The international EU-funded SIBERIA project (1998-2000) aimed at the production of an extensive forest map using spaceborne SAR data acquired by the ERS and JERS, satellites. For a large geographical region (900.000 km(2)) located in the Central Siberian Plateau, one-day ERS coherence and JERS backscatter were used to retrieve growing stock volume. A classification algorithm based on peaks in the coherence and backscatter histograms was used. Four volume classes, water and open land were considered. An independent test in 10 areas showed an accuracy above 80%. The produced forest map serves as a tool for the sustainable management of Siberian natural resources and for a better understanding of the role of boreal forests in climate change. The objective of the international EU-funded SIBERIA-II project (2002-2005) is to demonstrate the viability of full carbon accounting, including all greenhouse gasses, with a multi-sensor approach over a 2 million-km2 area in Siberia. Having recently started, a general overview of the aims and the objectives of the project is given. Using several satellite observations available and the SIBERIA database, the first step consists in the generation of several Earth Observation (EO) products (such as biomass, phenological parameters, soil moisture, snow cover etc). Together with land-cover information from local institutions, these products will be input to two dynamic vegetation models for full regional carbon accounting. To increase knowledge, additional products such as Afforestation-Reforestafion-Deforestation and fire scars maps are planned.

ALBIOC (ALbedo- BIOsphere- Carbon) is an integrated terrestrial biosphere model designed as a too] to explore the effects of climate and atmospheric CO, concentration on vegetation, land-surface characteristics and carbon storage. The model is based, although designed to be simple in structure and computationally fast, on biophysical and ecophysiological principles and simulates in a fully interactive manner the potential distribution of vegetation, terrestrial carbon storage and physical land-surface properties. Testing was extensive and focused on broad spatial patterns (5 degrees resolution) of biome distribution, and variables important for the surface energy balance and hydrological cycle (seasonal snow cover, surface albedo, runoff and evaporation) and for the global carbon cycle (seasonal canopy cover, primary production and carbon storage). Because ALBIOC simulates a range of physical and biogeochemical variables in an integrated way, it was possible to test the model against a more comprehensive range of indicators than has normally been the case for terrestrial biosphere models. The simulated vegetation distribution is as accurate as more specialised biogeography models taking into account the coarse resolution of the model. ALBIOC simulates a global NPP of 57 PgC/year, which is in the range of the values found in the literature and other model estimates. Land-surface albedo. snow depth, runoff, and FPAR showed a generally good agreement with observations within the known limits of available data sets of these variables. The model's mechanistic basis would allow extension to simulate, e.g. transient response to rapid climate change (vegetation dynamics) and carbon isotopic balances. while its computational efficiency renders it suitable for inclusion in Earth system models of intermediate complexity. (C) 2001 Elsevier Science B.V. All rights reserved.