Abstract : Hyperspectral remote sensing, also known as imaging spectroscopy, is a promising space technology regularly selected by agencies with regard to the exploration and observation of planets, to earth's geology or to the monitoring of the environment. It allows to collect for each pixel of a scene, the intensity of light energy reflected from planets as it varies across different wavelengths. More than one hundred spectels in the visible and near infra-red are typically recorded, making it possible to observe a continuous spectrum for each image cell. Usually, in space exploration, the analysis of these spectral signatures allows to retrieve the physical, chemical or mineralogical properties of surfaces and of atmospheres that may help to understand the geological and climatological history of planets. We propose in this paper a statistical method to evaluate the physical properties of surface materials on Mars from hyperspectral images collected by the OMEGA instrument aboard the Mars express spacecraft. The approach we develop is based on the estimation of the functional relationship F between some physical parameters and observed spectra. For this purpose, a database of synthetic spectra is generated by a physical radiative transfer model and used to estimate F. The high dimension of spectra is reduced by using Gaussian regularized sliced inverse regression (GRSIR) to overcome the curse of dimensionality and consequently the sensitivity of the inversion to noise (ill-conditioned problems). Compared with a naive spectrum matching approach such as the k-nearest neighbors algorithm, estimates are more accurate and realistic.