In this paper, the estimation of physical properties from hyperspectral data with support vector machine is addressed. Several kernel functions were used, from classical to advanced ones. The results are compared with Gaussian Regularized Sliced Inversion Regression and Partial Least Squares, both in terms of accuracy and complexity. Experiments on simulated data show that SVM produce highly accurate results, for some kernels, but with an increased of the processing time. Inversion of real images shows that SVM are robust and generalize well. In addition, the analysis of the support vectors allows to detect saturation of the physical model used to generate the simulated data.