- durée total des cours : 25 h
- avril ou juin 2019, mise à jour à venir rapidement
- 15h de travaux pratiques en salle informatique et 10h de cours magistral - batiment Condorcet de l'Université Paris Diderot - salle à venir

Characterizing natural surfaces: remote observations, modeling and inversion

Remote sensing of planetary surfaces by satellites or spacecraft provides important clues on their properties, at global scale or/and high spatial resolution. They help following their structural and temporal evolution over days or years which may impact human activities and conditions of living or provide unique knowledge on unexplored extraterrestrial surfaces. Radiances originating from various depths in the near surface and measured over a large range of wavelengths, directions and timescales are modulated by its structure and composition. Heat and radiative transfer models have been developed to infer these properties from radiances.

This course aims at delivering skills in multi-wavelengths remote sensing and modeling of natural surfaces and at training participants in data inversion of satellite/spacecraft data from visible to the thermal/microwave infrared domain. It will be delivered in the form of 10h (5 x 2h) of main lessons, merged with a training session (15h=5*3h). This will consist of characterizing the properties of a specific surface among various solar system bodies such as the Moon, icy satellites of Saturn, etc, using databases from NASA missions mainly. This training includes data manipulation, model study (sensitivity analysis,...) and data inversion based on Bayesian inference under Python environment.