Opportunistic vehicular sensing is a new paradigm which exploits variety of sensors embedded in vehicles or smartphones to collect data ubiquitously for large-scale urban sensing. Existing work lacks in-depth investigations on the coverage problem in such sensing systems: (1) how to define and measure the coverage? (2) what is the relationship between the coverage quality and the number of vehicles? and (3) how to select the minimum number of vehicles to achieve the specific coverage quality? First, we propose a metric called Inter-Cover Time (ICT) to characterize the coverage opportunities. According to the empirical measurement studies on real mobility traces of thousands of taxis, we find that the aggregated ICT Distribution (ICTD) follows a truncated power-law distribution. We also analyze the reasons behind this particular pattern by evaluating four known mobility models. Second, we propose a metric called opportunistic coverage ratio, and derive it as a function of the aggregated ICTD. We also analyze the changes of opportunistic coverage ratios on different days of a week. Finally, we present a vehicle selection algorithm to address the third problem. In addition, we present a framework of recruiting vehicles, serving as fundamental guidelines on the coverage measurement and network planning for urban vehicular sensing applications.