Currently, unmanned aerial vehicles (UAV) have been widely used in many applications, such as in transportation logistics, public safety, or even in non-terrestrial networks (NTN). In all these scenarios, it is an important issue to model channel behavior between the UAV and the user equipment (UE) on the ground. Among these channel features, a critical parameter that dominates channel behavior is the probability of the line-of-sight (LOS), since the statistical property of the channel fading can be either Ricean or Rayleigh, depending on the existence of LOS. Besides, with knowledge of LOS probability, operators can design approaches or schemes to maximum system performance, such as the serving coverage, received signal to noise ratio (SNR), or the bit error rate (BER) with the limited transmitted power. However, the LOS UAV channel is likely difficult to acquire or derive, as it depends on the deployment scenario, such as an urban or rural area. In this paper, we generated four different scenarios defined by the ITU via the ray tracing simulator. Then, we used the spatial geometric relation and the curve fitting approach to derive the analytic models to predict the probability of the UAV LOS channels for different scenarios. Results show that our proposed relationships yield better prediction results than the methods in the literature. Besides, an example of establishing UAV self-awareness ability for the deployed environment via using proposed models is also provided in this paper.