Over the past decade, remote sensing has become a key tool for monitoring and predicting environmental variables. Remotely sensed observations from ground based, airborne and space-borne instruments are providing improved understanding of the water, energy, and carbon exchanges between the ground and the atmosphere, and enhanced prediction capabilities of land surface processes.
This session aims to highlight state of the art of remote sensing on different land surface parameters and related algorithms involved in developing these parameters. Specific areas of interest include new remote sensing techniques, innovative land surface parameter retrieval algorithms, improved interpretation of remotely sensed observations for physical process understanding, and use for improving model predictive skill.