Developing highly scalable algorithms for satellite imagery simulations is becoming increasingly important as scientists inquire to understand the mechanism of satellite imagery before satellites are launched into orbit. Although physically based ray tracing technique for image rendering has produced some of the most realistic images to date, studies on satellite imagery simulations using this technique are still very less to be seen, due in large part to both the complex physical processes and the computational difficulties of the mathematical models. In this paper, we present a highly scalable physically based ray tracer for satellite imagery simulations. Our ray tracer is based on a Master-Worker-Receiver framework which can overcome the performance bottleneck of Master node. Besides, a novel sample distribution strategy is presented by the authors, aiming at removing high additional computation overhead which is introduced by the currently available pixel distribution strategy. Compared to the pixel distribution strategy, our sample distribution strategy drops the computation overhead by 0.25 to 4 times. We also discuss the issue with granularity of assignment partitioning for Inter-Nodes and Intra-Nodes, then a hybrid scheduling strategy combining static and dynamic scheduling strategies is presented. Experiments show that our physically based ray tracer almost reaches to a linear speedup by using 16,800 CPU cores on Tianhe-1A Supercomputer. Our ray tracer is more efficient and highly scalable.