Air and Waste Management Association Professional Development Course

AIR-257: Satellite Detection of Aerosols

Concepts and Theory

Instructor:Rudolf Husar, Ph.D. Professor of Mechanical Engineering

Washington University, St. Louis, MOOctober 25, 2004, 9:00 a.m. - 12:00 p.m. Asheville, NC

Syllabus

9:00-9:30 Introduction to satellite aerosol detection and monitoring

9:30-10:00 Satellite Types and their Usage10:00-10:30 Satellite detection of aerosol events: fires, dust storms,

haze

10:30-10:45 Break

10:45-11:00 Satellite data and tools for the RPO FASTNET project11:15-11:30 Satellite Data Use in AQ Management: Issues and Opportunities11:30-12:00 Class-defined problems, feedback, discussion, exam(?)

Radiation detected by satellites

• Air scattering depends on geometry and can be calculated (Rayleigh scattering)

• Clouds completely obscure the surface and have to be masked out

• Aerosols redirect incoming radiation by scattering and also absorb a fraction

• Surface reflectance is a property of the surface

Just like the human eye, satellite sensors detect the total amount of solar radiation that is reflected from the earth’s surface (Ro) and backscattered by the atmosphere from aerosol, pure air, and clouds. A simplified expression for the relative radiatioin detected by a satellite sensor (I/Io) is:

I / Io = Ro e- + (1- e-) P

Satellite Detection of Aerosols

Today, geo-synchronous and polar orbiting satellites can detect different aspects of aerosols over the globe daily.

where is the aerosol optical thickness and P the angular light scattering probability.

Height Type Size Angle Shape

dHdCdDdPdSSPDCHI

SeaWiFS Satellite Platform and Sensors

• Satellite maps the world daily in 24 polar swaths

• The 8 sensors are in the transmission windows in the visible & near IR

• Designed for ocean color but also suitable for land color detection, particularly of vegetation

Swath

2300 KM

24/day

Polar Orbit: ~ 1000 km, 100 min.

Equator Crossing: Local NoonChlorophyll Absorption

Designed for Vegetation Detection

Key aerosol microphysical parameters

Particle size and size distributionAerosol particles > 1 m in size are produced by windblown dust and sea salt from sea spray and bursting bubbles. Aerosols smaller than 1 m are mostly formed by condensation processes such as conversion of sulfur dioxide (SO2) gas to sulfate particles and by formation of soot and smoke during burning processes

Effective radiusMoment of size distribution weighted by particle area and number density distribution

Complex refractive indexThe real part mainly affects scattering and the imaginary part mainly affects absorption

Particle shapeAerosol particles can be liquid or solid, and therefore spherical or nonspherical.The most common nonspherical particles are dust and cirrus

Key aerosol optical parameters

Optical depthnegative logarithm of the direct-beam transmittancecolumn integrated measure of the amount of extinction (absorption + scattering)

Single-scattering albedo 0

given an interaction between a photon and a particle, the probability that the photon is scattered in some direction, rather than absorbed

Scattering phase functionprobability per unit solid angle that a photon is scattered into a particular direction relative to the direction of the incident beam

Angstrom exponent exponent of power law representation of extinction vs. wavelength

Remote Sensing Overview

• What is “remote sensing”?– Using artificial devices, rather than our eyes, to observe or measure

things from a distance without disturbing the intervening medium• It enables us to observe & measure things on spatial, spectral, & temporal

scales that otherwise would not be possible• It allows us to observe our environment using a consistent set of

measurements throughout the globe, without prejudice associated with national boundaries and accuracy of datasets or timeliness of reporting

• How is remote sensing done?– Electromagnetic spectrum

• Passive sensors from the ultraviolet to the microwave• Active sensors such as radars and lidars

– Satellite, airborne, and surface sensors– Training and validation sites

Remote Sensing Applications to be Covered in this Course

Remote Sensing Applications to be Covered in this Course

• History of remote sensing & global change • Remote sensing of land surface properties

– Spectral and angular reflectance, land cover & land cover change– Fire monitoring and burn scars– Leaf area index & flux of photosynthetically active radiation– Temperature & emissivity separation of terrestrial surfaces

• Remote sensing of atmospheric properties– Cloud cover, cloud optical properties, and cloud top properties– Aerosol properties– Water vapor– Atmospheric chemistry (carbon monoxide and methane)– Earth radiation budget and cloud radiative forcing

• Remote sensing of the oceans from space– Chlorophyll concentration and biological productivity of the oceans – Sea surface temperature using thermal methods

• Angular directional models of the Earth-atmosphere-ocean system

• Remote sensing uses the radiant energy that is reflected and emitted from Earth at various “wavelengths” of the electromagnetic spectrum

• Our eyes are only sensitive to the “visible light” portion of the EM spectrum

• Why do we use nonvisible wavelengths?

