With the start of the SENTINEL era, a major challenge for users is the efficient extraction of valuable information from an unprecedented amount of data. To provide data products that allow scientists, commercial users and decision makers to efficiently exploit these novel data, new methods are required to estimate land surface information from data retrieval, and to provide novel approaches and data dissemination. In this view, the MULTIPLY platform enables users to synergistically combine different satellite observations (including optical and SAR) together with additional a priori knowledge to provide inferences on land surface quantities (such as leaf area index, soil moisture, radiative fluxes, pigment concentrations, etc.) , as well as provide tools for information extraction and visualisation.
The platform uses state-of-the-art physical models of radiative transfer between the atmosphere and the land surface. The models allow for a coherent interpretion of different observation types. Additional information that constrains the inversion is also included as priors, which include not only expert or database-derived estimates of parameters but also dynamic models. This results in a continuous (in space and time) stream of parameters at high resolution (10s of m) that characterise the land surface, together with an estimate of their uncertainties.

This course is aimed at scientists, who require consistent and gap-free retrieval of land surface parameters, but are confronted with the limitations of current state-of-art approaches. Using a mix of hands-on demonstrators with the MULTIPLY platform, as well as theoretical background information, the course will deal with
• The basic concepts behind radiative transfer models
• The integration of a priori knowledge to land surface parameter retrieval.
• Combining observations and prior information in a Bayesian retrieval scheme
The course will focus on the specific challenges of current state-of-art approaches, and show the potential of MULTIPLY as a beyond-state-of-art framework, and highlight the platform as a useful tool for ecologists, agronomists and climate scientists who require timely information about the state of the land surface.