2005-02-01 Co-Retrieval of Aerosol Color and Surface Color from SeaWiFS Satellite Images


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2005-02-01 Co-Retrieval of Aerosol Color and Surface Color from SeaWiFS Satellite Images

  1. 1. Co-Retrieval of Aerosol Color and Surface Color from SeaWiFS Satellite Images Rudolf B. Husar CAPITA, Washington University, St. Louis, MO, October 1999 rhusar@me.wustl.edu
  2. 2. Contents: <ul><li>Goals, Data, Tools and Methods </li></ul><ul><li>Radiative Transfer Theory of Co-Retrieval </li></ul><ul><li>Illustration of Aerosol Effects on Surface Color </li></ul><ul><li>Surface Effects on Aerosol Color </li></ul><ul><li>Co-Retrieval Procedures </li></ul><ul><li>Aerosol and Surface Reflectance Retrieval for the Northeastern US </li></ul><ul><li>Summary </li></ul><ul><li>Conclusions </li></ul>
  3. 3. Goals, Data, Methods and Tools <ul><li>The goal of the work is to simultaneously obtain (co-retrieve) the columnar aerosol optical properties as well as the aerosol-free surface reflectance. The focus of this work-phase is to retrieve the spectral aerosol optical thickness, τ and spectral surface reflectance. </li></ul><ul><li>The work is performed using the 8 wavelength (0.4-0.9 μm) SeaWiFS satellite data. </li></ul><ul><li>The surface-aerosol co-retrieval method is based largely on an unpublished procedure developed for LANDSAT data in the 70s; the Rayleigh correction algorithm is by Eric Vermote . </li></ul><ul><li>The SeaWifs data pre processing programs were written by Fang Li and R.B. Husar at CAPITA and the procedures . </li></ul><ul><li>The SeaWifs data pre-processing was performed by Fang Li of CAPITA using the commercial software ENVI by RSI with built-in IDL language support. </li></ul><ul><li>This work builds on the rich literature on aerosol retrieval/surface detection work of Kaufmann, Tanre, King, Vermote…as well as on the remarkably successful ocean color retrieval work of Gordon. At some later time, proper references will be made to their extensive work. </li></ul>
  4. 4. Co-Retrieval of Aerosol and Surface Reflectance <ul><li>Remote sensing provides the combined surface and aerosol reflectance but not the separate contributions. The challenge of co-retrieval is to separate the aerosol and surface reflectances. </li></ul><ul><li>The aerosol optical parameters can be estimated from the distorted surface reflectance. The condition is that one has a high quality and stable haze-free surface image with substantial texture containing bright and dark surfaces. Conversely, the derived aerosol parameters allow the reconstruction of haze-free surface reflectance . </li></ul><ul><li>The retrievals are interdependent and through iteration provide a mutual quality control: </li></ul><ul><ul><li>Successful aerosol retrieval depends on high quality surface reflectance data. </li></ul></ul><ul><ul><li>The surface reflectance can only be retrieved for known aerosol-optical properties. </li></ul></ul><ul><li>In this work, the retrieved aerosol parameters are the columnar aerosol optical thickness at several wavelengths (e.g. 0.416 and 0.67) um and the spectral aerosol reflectance function . Both the aerosol parameters and the surface reflectance are retrieved over all cloud-free areas at all wavelengths. However, the quality of the retrievals is low in the near IR. </li></ul>
  5. 5. 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.
  6. 6. Retrieval Procedures <ul><li>Rayleigh air scattering and gaseous absorption is removed first by the E. Vermote algorithm. </li></ul><ul><li>Cloudy pixels are masked out since they obscure the surface and aerosol reflectance </li></ul><ul><li>The remaining reflectance over land and water consists of the combined effect of aerosol scattering/absorption and surface reflectance. </li></ul><ul><li>The goal of the co-retrieval is to separate the reflectance due to aerosol from surface reflectance </li></ul>
  7. 7. Aerosol and Surface Radiative Transfer <ul><li>Major Assumptions </li></ul><ul><li>Gaseous scattering and absorption is subtractable from the sensed radiation – multiple scattering is negligible. </li></ul><ul><li>All the remaining solar radiation reaches the surface directly or diffusely –small backscattering fraction. </li></ul><ul><li>Backscattering to space is due to incoming solar radiation – low surface reflectance. </li></ul>I 0 – Intensity of the incoming radiation. R 0 - 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 atmosphere P - aerosol angular reflectance function; includes absorption, P = ω p
  8. 8. 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 -   <ul><li>The surface reflectance R 0 objects viewed from space is modified by aerosol scattering and absorption. </li></ul><ul><li>The apparent reflectance, R, is: R = ( R 0 + R a ) T a </li></ul>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.