The Electromagnetic Spectrum

Michael D. King, EOS Senior Project Scientist

Atmospheric Absorption in the Wavelength Range from 0-15 µm

Michael D. King, EOS Senior Project Scientist

Generalized Spectral Reflectance Envelopes for Deciduous and Coniferous Trees

Michael D. King, EOS Senior Project Scientist

Typical Spectral Reflectance Curves for Vegetation, Soil, and Water

Michael D. King, EOS Senior Project Scientist

Different Types of Reflectors

Specular reflector (mirror) diffuse reflector (lambertian)

nearly diffuse reflectorNearly Specular reflector (water)

Hot spot reflection

E. Vermote, 2002

Sun glint as seen by MODIS Hot hot-spot over dense vegetationHot hot-spot over dense vegetation

E. Vermote, 2002

Scattering of Sunlight by the Earth-Atmosphere-Surface System

A = radiation transmitted through the atmosphere and reflected by the surface

B = radiation scattered by the atmosphere and reflected by the surface

C = radiation scattered by the atmosphere and into the ‘radiometer’

G = radiation transmitted through the atmosphere, reflected by background objects, and subsequently reflected by the surface towards the ‘radiometer’

I = ‘adjacency effect’ of reflectance from a surface outside the field of view of the sensor into its field of view

Michael D. King, EOS Senior Project Scientist

Solar Energy Paths

Aerosol and Surface Radiative Transfer

oI

ooIRAerosol

eRI oo

ePoI 1

Surface at the

ReachingRadiation Reflection Surface

Attenuated

oPI

Reflection Aerosol Attenuated

ePIeRIRII oooo 1

Major Assumptions

• Gaseous scattering and absorption is subtractable from the sensed radiation – multiple scattering is negligible.

• All the remaining solar radiation reaches the surface directly or diffusely –small backscattering fraction.

• Backscattering to space is due to incoming solar radiation – low surface reflectance.

I0 – Intensity of the incoming radiation.

R0- surface reflectance. Depends on surface type as well as the incoming and outgoing angles R- surface reflectance sensed at the top of the atmosphere as perturbed by the atmosphereP - aerosol angular reflectance function; includes absorption, P = ω p

Apparent Surface Reflectance, R• The surface reflectance R0 is obscured by aerosol scattering and absorption before it reaches the sensor

• Aerosol acts as a filter of surface reflectance and as a reflector solar radiation

Aerosol as Reflector: Ra = (e-– 1) P

R = (R0 + (e-– 1) P) e-

Aerosol as Filter: Ta = e-

Surface reflectance R0

• The apparent reflectance , R, detected by the sensor is: R = (R0 + Ra) Ta

• Under cloud-free conditions, the sensor receives the reflected radiation from surface and aerosols

• Both surface and aerosol signal varies independently in time and space

• Challenge: Separate the total received radiation into surface and aerosol components

Apparent Surface Reflectance, R

Aerosols will increase the apparent surface reflectance, R, if P/R0 < 1. For this reason, the reflectance of ocean and dark vegetation increases with τ.

When P/R0 > 1, aerosols will decrease the surface reflectance. Accordingly, the brightness of clouds is reduced by overlying aerosols.

At P~ R0 the reflectance is unchanged by haze aerosols (e.g. soil and vegetation at 0.8 um)..

