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

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

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

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)

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.

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

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 

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

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

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

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

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 

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

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

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

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

R. Husar, F. Li, E. Vermote

M. King, Y. Kaufman, D. Tanre, J. Martins, P. Hobbs . . .

U.S. EPA