Currently, global-scale monitoring of vegetation properties by remote sensing is almost always based on the use of optical or infrared sensors. The information provided by these sensors is most often related to e.g. the chlorophyll content, photosynthetic properties, and spectral and/or thermal characteristics of the canopy surface. However, passive microwave remote sensing has the potential to offer unique and complementary information, as at these higher frequencies most vegetation is semi-transparent. The observations are therefore representative of the entire vegetation canopy, rather than just the top. This information can be derived from the passive microwave ‘vegetation optical depth’ variable, which is related to vegetation water content, structure and biomass. The relationship with vegetation water is of strong interest, as plant water status is one of the key controls on photosynthesis and transpiration. Vegetation water status determines both the carbon uptake and the water use of the plant, through the common pathway of the two fluxes – the leaf stomata. In this way, vegetation water status forms a crucial link between the carbon and water cycles. Besides having clear applications in the field of agriculture, as it is directly related to plant water stress, it is also of strong interest for e.g. terrestrial biosphere and climate modelling. In this study, gravimetric vegetation water content was obtained from vegetation optical depth measurements made by ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite mission. Being a gravimetric measurement, it gives the amount of water available per unit of fresh biomass, i.e. it is expressed in [kg·kg-1]. As such it is independent of the amount of biomass per area, and thus it is strongly linked to the actual water status of the vegetation. The approach taken was to combine an effective medium model valid at passive microwave frequencies with a vegetation dielectric constant model. Besides SMOS observations of passive microwave optical depth, inputs were skin temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and literature values of vegetation bulk density. The algorithm was calibrated for each of the 11 global vegetation classes in the University of Maryland (UMD) global landcover classification scheme. Calibration was done using a combination of in situ measurements from the FLUXNET global network and simulations of leaf water potential from the Soil-Plant-Atmosphere (SPA) model, the only model of its kind to explicitly model plant water transport. The resulting product consists of temporally dynamic (daily to monthly) 0.25° global grids of gravimetric vegetation water content. The maps clearly show seasonal differences in vegetation water, which vary for the different continental regions due to variations in e.g. latitude, climate GV2M: Global Vegetation Monitoring and Modeling water, which vary for the different continental regions due to variations in e.g. latitude, climate and landcover type. In general the gravimetric water content of the vegetation can be seen to decrease during the summer months and increase again towards the winter. This behavior is opposite to that generally observed in well-known optical vegetation indices such as e.g. NDVI and Leaf Area Index (LAI), which are dependent on biomass per area. Thus, this new gravimetric vegetation water product is unique and offers important complementary information to existing indices. In a next step, the product will be implemented in a Carbon Cycle Data Assimilation System (CCDAS), which is built around a modified version of the Biosphere Energy-Transfer Hydrology scheme (BETHY), in order to improve estimations of terrestrial carbon and water fluxes.Read less <