At large τ (radiation equilibrium), both dark and bright surfaces asymptotically approach the ‘aerosol reflectance’, P

The critical parameter whether aerosols will increase or decrease the apparent reflectance, R, is the ratio of aerosol angular reflectance, P, to bi-directional surface reflectance, R0, P/ R0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.5 1 1.5 2 2.5 3 3.5 4

Aerosol Optical Thickness, AOT

Ap

par

ent R

efle

ctan

ce,R

ePeRR ))1(( 0

Clouds at all wavelengths, P/Ro<o.5

Ocean at >0.6 umVegetation at 0.4 um, P/Ro>10

Soil at >0.6 umVegetation at >0.6 um, P/Ro=1

Vegetation at 0.5 um, P/Ro=2-5

Best fit P values:Haze: P = 0.38Dust: P = 0.28

ReflectanceReflectanc

e

Reduced Reflectance

Increased Reflectance

Loss of Contrast

0.01

0.1

1

0 1 2 3 4

Aerosol Optical T ickness, AOT

Co

ntr

ast

Rat

io, C

/Co

P/R=5 P/R=2 P/R=1 P/R=0.5P/R=0.2 P/R=10 P/R=0.1

P/R0=10

P/R0=0.1 P/R0=1

Detectable (Visual) Range Limit, C/C0 = 0.02-0.05

1

210

Contrast Surface

R

RRC

1

21

ContrastSensor

I

IIC

1

0 )1(1

1

:Aerosol todue LossContrast

RP

eC

C

The aerosol τ can also be estimated from the loss of surface contrast.

Whether contrast decays fast or slow with increasing τ depends on the ratio of aerosol to surface reflectance, P/ R0

Note: For horizontal vision against the horizon sky, P/R0 = 1, contrast decays exponentially with τ, C/C0=e-τ.

Obtaining Aerosol Optical Thickness from Excess Reflectance

The perturbed surface reflectance, R, can be used to derive the the aerosol optical thickness, τ , provided that the true surface reflectance R0 and the aerosol reflectance function, P are known. The excess

reflectance due to aerosol is : R- R0 = (P- R0)(1-e- τ) and the optical depth is:

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.05 0.1 0.15 0.2 0.25 0.3

Exce ss Re fle ctance (R-R0)

Aero

sol O

ptic

al T

hick

ness

, AO

T

)ln(0 PR

PR

0at 0 RP

R

00 R

3.00 R2.00 R

1.00 R

05.00 R

P=0.38For a black surface, R0 =0 and optically thin aerosol,

τ < 0.1, τ is proportional to excess radiance, τ =R/P. For τ > 0.1, the full logarithmic expression is needed.

As R0 increases, the same excess reflectance

corresponds to increasing values of τ.

When R0 ~P the aerosol τ can not be retrieved since

the excess reflectance is zero. For R0 > P, the surface

reflectance actually decreases with τ, so τ could be retrieved from the loss of reflectance, e.g. over bright clouds.

The value of P is derived from fitting the observed and retrieved surface reflectance spectra. For summer light haze at 0.412 μm, P=0.38.

Accurate and automatic retrieval of the relevant aerosol P is the most difficult part of the co-retrieval process. Iteratively calculating P from the estimated τ( λ) is one possibility.

)ln(0 PR

PR

Aerosol Effects on Surface Colorand

Surface Effects on Aerosol Color

The image was synthesized from the blue (0.412 μm), green (0.555 μm), and red (0.67 μm) channels of the 8 channel SeaWiFS sensor. Air scattering is removed to highlight the haze and surface reflectance.

Aerosol Effect on Surface Color and Surface Effect on Aerosol

• Aerosols add to the reflectance and sometimes reduce the reflectance of surface objects

• Aerosols always diminish the contrast between dark a bright surface objects• Haze and smoke aerosols change the color of surface objects to bluish while dust adds a yellowish

tint. (Click on the Images to View)

• Dark surfaces like ocean and dark vegetation makes the aerosol appear bright.• Bright surfaces like sand and clouds makes the aerosol invisible.

SeaWiFS Images and Spectra at Four Wavelengths (Click on the Images to View)

At blue (0.412) wavelength, the haze reflectance dominates over land surface reflectance. The surface features are obscured by haze. Air scattering (not included) would add further reflectance in the blue. The blue wavelength is well suited for aerosol detection over land but surface detection is difficult.

