2004-06-23 Retrieval of smoke aerosol loading from remote sensing data

Retrieval of smoke aerosol loading from remote sensing data Sean Raffuse and Rudolf Husar Center for Air Pollution Impact and Trends Analysis Washington University

Overview
Problem statement and goal
Method
Radiative transfer theory
Aerosol map generation
Summary
Continuing work

Problem statement and goal
Biomass burning contributes a significant fraction of the anthropogenic aerosol
Wildfires and prescribed burns
Slash-and-burn agriculture
Crop waste burning
The amount of aerosol generated by biomass burning is not well quantified
No satisfactory tracer for biomass smoke has been found
Ground and aircraft-based studies do not provide adequate spatial coverage
Aerosols from smoke contribute to global cooling
Quantification is needed to model global climate change
Problem Goal To quantify the emission of smoke from biomass burning as well as study its spatial and temporal pattern

Method: remote sensing of aerosol optical properties
Remote sensors deployed in research satellites detect radiation from the earth and its atmosphere
These sensors allow us to detect aerosols that scatter and absorb light
We utilize the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument on NASA’s SeaStar spacecraft
Polar-orbiting
1 km resolution
Daily coverage
8 channels (6 visible, 2 near-IR)

Radiative transfer theory for aerosol-surface co-retrieval The sensed radiation is decomposed into scattering and absorption by (1) gases, (2) aerosols as well as reflection from the (3) surfaces and (4) clouds. Air scattering and surface/aerosol reflectance are assumed to be additive, disregarding multiple scattering effects.

Apparent surface reflectance, R Aer. Transmittance Both R 0 and R a are attenuated by aerosol extinction T a which act as a filter Aerosol Reflectance Aerosol scattering acts as reflectance, R a adding ‘ airlight ’ to the surface reflectance Surface Reflectance The surface reflectance R 0 is an inherent characteristic of the surface R = ( R 0 + ( e -  – 1 ) P ) e -  
The surface reflectance R 0 objects viewed from space is modified by aerosol scattering and absorption.
The apparent reflectance, R, is: R = ( R 0 + R a ) T a
Aerosol as Reflector: R a = ( e -  – 1 ) P Aerosol as Filter: T a = e -  Apparent Reflectance R may be smaller or larger then R 0 , depending on aerosol reflectance and filtering.

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 R 0 and the aerosol reflectance function, P are known. The excess reflectance due to aerosol is : R- R 0 = (P- R 0 )(1-e - τ ) and the optical depth is: As R 0 increases, the same excess reflectance corresponds to increasing values of τ. Accurate and automatic retrieval of the relevant aerosol P is a difficult part of the co-retrieval process. Iteratively calculating P from the estimated τ( λ) is one possibility.  can be related to mass loading by assuming physical and optical properties.

Aerosol effects on surface color and 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 has been removed to highlight the haze and surface reflectance.

Process for co-retrieval
Generate daily total reflectance image with air reflectance removed, R
Generate surface reflectance image, R 0
Subtract daily total reflectance image from surface reflectance image to get aerosol optical thickness, 
Filter  , removing clouds and other interferences
R 0 R 

1. Daily reflectance image
2000-08-23 RGB image after preprocessing
Preprocessing includes
Conversion from L1a “engineering” values to L1b “scientific” values (counts  radiance)
Georeferencing
Splicing
Rayleigh correction

2. Generating the surface reflectance, part 1
The surface image is the “clean” surface image with all clouds, air, and aerosol removed
Daily surface reflectances are generated by creating a composite image from the nearest 15 days
At each pixel, the cleanest daily value is used
As aerosol and clouds both make the reflectance brighter, the cleanest value is the one with the lowest reflectance
Cloud shadows and other anomalous low values are not used

2. Generating the surface reflectance, part 2
In 15 days, some locations are not cloud and aerosol free
This results in leftover haze, and areas of continual cloud cover
We use a small (15-day) time span to preserve temporal surface change, such as in the fall
However, the blue channel remains fairly constant over a longer time period
Leftover aerosol signal is subtracted from a 60-day blue minimum
Other channels are subtracted assuming a wavelength dependence of 
Uncleaned Surface Reflectance Cleaned Surface Reflectance

3-4. Generating aerosol optical thickness ( 
Aerosol optical depth (  is then calculated from the daily total reflectance (R) and surface reflectance (R 0 )
Clouds are removed using several filters based on the spectral characteristics of 
This image shows the blue channel (412 nm) aerosol optical depth

Total reflectance and optical depth comparison Smoke plume Haze Filtered clouds

Summary
Biomass smoke is difficult to quantify
No satisfactory tracers have been discovered
Ground-based and aircraft studies do not provide good spatial coverage
Aerosol optical thickness can be retrieved from remote sensing imagery
With knowledge of particle physical and optical properties, an estimation of mass loading can be made
Size distribution, morphology, mixing regime
Extincion coefficient, single-scatter albedo, phase function

Continuing work
Estimation of smoke fluxes
Identify specific smoke plumes
Divide map into location grids
Use wind vector data to calculate flux through the grids
These values are required for climatological models
Data fusion
Data from remote sensing and ground-based networks are complimentary
Multiple data sets will be fused to improve understanding

Thank You!
R. Husar, F. Li, E. Vermote
M. King, Y. Kaufman, D. Tanre, J. Martins, P. Hobbs . . .
U.S. EPA

2004-06-23 Retrieval of smoke aerosol loading from remote sensing data

by Rudolf Husar

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2004-06-23 Retrieval of smoke aerosol loading from remote sensing data Presentation Transcript