physical or intuitive considerations, without any careful and thorough analysis of their ability to properly describe real mixtures while performing spectral unmixing. In this article, we propose to fill this gap by evaluating the relevance of various nonlinear models when used for spectral unmixing of images acquired over vegetated areas. Specifically, requirements for ensuring the quality of a model in this specific applicative context are threefold: *(i)* the model should not depend on external parameters related to the studied scene (e.g., leaf index area, geometry or illumination incidence) since this prior knowledge is generally not available, *(ii)* this model should be still sufficiently flexible to fit the real observations in various external conditions, despite the ignorance of these unknown external parameters, *(iii)* it should be able to account for the relative spatial distribution of the materials in the pixel, with the prime objective to estimate the abundance coefficients. In particular, mainly because of the two first requirements enounced above, advanced nonlinear models proposed in the remote sensing literature (e.g., [@Borel1994]) will not be considered in this study since they need a detailed prior knowledge regarding the analyzed scene.

To meet this challenge, we take advantage of an interesting set of 