The Global Navigation Satellite System (GNSS) comprises the U.S. system GPS (Global Positioning System), its Russian pendant GLONASS, and presumably, in the future, the European system Galileo. The potential of GNSS-based phase delay measurements for accurately estimating vertically and slant-path-integrated water vapor has been demonstrated recently for radio links between GPS satellites and ground-based GPS receivers. GNSS-based radio occultation, on the other hand, has been demonstrated via the GPS/Meteorology experiment to deliver accurate near-vertical profiles of atmospheric variables such as temperature and humidity with high vertical resolution. Height-resolving imaging of atmospheric water vapor becomes feasible when occultation profiles from spaceborne receivers in Low Earth Orbits (LEO) are combined with ground-based GNSS data from a colocated receiver network. We developed a two-dimensional, height-resolving tomographic imaging technique following the Bayesian approach for optimal combination of information from different sources. Using simulated GNSS-based water vapor measurements from LEO and ground, we show representative results derived from simple synthetic refractivity fields as well as from a realistic refractivity field based on a European Centre for Medium-Range Weather Forecasts (ECMWF) analysis. For cases located poleward of ∼40° we found a new simple mapping function to perform best within our forward model scheme, where the only free parameter is the climatological scale height in the troposphere, the exact value of which is not critical. The mapping function exploits the ratio between the straight-line ray path length within the first two scale heights above surface and the “effective height” defined by these first two scale heights. We found our technique capable of reconstructing atmospheric features like water vapor maxima near the top of the trade wind inversion. Adjustment of the integral over the water vapor profile measurements to the horizontally averaged ground-based vertical integrated water vapor data efficiently mitigates potential biases in the former data. Accuracies are best in areas with high absolute humidities but also over drier areas such as Finland, useful two-dimensional information can still be obtained. Thus it is attractive to apply the developed technique in a next step to real data.