  9. 9. Apparent Surface Reflectance, R Aerosols will increase the apparent surface reflectance, R, if P/R 0 < 1. For this reason, the reflectance of ocean and dark vegetation increases with τ. When P/R 0 > 1, aerosols will decrease the surface reflectance. Accordingly, the brightness of clouds is reduced by overlying aerosols. At P~ R 0 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, R 0 , P/ R 0
  10. 10. Loss of Contrast 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/ R 0 Note: For horizontal vision against the horizon sky, P/R 0 = 1, contrast decays exponentially with τ, C/C 0 =e -τ .
  11. 11. 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: For a black surface, R 0 =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 R 0 increases, the same excess reflectance corresponds to increasing values of τ. When R 0 ~P the aerosol τ can not be retrieved since the excess reflectance is zero. For R 0 > 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.
  12. 12. Aerosol Effects on Surface Color and Surface Effects on Aerosol Color <ul><li>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. </li></ul>
  13. 13. Aerosol Effect on Surface Color and Surface Effect on Aerosol <ul><li>Aerosols add to the reflectance and sometimes reduce the reflectance of surface objects </li></ul><ul><li>Aerosols always diminish the contrast between dark a bright surface objects </li></ul><ul><li>Haze and smoke aerosols change the color of surface objects to bluish while dust adds a yellowish tint. (Click on the Images to View) </li></ul><ul><li>Dark surfaces like ocean and dark vegetation makes the aerosol appear bright . </li></ul><ul><li>Bright surfaces like sand and clouds makes the aerosol invisible . </li></ul>
  14. 14. 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. 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. At red (0.67) wavelength over land, dark vegetation is distinctly different from brighter yellow-gray soil. The surface features, particularly water (R 0 <0.01), vegetation (R 0 <0.04), and soil (R 0 <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. In the near IR (0.865) over land, the surface reflectance is uniformly high (R 0 >0.30) over both vegetation and soil and haze is not discernable . Water is completely dark (R 0 <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.
  15. 15. Haze Effect on Spectral Reflectance over Land The spectral reflectance of vegetation in the visible λ is low at 0.01<R 0 <0.1. Haze significantly enhances the reflectance in the blue but the haze excess in the near IR is small. This is consistent with radiative transfer theory of haze impact.
  16. 16. Comparison of Haze Effects on Land and Ocean In the blue (λ=0.412) and red (λ=0.67) both the land and the ocean have low surface reflectance and the excess reflectance is the same. However at green and near IR the excess reflectance over land is lower then over the ocean as expected from radiative transfer theory.
  17. 17. The Aerosol Retrieval – No Model <ul><li>The spectral aerosol optical thickness, τ (λ ) is retrieved for all pixels , except over clouds. </li></ul><ul><li>The aerosol retrieval does not assume a particular form of aerosol optical parameters, i.e. there is no a priori ‘aerosol model’ . Rather, the spectral τ is retrieved for each 8 wavelength from the excess reflectance and calibrated P. </li></ul><ul><li>At this time the aerosol angular reflectance function  λ P λ is calibrated using best fit to bright and dark surfaces. In the future, P will also be estimated iteratively based on the shape of the spectral extinction curve. </li></ul><ul><li>The current aerosol retrieval method has many limitations: </li></ul><ul><ul><li>It is only possible if the aerosol-free reflectance is available </li></ul></ul><ul><ul><li>Estimation of P is tedious; the derivation of P λ from τ λ is not completed </li></ul></ul><ul><ul><li>The estimation τ λ at 0.7 and 0.8 μm over land is uncertain due to low excess reflectance </li></ul></ul><ul><ul><li>Tests were only done for the hazy Northeastern US; dusty regions will differ </li></ul></ul><ul><ul><li>Plus many, many other limitations </li></ul></ul>
