Yating Zhan

The Ulva prolifera green tide in the South Yellow Sea has erupted annually for many years, posing significant threats to coastal ecology, the economy, and society. While environmental factors are widely acknowledged as prerequisites for these outbreaks, the asynchrony and complex coupling between their variations and disaster events have challenged traditional studies that rely on instantaneous correlations to uncover the underlying dynamic mechanisms. This study focuses on the Ulva prolifera disaster in the South Yellow Sea, systematically analyzing its spatiotemporal distribution patterns, the temporal accumulation and lag effects of environmental factors, and the coupled driving mechanisms using the Floating Algae Index (FAI). The results indicate that: (1) The disaster shows significant interannual variability, with 2019 experiencing the most severe outbreak. Monthly, the disaster begins offshore of Jiangsu in May, moves northward and peaks in June, expands northward with reduced scale in July, and largely dissipates in August. Years with large-scale outbreaks exhibit higher distribution frequency and broader spatial extent. (2) Environmental factors demonstrate significant accumulation and lag effects on Ulva prolifera disasters, with a mixed temporal mode of both accumulation and lag effects being dominant. Temporal parameters vary across different factors—nutrients generally have longer lag times, while light and temperature factors show longer accumulation times. These parameters change dynamically across disaster stages and display a clear inshore–offshore gradient, with shorter effects in coastal areas and longer durations in offshore waters, revealing significant spatiotemporal heterogeneity in temporal response patterns. (3) The driving mechanism of Ulva prolifera disasters follows a “nutrient-dominated, temporally relayed” pattern. Nutrient accumulation (PO₄, NO₃, SI) from the previous autumn and winter serves as the decisive factor, explaining 86.8% of interannual variation in disaster scale and 56.1% of the variation in first outbreak timing. Light and heat conditions play a secondary modulating role. A clear temporal relay occurs through three distinct stages: the initial outbreak triggered by nutrients, the peak outbreak governed by light–temperature–nutrient synergy, and the system decline characterized by the dissipation of all driving forces. These findings provide a mechanistic basis for developing predictive models and targeted control strategies.

Remote sensing technology plays a crucial role in the rapid and wide-scale monitoring of water quality, which is of great significance for water pollution prevention and control. In this study, the downstream and nearshore areas of the Huaihe River Basin were selected as the study area. By utilizing spectral information from standard solution measurements in the laboratory and in situ water quality data matched with satellite spatiotemporal data, inversion models for total phosphorus (TP) and ammonia nitrogen (NH₃-N) water quality parameters were developed. The validation results using field measurements demonstrated that the inversion models performed well, with coefficients of determination (R²) of 0.7302 and 0.8024 and root mean square errors of 0.02614 mg/L and 0.0368 mg/L for total phosphorus and ammonia nitrogen, respectively. By applying the models to Sentinel-2 satellite images from 2022, the temporal and spatial distribution characteristics of total phosphorus and ammonia nitrogen concentrations in the study area were obtained. The ammonia nitrogen concentration ranged from 0.05 to 0.30 mg/L, while the total phosphorus concentration ranged from 0.10 to 0.40 mg/L. Overall, the distribution appeared to be stable. The southern region of the Guan River estuary showed slightly higher water quality parameter concentrations compared to the northern region, while the North Jiangsu Irrigation Main Canal estuary was affected by the dilution of river water, resulting in lower concentrations in the estuarine area.

Timely, accurate, and efficient extraction of aquaculture sea is important for the scientific and rational utilization of marine resources and protection of the marine environment. To improve the classification accuracy of remote sensing of aquaculture seas, this study proposes an automatic extraction method for aquaculture seas based on the improved SegNet model. This method adds a pyramid convolution module and a convolutional block attention module based on the SegNet network model, which can effectively increase the utilization ability of features and capture more global image information. Taking the Gaofen-1D image as an example, the effectiveness of the improved method was proven through ablation experiments on the two modules. The prediction results of the proposed method were compared with those of the U-Net, SegNet, and DenseNet models, as well as with those of the traditional support vector machine and random forest methods. The results showed that the improved model has a stronger generalization ability and higher extraction accuracy. The overall accuracy, mean intersection over union, and F1 score of the three test areas were 94.86%, 87.23%, and 96.59%, respectively. The accuracy of the method is significantly higher than those of the other methods, which proves the effectiveness of the method for the extraction of aquaculture seas and provides new technical support for automatic extraction of such areas.