The amount and spatial distribution of forest resources in the boreal zone are highly debated because they are often roughly quantified and seldom verified. Gaps and errors in available datasets of growing stock volume (GSV) or aboveground biomass imply that carbon stocks assessments can suffer from substantial uncertainties. Satellite remote sensing supports mapping and monitoring of forest resources on a large scale because of its synoptic view, frequent revisit capability, relatively low cost and sensitivity of the observable to either biophysical or structural properties of forests. For the boreal zone, observations of the radar backscatter by Envisat ASAR ScanSAR are available with an almost daily frequency and are systematically available to the scientific community. Despite the short wavelength, it is possible to retrieve forest growing stock volume using a multi-temporal approach, which reduces the retrieval error substantially when compared to an estimate based on a single measurement of the radar backscatter. Scope of this project was the validation of a fully automated approach for the retrieval of GSV starting from a hyper-temporal set of Envisat ASAR ScanSAR backscatter measurements (BIOMASAR algorithm) and its application to generate a pan-boreal map of GSV for the year 2010 at 1 km spatial resolution. Validation at local sites showed that the retrieval error is on the order of 40%, decreasing to 25% when averaging over at least 100 pixels (e.g. from 1 km to 10 km). The pan-boreal map has been produced from 65,000 ASAR data strips using the computing resources of the Grid Processing on Demand (G-POD) platform. In total, 565 Gbyte of SAR data were used to generate the final estimate of GSV. The pan-boreal estimates of GSV have been used computing to improve understanding of the time it takes for a carbon atom fixed in a plant by photosynthesis to return into the atmosphere as carbon dioxide - known as 'carbon turnover'. According to this study published in Nature, the global average carbon turnover is 23 years. But the study shows, for the first time, how this turnover time varies in different regions around the world.