Abstract
A tutorial review is provided of forward and inverse radiative transfer in coupled atmosphere-snow/ice-water systems. The coupled system is assumed to consist of two adjacent horizontal slabs separated by an interface across which the refractive index changes abruptly from its value in air to that in ice/water. A comprehensive review is provided of the inherent optical properties of air and water (including snow and ice). The radiative transfer equation for unpolarized as well as polarized radiation is described and solutions are outlined. Several examples of how to formulate and solve inverse problems encountered in environmental optics involving coupled atmosphere-water systems are discussed in some detail to illustrate how the solutions to the radiative transfer equation can be used as a forward model to solve practical inverse problems.
Highlights
Reliable, accurate, and efficient modeling of electromagnetic radiation transport in turbid media has important applications in studies of Earth’s climate by remote sensing
We have described a computational tool, Accurate Radiative Transfer (AccuRT), for radiative transfer simulations in a coupled system consisting of two adjacent horizontal slabs with different refractive indices
The computer code accounts for reflection and transmission at the interface between the two slabs, and allows for each slab to be divided into a sufficiently large number of layers to resolve the variation in the inherent optical properties (IOPs), described in Section 3, with depth in each slab
Summary
A primary goal in remote sensing of the Earth from space is to retrieve information about atmospheric and surface properties from measurements of the radiation emerging at the top-of-theatmosphere (TOA) at several wavelengths [24,81]. These retrieval parameters (RPs), including cloud phase and optical depth, aerosol type and loading, and concentrations of aquatic constituents in an open ocean or coastal water area, depend on the inherent optical properties (IOPs) of the atmosphere and the water. The purpose of this section is not provide a comprehensive review of forward-inverse methodology, but rather to provide a few examples of how RT modeling involving coupled atmospherewater systems described in the previous sections can be used to solve the inverse problem with an emphasis of how machine learning techniques (neural networks) can be used to our advantage
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