Assimilation of data from heterogeneous sensors and sensor networks is critical for achieving accurate measurements of environmental processes at the time and space scales necessary to improve forecasting and decision-making. Owing to different measurement accuracies and types of spatial and/or temporal measurement support of the component sensors, it is often unclear how best to combine these data. This study explores the utility of ubiquitous sensors producing categorical wet/dry rainfall measurements for improving the resolution of areal quantitative precipitation estimates through fusion with weather radar observations. The model developed in this study employs a Markov random field model to compute the probability of rainfall at sub-grid pixels. These likelihoods are used to ‘unmix’ the cell-averaged rainfall rate measured by the radar. Simulation studies using synthetic and known rainfall fields reveal that the model can improve remotely sensed quantitative rainfall intensity measurements by 40% using networks of ubiquitous sensors with a density of 56 sensors per square kilometer, and for denser networks, the accuracy can increase by as much as 50%.