0

50

100

150

200

250

300

0.3 0.5 0.7 0.9

Wave le ngth,um

Ref

lect

ance

,x10

00

Haze

Surface

Surface + Haze

Vegetation

0

50

100

150

200

250

300

0.3 0.5 0.7 0.9

Wave le ngth,um

Ref

lect

ance

,x10

00

Surface + Haze

Haze

Surface

Ocean

At green (0.555) over land, the haze is reduced and the vegetation reflectance is increased. The surface features are obscured by haze but discernable. Due to the low reflectance of the sea, haze reflectance dominates. The green not well suited for haze detection over land but appropriate for haze detection over the ocean and for the detection of surface features. 0

50

100

150

200

250

300

0.3 0.5 0.7 0.9

Wave le ngth,um

Ref

lect

ance

,x10

00

Haze

Surface

Surface + Haze

Vegetation

0

50

100

150

200

250

300

0.3 0.5 0.7 0.9

Wave length,um

Ref

lect

ance

,x10

00 Surface + Haze

Haze

Surface

Ocean

At red (0.67) wavelength over land, dark vegetation is distinctly different from brighter yellow-gray soil. The surface features, particularly water (R0<0.01), vegetation (R0<0.04), and soil (R0<0.30) are are easily distinguishable. Haze reflectance dominates over the ocean. Hence, the red is suitable for haze detection over dark vegetation and the ocean as well as for surface detection over land.

0

50

100

150

200

250

300

0.3 0.5 0.7 0.9

Wave length,um

Ref

lect

ance

,x10

00

Haze

Surface

Surface + Haze

Vegetation

0

50

100

150

200

250

300

0.3 0.5 0.7 0.9

Wave le ngth,um

Ref

lect

ance

,x10

00

Surface + Haze

Haze

Surface

Ocean

In the near IR (0.865) over land, the surface reflectance is uniformly high (R0>0.30) over both vegetation and soil and haze is not discernable. Water is completely dark (R0<0.01) making land and water clearly distinguishable. The excess haze reflectance over land is barely perceptible but measurable over water. Hence, the near IR is suitable for haze detection over water and land-water differentiation.

0

50

100

150

200

250

300

0.3 0.5 0.7 0.9

Wave le ngth,um

Ref

lect

ance

,x10

00

Haze

Surface

Surface + Haze

Vegetation

0

50

100

150

200

250

300

0.3 0.5 0.7 0.9

Wave le ngth,um

Ref

lect

ance

,x10

00

Surface + Haze

Haze

Surface

Ocean

The image was synthesized from the blue (0.412 μm), green (0.555 μm), and red (0.67 μm) channels of the 8 channel SeaWiFS sensor. Air scattering has been removed to highlight the haze and surface reflectance.

Aerosol effects on surface colorand

Surface effects on aerosol color

Preprocessing

Transform raw SeaWiFS data • Georeferencing – warping data to geographic

lat/lon coordinates with a pixel resolution of ~ 1.6 km

• Splicing – mosaic data from adjacent swaths to cover entire domain

• Rayleigh correction – remove scattering by atmospheric gases and convert to reflectance units

• Scattering angle correction – normalize all pixels to remove reflectance dependence on sun-target-sensor angles

Result is daily apparent reflectance, R for all 8 channels

General Approach: Co-Retrieval of Surface and Aerosol Reflectance

1. Surface Reflectance Retrieval by Time Series Analysis – (Sean Raffuse, MS Thesis 2003)

2. Aerosol Retrieval over Land – Radiative transfer model + Surface data

3. Refined Surface Reflectance

– Iteration back to 1., 2. …

Approach – Time Series Analysis

• For any location (pixel), the sensor detects a “clean” day periodically– Aerosol scattering (haze) is near zero– Pixel must also be free of other interferences

• Clouds• Cloud shadows

Methodology – Cloud shadows

• Clouds are easily detected by their high reflectance values• Cloud shadows are found in the vicinity of clouds• We enlarge the cloud mask by a three-pixel ‘halo’ to remove cloud shadows• Cloud shadows reduce the apparent surface reflectance considerably in all channels

Methodology – Preliminary anchor days

• Surface reflectance is retrieved for individual pixels from time series data (e.g. year) • The procedure first identifies a set of ‘preliminary clear anchor’ days in a 17-day moving

window– The main interferences (clouds and haze) tend to increase the apparent surface reflectance, especially

in the low wavelength channels– The anchor day is chosen as the day with the minimum sum of the lowest four channels

April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290

Results – Seasonal surface reflectance, Eastern US

Results – Eight month animation

Retrieval Procedures

• Rayleigh air scattering and gaseous absorption is removed first by the E. Vermote algorithm.

• Cloudy pixels are masked out since they obscure the surface and aerosol reflectance

• The remaining reflectance over land and water consists of the combined effect of aerosol scattering/absorption and surface reflectance.

• The goal of the co-retrieval is to separate the reflectance due to aerosol from surface reflectance