The fast motion of Low Earth Orbit (LEO) satellites causes the propagation channel to vary rapidly, and its behavior is strongly shaped by the surrounding environment, especially at low elevation angles where signals are highly susceptible to terrain blockage and other environmental effects. Existing studies mostly rely on assumed statistical channel distributions and therefore ignore the influence of the actual geographic environment. In this paper, we propose an environment-aware channel modeling method for air-to-ground wireless links. We leverage real environmental data, including digital elevation models (DEMs) and land cover information, together with ray tracing (RT) to determine whether a link is line-of-sight (LOS) or non-line-of-sight (NLOS) and to identify possible reflection paths of the signal. The resulting obstruction and reflection profiles are then combined with models of diffraction loss, vegetation absorption, and atmospheric attenuation to quantitatively characterize channel behavior in realistic geographic environments. Since RT is computationally intensive, we use RT-generated samples and environmental features to train a scalable diffusion model that can efficiently predict channel performance for arbitrary satellite and ground terminal positions, thereby supporting real-time decision-making. In the experiments, we validate the proposed model with measurement data from both cellular and LEO satellite links, demonstrating its effectiveness in realistic environments.

翻译：低地球轨道卫星的高速运动导致传播信道快速变化，其行为受周围环境的强烈影响，特别是在低仰角情况下信号极易受到地形阻挡及其他环境效应的影响。现有研究大多依赖假设的统计信道分布，因而忽略了实际地理环境的影响。本文提出一种面向空对地无线链路的感知环境信道建模方法。我们利用真实环境数据（包括数字高程模型和土地覆盖信息）结合射线追踪技术，判断链路为视距或非视距链路，并识别信号可能的反射路径。随后将由此生成的遮挡与反射特征与绕射损耗、植被吸收及大气衰减模型相结合，以定量表征实际地理环境中的信道行为。由于射线追踪计算量庞大，我们利用射线追踪生成的样本与环境特征训练可扩展的扩散模型，该模型能高效预测任意卫星与地面终端位置对应的信道性能，从而支持实时决策。实验中，我们采用蜂窝网络与低轨卫星链路的实测数据对所提模型进行验证，证明了其在真实环境中的有效性。