The landing process of the quadrotors can be affected by the disturbance from the ground effect when approaching the landing surface. Such a disturbance significantly increases the chances of collision and jittering of the quadrotors, thereby posing threats to the safety of both the quadrotors and the mounted equipment. In light of this, we propose SmoothLander, an aerodynamics and reinforcement learning-based control system to stabilize the quadrotors under the influence of the ground effect and control noise. First, we design a landing trajectory for the quadrotor in accordance with aerodynamics. Then we design a reinforcement learning-based command generator to effectively optimize the quadrotor’s landing behavior. We evaluate our control system through physical feature-based simulation and in-field experiments. The results show that our method can enable the quadrotor to land more smoothly and stably against control noise than the baseline.