Dans le cadre de la mission spatiale SMOS (Soil Moisture and Ocean Salinity), nous présentons dans cette thèse une nouvelle approche numérique de modélisation du calcul de l’émissivité et du coefficient bi-statique de systèmes forestiers sol-litière en Bande L. Le système sol-litière est représenté par deux couches diélectriques 3D comportant des interfaces rugueuses, une démarche qui n’apparait pas actuellement dans la littérature. Nous validons notre approche pour une seule couche en comparant les simulations de l'émissivité avec celles produites par la méthode des moments et des données expérimentales. A partir de ce nouveau modèle, nous évaluons la sensibilité de l’émissivité du système sol-litière en fonction de l’humidité et de la rugosité de la litière. Ce nouveau modèle permettra de créer une base de données synthétiques d’émissivités calculées en fonction de nombreux paramètres qui contribuera à améliorer la prise en compte de la litière dans l'algorithme d’inversion des données de la mission spatiale SMOS. / In the context of the SMOS (Soil Moisture and Ocean Salinity) mission, we present a new numerical modelling approach for calculating the emissivity and bistatic scattering coefficient of the soil-litter system found in forests, at L-band. The soil-litter system is modelled as two 3-dimensional dielectric layers, each with a randomly rough surface, which to our knowledge has not previously been achieved. We investigate the validity of the approach for a single layer by comparing emissivity simulations with results of Method of Moments simulations, and experimental data. We then use the approach to evaluate the sensitivity of the soil-litter system as a function of moisture content and the roughness of the litter layer. The numerical modelling approach which has been developed will allow us in the future to create a synthetic database of the emissivity of the soil-litter system as a function of numerous parameters, which will contribute to validating and improving the inversion algorithm used by the SMOS mission to retrieve soil moisture over forests.

Indiana University-Purdue University Indianapolis (IUPUI) / Photoacoustic Imaging is a non-invasive optical imaging modality used to image biological tissues. In this method, a pulsating laser illuminates a region of tissues to be imaged, which then generates an acoustic wave due to thermal volume expansion. This wave is then sensed using an acoustic sensor such as a piezoelectric transducer and the resultant signal is converted into an imaging using the back projection algorithm. Since different types of tissues have different photo-acoustic properties, this imaging modality can be used for imaging different types of tissues and bodily organ systems. This study aims at quantifying the process of light conversion into the acoustic signal. Light travels through tissues and gets attenuated (scattered or absorbed) or reflected depending on the optical properties of the tissues. The process of light propagation through tissues is studied using Monte Carlo simulation software which predicts the propagation of light through tissues of various shapes and with different optical properties. This simulation gives the resultant energy distribution due to light absorption and scattering on a voxel by voxel basis. The Monte Carlo code alone is not sufficient to validate the photon propagation. The success of the Monte Carlo code depends on accurate prediction of the optical properties of the tissues. It also depends on accurately depicting tissue boundaries and thus the resolution of the imaging space. Hence, a validation algorithm has been designed so as to recover the optical properties of the tissues which are imaged and to successfully validate the simulation results. The accuracy of the validation code is studied for various optical properties and boundary conditions. The results are then compared and validated with real time images obtained from the photoacoustic scanner. The various parameters for the successful validation of Monte Carlo method are studied and presented. This study is then validated using the algorithm to study the conversion of light to sound. Thus it is a significant step in the quantification of the photoacoustic effect so as to accurately predict tissue properties.