A fog simulation method based on depth estimation is proposed, aiming at the lack of foggy image datasets. The brightness and saturation are adjusted adaptively to preprocess the clear original image, self-supervised monocular depth mining network is used to generate the depth map and which is optimized by guided filtering. Transmittance map is obtained with setting the visibility of the simulated image, the dark channel map is used to distinguish sky area to estimate the atmospheric light value, and simulated foggy image with visibility is generated through the atmospheric scattering model. According to the experimental data, the problems of unclear targets in simulated images and sharpening of fog edges are improved effectively. The effect is stable when simulated foggy visibility is below 2000 m, which average error rate of feature evaluation index between simulated foggy image and real foggy image is 6.28%, which shows that the proposed method is feasible. It can simulate clear images in natural environment to solve the problems of lack of foggy image dataset and visibility data.