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Spectral reflectance is an inherent material property which can be used for material identification tasks. We present a nonlinear algorithm for estimating surface spectral reflectance of the ground material using images acquired by an airborne sensor. The nonlinear algorithm separates the atmospheric and illumination effects from the measured spectral radiance corresponding to a single pixel in the image to recover the surface spectral reflectance. A low-dimensional subspace model for the reflectance spectra is used for the algorithm. The algorithm also considers the interdependence of the path radiance and illumination spectra by using a coupled subspace model. We have analyzed the accuracy of the algorithm over simulated and real 0.4-1.74-mum sensor radiance spectra.