

Here youcan see thespectralresponseof the largeand small particles.

Note that thelarge particlesproduce a highresponse acrossthe spectral range.

The small particlesproduce weakerresponses asthe wavelength ofthe light increases

Physical PropertiesAngstrom Exponent -α

The Angstrom exponent is often used as a qualitative indicator of mean aerosol particle size in the atmospheric column.Values greater than 2 – small particles Values less than 1 – large particles

For measurements of optical thickness

1 and 2 taken at two different wavelengths 1 and 2

α = -

1

2 ln

1

2 ln

Physical PropertiesThe angstrom exponent really represents the (negative of the) slope of the spectral response.

wavelength in µm

The response oflarge particles ischaracterized by littleto no slope.

The response ofsmall particles ischaracterized by moderate to large slopes.

20 k

m

12 km

R = 0.66 µmG = 0.55 µmB = 0.47 µm

R = 1.6 µmG = 1.2 µmB = 2.1 µm

Ag (2.1 µm) < 0.10

0.10 < Ag (2.1 µm) < 0.150 = 36°

Biomass burningCuiabá, Brazil (August 25, 1995)

Extracting Information by Specific Band Usage

AVIRIS

Example MODIS Data GranuleCanadian Fires, MODIS Terra, 7 July 2002

true color SWIR composite

sea ice

smokeice cloud

Using multiple bands to separate cloud, land and ocean surfaces. This is an Aqua

MODIS image of a storm system offthe coast of SouthAmerica

We are going to use a combinationof three different bandsto quantitatively draw adistinction betweenclouds, land and ocean.

We begin by making an ocean vs land mask

MODIS band 20.86 um

Sensitive to vegetation Clouds are brightWater is dark

MODIS band 10.66 um

Land is darkClouds are brightWater is dark

Band 2 / Band 1

Accentuates the differencesbetween land and ocean

Is not sensitive to clouds-clouds are spectrally bright through all of thereflectance bands. When we divide band 2 by band 1 almost all values for cloud become very close to 1.0

Separating Cloud from Non-Cloud

MODIS Band 31- 11um

This is a thermal bands soit is sensitive to temperature.

Clouds are cold relative to land and ocean surfaces.

Typically higher response valuesmap to higher intensity displayvalues. In this case we use an inverted display scale so that thelower response values of thecold clouds will appear brightin the image.

Putting it all together

This is a scatter plotof band 31 (temperatureinformation) on the x-axis

Vs

Band 2 / Band 1 (our land/seamask on the y-axis

We have managedto separate the twofeatures into somewhat distinct groups.

Band 31(11μm) – brightness temperature

Ban

d 2

(.8

6 μ

m )

/ B

and

1 (

0.66

μm

) –

La

nd /

Sea

mas

k

Putting it all together

Using the hydra toolwe can select some of the distinctive clusters of points in thescatter plot and color their corresponding locationsin the image.

The image at bottomleft now shows our crudemask of Land, Oceanand Cloud.

Observe the following true color images.

See how many surface issues, satellite issues or combinationsof the two you can find from the following three images.

09 April 2004 12 June 2004 19 December 2004

What are some complications as we add more aerosol?