The circular sensing model has been widely used to estimate performance of sensing applications in existing analysis and simulations. While this model provides valuable high-level guidelines, the quantitative results obtained may not reflect the true performance of these applications, due to the existence of obstacles and sensing irregularity introduced by insufficient hardware calibration. In this project, we design and implement two Sensing Area Modeling (SAM) techniques useful in the real world. They complement each other in the design space. P-SAM provides accurate sensing area models for individual nodes using controlled or monitored events, while V-SAMprovides continuous sensing similarity models using natural events in an environment. With these two models, we pioneer an investigation of the impact of sensing irregularity on application performance, such as coverage scheduling. We evaluate SAM extensively in real-world settings, using three testbeds consisting of 40 MICAz motes and 14 XSMmotes. To study the performance at scale, we also provide an extensive 1,400-node simulation. Evaluation results reveal several serious issues concerning circular models, and demonstrate significant improvements.