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Green's function method in the radiative transfer problem. II. Spatially heterogeneous anisotropic surface.

Alexei Lyapustin1, Yuri Knyazikhin

  • 1Goddard Earth Sciences and Technology Center, University of Maryland Baltimore County, USA. alyapustin@pop900.gsfc.nasa.gov

Applied Optics
|September 25, 2002
PubMed
Summary

Three-dimensional (3-D) radiation effects significantly impact optical remote sensing accuracy for aerosols and land surfaces. A new radiative transfer theory rigorously addresses these 3-D effects and surface anisotropy for improved data retrieval.

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Area of Science:

  • Atmospheric science
  • Optical remote sensing
  • Radiative transfer theory

Background:

  • Three-dimensional (3-D) radiation effects are crucial in optical remote sensing but are often ignored or inadequately addressed.
  • These effects introduce systematic biases in land surface albedo datasets from global observation systems.
  • Existing algorithms lack the rigor to handle both 3-D effects and surface anisotropy.

Purpose of the Study:

  • To develop advanced radiative transfer theory capable of treating 3-D effects and surface anisotropy.
  • To enable the creation of fast forward and inversion algorithms for remote sensing data processing.
  • To improve the accuracy of atmospheric aerosol and land surface reflectance retrievals.

Main Methods:

  • Developed a novel theoretical solution for the 3-D radiative transfer problem.

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  • Utilized an exact semianalytical solution in operator form via the Green's function method.
  • Implemented numerical parameterizations to accelerate computations while maintaining accuracy.

    • Main Results:

    • The new solution accurately addresses 3-D radiative transfer with arbitrary nonhomogeneous, non-Lambertian surfaces.
    • Numerical implementation significantly accelerates the solution process.
    • Accuracy is maintained within several percent under general conditions.

    Conclusions:

    • The developed theory provides a robust framework for incorporating 3-D radiation effects in remote sensing.
    • This advancement is expected to reduce systematic biases in land surface albedo data.
    • The method facilitates more accurate optical remote sensing of Earth's atmosphere and surface.