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

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Presentation on theme: "Retrieval of smoke aerosol loading from remote sensing data Sean Raffuse and Rudolf Husar Center for Air Pollution Impact and Trends Analysis Washington."— Presentation transcript:

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

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

3 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

4 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)

5 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. Radiative transfer theory for aerosol-surface co-retrieval

6 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. Apparent surface reflectance, R

7 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. Obtaining aerosol optical thickness from excess reflectance

8 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 color and Surface effects on aerosol color

9 Process for co-retrieval 1.Generate daily total reflectance image with air reflectance removed, R 2.Generate surface reflectance image, R 0 3.Subtract daily total reflectance image from surface reflectance image to get aerosol optical thickness,  4.Filter , removing clouds and other interferences R0R0 R 

10 1. Daily reflectance image 2000-08-23 RGB image after preprocessing Preprocessing includes 1.Conversion from L1a “engineering” values to L1b “scientific” values (counts  radiance) 2.Georeferencing 3.Splicing 4.Rayleigh correction

11 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

12 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

13 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

14 Total reflectance and optical depth comparison Smoke plume Haze Filtered clouds

15 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

16 Continuing work Estimation of smoke fluxes 1.Identify specific smoke plumes 2.Divide map into location grids 3.Use wind vector data to calculate flux through the grids 4.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

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

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