Cloud remote sensing with sideways looks: theory and first results using Multispectral Thermal Imager data

In operational remote sensing, the implicit model for cloud geometry is a homogeneous plane-parallel slab of infinite horizontal extent. Each pixel is indeed processed as if it exchanged no radiant energy whatsoever with its neighbors. The shortcomings of this conceptual model have been well documented in the specialized literature but rarely mitigated. The worst-case scenario is probably high-resolution imagery where dense isolated clouds are visible, often both bright (reflective) and dark (transmissive) sides being apparent from the same satellite viewing angle: the low transmitted radiance could conceivably be interpreted in plane-parallel theory as no cloud at all. An alternative to the plane-parallel cloud model is introduced here that has the same appeal of being analytically tractable, at least in the diffusion limit: the spherical cloud. This new geometrical paradigm is applied to radiances from cumulus clouds captured by DOE's Multispectral Thermal Imager (MTI). Estimates of isolated cloud opacities are a necessary first step in correcting radiances from surface targets that are visible in the midst of a broken-cloud field. This type of advanced atmospheric correction is badly needed in remote sensing applications such as nonproliferation detection were waiting for a cloud-free look in the indefinite future is not a viable option.

[1]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[2]  A. Marshak,et al.  Multiple Scattering in Clouds: Insights from Three-Dimensional Diffusion/P1 Theory , 2001 .

[3]  R. Davies,et al.  The Effect of Finite Geometry on the Three-Dimensional Transfer of Solar Irradiance in Clouds , 1978 .

[4]  Alvin M. Weinberg Linear transport theory: by K. M. Case and P. W. Zweifel. 342 pages, diagrams, illustr. 6 × 9 in. Reading, Mass. Addison-Wesley Publ. Co., 1967. Price, $17.50 , 1968 .

[5]  John Harte,et al.  Consider a Spherical Cow: A course in environmental problem solving , 1985 .

[6]  Yuri Knyazikhin,et al.  Cloud‐vegetation interaction: Use of normalized difference cloud index for estimation of cloud optical thickness , 2000 .

[7]  R. Giovanelli,et al.  Radiative Transfer with Distributed Sources , 1956 .

[8]  Anthony B. Davis,et al.  Nonlocal independent pixel approximation: direct and inverse problems , 1998, IEEE Trans. Geosci. Remote. Sens..

[9]  G. Rybicki Radiative transfer , 2019, Climate Change and Terrestrial Ecosystem Modeling.

[10]  R. Preisendorfer,et al.  Multimode radiative transfer in finite optical media. I - Fundamentals. II - Solutions , 1984 .

[11]  Thierry Faure,et al.  Neural network retrieval of cloud parameters of inhomogeneous and fractional clouds , 2001 .

[12]  E. Leontieva,et al.  Remote Sensing of Cloud Optical Properties from Ground-Based Measurements of Transmittance: A Feasibility Study , 1996 .

[13]  A. Schuster Radiation through a foggy atmosphere , 1903 .

[14]  Richard J. Blakeslee,et al.  Diffusion model for lightning radiative transfer , 1994 .

[15]  Anthony B. Davis,et al.  MTI science, data products, and ground-data processing overview , 2001, SPIE Defense + Commercial Sensing.

[16]  Multimode Radiative Transfer in Finite Optical Media. II: Solutions , 1984 .

[17]  F. Reif,et al.  Fundamentals of Statistical and Thermal Physics , 1965 .

[18]  Qilong Min,et al.  Cloud properties derived from surface MFRSR measurements and comparison with GOES results at the ARM SGP Site , 1996 .