In remote sensing, radiative transfer models (RTM) provide a physically-based link between the bio-physical variables of the Earth surface and the spectral signature recorded by Earth Observation (EO) sensors. Combined with machine learning approaches, RTM can be used to retrieve important properties of the land surface, while allowing for a less data demanding training, offering advantages in terms of model interpretability and providing useful priors.
Bio: Clement Atzberger is Full Professor for Geomatics and remote sensing at BOKU university and one of the co-founders of fin-tech company Mantle-Labs. His interests cover machine learning, radiative transfer modeling, time series analysis for the assessment of natural resources using Earth Observation data.
