No abstract

Fog generally forms at dawn and dusk, which exerts serious impacts on public traffic and human health. Terrain strongly affects fog formation, which provides a useful clue for fog detection from satellite observation. With the aid of the advanced Himawari-8 imager data (H8/AHI), this study develops a deep learning algorithm for fog detection at dawn and dusk under terrain-restriction and enhanced channel domain attention mechanism (DDF-Net). The DDF-Net is based on the traditional U-Net model, with the digital elevation model (DEM) data acting as the auxiliary information to separate fog from the low stratus. Furthermore, the squeeze-and-excitation networks (SE-Net) is integrated to optimize the information extraction for eliminating the influence of solar zenith angles (SZA) on the spectral characteristics over a large region. Results show acceptable accuracy of the DDF-Net. The overall probability of detection (POD) is 84.0% at dawn and 83.7% at dusk. In addition, the terrain-restriction strategy improves the results at the edges of foggy regions and reduces the false alarm rate (FAR) for low stratus. The accuracy is expected to be improved when training at a season or month scale, rather than at a longer temporal scale. Results of our study help to improve the accuracy of fog detection, which could further support the relevant traffic planning or healthy travel.