The reflectance spectrum of green vegetation is mainly determined by the leaf content in chlorophyll and water, the respective spectral signatures being modulated by the structural characteristics of the leaf and the canopy. However, correlation between the reflectance spectra and the leaf content in such constituents as lignin, cellulose, nitrogen, starch, etc. has been demonstrated. To further study this question, we have constituted a data set associating high quality spectra (of single leaves, optically thick stacks, needles, stalks, on fresh and dried material) with a number of physical and chemical measurements (leaf thickness, water content, chlorophyll, carotenoids, cellulose, lignin, proteins, nitrogen, starch). This data set has been used to investigate the link between the optical properties and the composition, focusing on the biochemical components. Two approaches have been followed: the classical regression analysis and modeling based on the Kubelka-Munk formula. In the latter case, empirical specific absorption coefficients have been determined and then used to decompose the infrared spectra in water and other components contributions. This method is successful in retrieving the relative water content but does not yet allow us to estimate the biochemical content. It was applied both on laboratory and AVIRIS spectra.