The atmosphere and the terrestrial biosphere are subsystems of the Earth system, which influence each other by exchanging energy, matter and information. These exchanges or interactions, organized on different temporal and spatial scales, require a better understanding and characterization that may ensure both an accurate description of the past processes and fluxes as well as a reliable short-term and long-term prediction of the climate and Earth system behavior and their impacts on human activities. In the last decades, a vast amount of data has been collected via in-situ instrumental networks and by Earth Observation (EO) systems characterizing several of the key variables governing land-atmosphere processes and interactions (e.g., aerosols, active fires, burned area, smoke plume height, LAI, fPAR, greenhouse gases concentrations, soil moisture, land cover, inundation, biomass, etc..). So far, however, these data sets have not been sufficiently exploited in an integrated and synergetic manner. On the one hand, traditional analysis strategies were often disciplinary and did not benefit from newly developed non-linear statistical techniques, which for example have been successfully applied in bioinformatics, which may allow the efficient exploitation of the synergic information provided by the different datasets in time and in space. On the other hand, the synergic use of different EO data sets (e.g., with different levels of maturity, accuracy and consistency) requires a significant effort of harmonization and error characterization that render this important task a significant scientific and technical challenge. In this context, a new generation of global indices is required in order to monitor the major dimensions of spatial and in particular temporal change from the highly multivariate and diverse observational data. Ideally, an index of this kind would provide an “early warning” system for changes in Biosphere-Atmosphere interactions and likewise find associations to human activity (for instance the human appropriation of natural resources). In this context, the objectives of the Couple-Biosphere-Atmosphere Virtual Lab is to build a hyper-cube of integrated datasets merging different EO-based products in a common spatial and temporal grid characterizing the key variables governing land-atmosphere in a platform supported by a number of data processing tools and methods that may allow the full exploration of the different spatial and temporal interactions among the different components of the land-atmosphere system. The ultimate target of the study is to exploit that capacity to derive a number of global indexes that may provide an overall dynamic view of the state of our planet and the major trends and hot spots of change in the land-atmosphere interactions