Invertible Physics-Based Hyperspectral Signature Models: A review

The richness of hyperspectral imaging (HSI)-collected signals makes possible quantitative inference by the inverse problem-solving of the chemical and biophysical parameters of the imaged object. In this article, we first propose a classification of the large variety of literature on invertible physics-based hyperspectral signature models by analyzing their founding hypotheses and methodologies. All the models found in the literature are based on the radiative transfer theory (RTT), but they are divided into three main branches. In the first one, the models rely on the Beer-Lambert law. In the second and the third ones, the models are based on Lommel's radiative transfer equation (RTE), which they simplify into a diffusion approximation equation or into a single- and multiple-scattering approximation equation, respectively. Thereafter, we present the most recent models available for each branch for applications in geoscience and remote sensing domains: MARMIT-1 and MARMIT-2 for soil, the Microphytobenthos Optical Model (MPBOM) for algae and bacteria biofilm, PROSPECT for plant leaves, Farrell for fruits and vegetables, and Hapke for solar system objects. On the one hand, this article provides an overview of the different works in the literature. On the other hand, it proposes a classification tree of the different models, allowing one to know the similarities and differences of the different models. Ultimately, the aim is to assist researchers in selecting an appropriate model based on their specific targeted application.