Abstract
The linear spectrum-mixing model for hyperspectral data analysis is evaluated in the context of the stochastic radiative transfer equations. The stochastic radiative transfer equations that treat media as binary stochastic mixtures are reformed and then solved within a discrete random layer over a rough interface. The layer constituents are randomly distributed within ellipsoidal spatial cells of various sizes. The solution of the stochastic radiative transfer equations is used to investigate the validity domain of the linear spectrum-mixing model. The solution is also used to estimate the deviations in the predictions of the linear spectrum-mixing model for the layer brightness temperature.
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