Green tide beaching events have occurred frequently in the Yellow Sea since 2007, causing a series of ecological and economic problems. Satellite imagery has been widely applied to monitor green tide outbreaks in open water. Traditional satellite sensors, however, are limited by coarse resolution or a low revisit rate, making it difficult to provide timely distribution of information about green tides in the nearshore. In this study, both PlanetScope Super Dove images and unmanned aerial vehicle (UAV) images are used to monitor green tide beaching events on the southern side of Shandong Peninsula, China. A deep learning model (VGGUnet) is used to extract the green tide features and quantify the green tide coverage area or biomass density. Compared with the U-net model, the VGGUnet model has a higher accuracy on the Super Dove and UAV images, with F1-scores of 0.93 and 0.92, respectively. The VGGUnet model is then applied to monitor the distribution of green tide on the beach and in the nearshore water; the results suggest that the VGGUnet model can accurately extract green tide features while discarding other confusing features. By using the Super Dove and UAV images, green tide beaching events can be accurately monitored and are consistent with field investigations. From the perspective of near real-time green tide monitoring, high-resolution imagery combined with deep learning is an effective approach. The findings pave the way for monitoring and tracking green tides in coastal zones, as well as assisting in the prevention and control of green tide disasters.

Extensive production and application of magnetic minerals introduces significant amounts of magnetic wastes into the environment. Exposure to magnetic minerals could affect microbial community composition and geographic distribution. Here, we report that magnetic susceptibility is involved in determining bacterial α-diversity and community composition in surface sediment across the Bohai Sea by high-throughput sequencing analysis of the 16S rRNA gene. The results showed that environmental factors (explained 9.80%) played a larger role than spatial variables (explained 6.72%) in conditioning the bacterial community composition. Exposure to a magnetite center may shape the geographical distribution of five dissimilatory iron reducing bacteria. The microbial iron reduction ability and electroactive activity in sediment close to a magnetite center are stronger than those far away. Our study provides a novel understanding for the response of DIRB and electroactive bacteria to magnetic minerals exposure.