  18. 18. The Aerosol Retrieval: τ( 0.412), τ( 0.67), b <ul><li>The retrieved aerosol parameters are τ(λ) and P(λ). The τ is a measure of the columnar aerosol concentration and it has a strong spatial variation. The aerosol reflectance function P is a measure of characteristic particle size and particle absorption. P is presumed to vary less spatially. (Actually, the data indicate that in some conditions (e.g. transition between ‘haze’ and ‘mist’ there are strong spatial gradients in P as well as absorption). </li></ul><ul><li>The most reliable wavelengths for aerosol retrieval over land are blue (0.412) and red (0.67) since both are dark compared to the brighter green. The Angstrom exponent, b , τ~ λ -b , is derived from the estimated τ at the two ‘dark surface’ wavelengths (0.412 and 0.67): </li></ul><ul><li>In the following illustrations of aerosol retrieval, the spatial pattern of τ(0.412), τ(0.67) and the Angstrom exponent, b, are shown. </li></ul><ul><li>The daily satellite co-retrieval data for July 15-Aug15, 1999 period are shown on a separate web-page. </li></ul>
  19. 19. Co-Retrieval Procedures and Illustration
  20. 20. Surface and Aerosol Co-Retrieval Procedure <ul><li>Remove air scattering and absorption from daily images </li></ul><ul><li>Generate aerosol and cloud-free surface from long-term data </li></ul><ul><li>Estimate the initial aerosol properties, P and τ over non-cloudy areas </li></ul><ul><li>Derive the aerosol τ for each pixel based on an excess reflectance </li></ul><ul><li>Reconstruct daily aerosol-free surface reflectances </li></ul><ul><li>Loop to update aerosol properties in 3 using improved value of P. </li></ul>
  21. 21. Nearly Cloud and Haze-Free Northeast (Click on the Images to View) The haze and cloud-free image was constructed as lowest reflectance from 28 days of data (July15-Aug 15, 1999). Some areas show residues of haze and clouds. The first step is to create cloud and haze-free image of the surface reflectance. In the visible, 0.4 < λ < 0.7 the surface reflectance is relatively low (R=0.01- 0.1) and highly textured. The main colors are green (vegetation), yellow-brown (soil, concrete) and blue-black (water). The residue haze and cloud effects were removed from the ‘minimum image’ (except over the coastal areas). This image was used to calculate excess reflectance due to aerosols .
  22. 22. Retrieved Aerosol Optical Thickness, τ (Click on the Images to View) <ul><li>Total reflectance due to surface, haze and clouds. </li></ul>The Angstrom slope b of the spectral AOT (τ ~ λ -b ) is sharply reduced over the ‘misty’ haze region Aerosol optical thickness at 0.412 shows large patches of τ > 0.5.The black areas are from the cloud mask. The τ at 0.67 shows a sharply delineated area of ‘mist’ i.e. thick gray haze.
  23. 23. Hazy and Haze-Corrected Surface Reflectance (Click on the Images to View) Over the ocean with thick haze, the haze correction removes over 90% of the signal Haze correction over land retrieves the vegetation spectral pattern in the visible and near IR. Reflectance with haze removed Total reflectance due to clouds, haze and surface
  24. 24. Summary and Conclusions from the Pilot Study <ul><li>The aerosol and the surface parameters over cloud-free land and the ocean can be retrieved from daily 8 wavelength SeaWiFS LAC satellite data. </li></ul><ul><li>A 30 day pilot study (July15-Aug 15, 1999) over the NE United States has shown the existence of large, 1000-scale hazy air masses with strongly varying spectral extinction , particularly near clouds. A full-scale calibration of the results has not taken place. </li></ul><ul><li>The pilot study also demonstrates that the methodology is potentially applicable for daily global-scale co-monitoring of aerosols and surface color over cloud-free areas. </li></ul><ul><li>It appears that the next level of improvement in co-retrieval would benefit from the closer interaction of experts in ocean and land surface color and aerosol characterization </li></ul><ul><li>This is work in progress and it will be periodically updated. It is shared here for purposes of seeking interaction with interested researchers. Interested? Pleas contact us: rhusar@me.wustl.edu </li></ul>
  25. 25. Acknowledgements and Disclaimer <ul><li>This work was supported by an EPA cooperative agreement with the Center for Air Pollution Impact and Trend Analysis (CAPITA) at Washington University, St. Louis. </li></ul><ul><li>The SeaWiFS Pre-Processing routines (L1A-L1B Raleigh Correction) procedures were written by Fang Li of CAPITA. His tireless effort is gratefully acknowledged. </li></ul><ul><li>The technical assistance of the NASA SeaWiFS group ( Norman Kuring ) as well as the encouragement to conduct this development by Chuck McClain is herewith gratefully acknowledged. </li></ul><ul><li>The Rayleigh correction algorithm was provided to us by Eric Vermote of NASA Goddard, EOS/MODIS team. </li></ul><ul><li>This is work in progress and it will be periodically updated. References (links) to other works on aerosol retrieval/surface detection, particularly to the extensive work by Tanre, Kaufmann, King… will also be added. </li></ul>
  26. 27. Filter conditions