Satellites with low-to-medium spatial resolution face challenges in monitoring the early and receding stages of green tides, while those with high spatial resolution tend to reduce the monitoring frequency of such phenomena. This study aimed to observe the emergence, evolution, and migratory patterns of green tides. We integrated GF-1 and MODIS imagery to collaboratively monitor the green tide disaster in the Yellow Sea during 2021. Initially, a linear regression model was employed to adjust the green tide coverage area as captured using MODIS imagery. We jointly observed the distribution range, drift path, and coverage area of the green tide and analyzed the drift path in coordination with offshore wind field and flow field data. Furthermore, we investigated the influence of SST, SSS, and rainfall on the 2021 green tide outbreak. The correlations calculated between SST, SSS, and precipitation with the changes in the area of the green tide were 0.43, 0.76, and 0.48, respectively. Our findings indicate that the large-scale green tide outbreak in 2021 may be associated with several factors. An increase in SST and SSS during the initial phase of the green tide established the essential conditions, while substantial rainfall during its developmental stage provided favorable conditions. Notably, the SSS exhibited a close association with the outbreak of the green tide.

Under the influence of global warming, the problem of sea-level rise is becoming increasingly prominent. The northern part of the South China Sea (SCS) is low lying, with intense economic development, and densely populated. These characteristics make the region extremely sensitive to the consequences of rising sea levels. This study aims to reveal the trends of sea-level changes in the northern SCS and provide scientific insights into the potential flooding risks in low-lying areas. To achieve this, the Ensemble Empirical Mode Decomposition (EEMD) method is used to analyze the water level time series data from three tide gauges along the coast of Hong Kong. This analysis reveals the multidimensional change characteristics and response mechanisms of the sea level in the SCS. The findings reveal distinct seasonal, interannual, decadal, and interdecadal variations in sea-level changes. Furthermore, we explore the impact of the El Niño-Southern Oscillation (ENSO) on sea-level changes in the study area, finding a 6-month lagged correlation between the sea level and ENSO. Spatially, the rate of sea-level change is faster in nearshore areas than in the open ocean and higher in the northern regions than in the southern regions. The Multifractal Detrended Fluctuation Analysis (MF-DFA) method is employed to analyze the sea-level change time series, revealing long-range correlations and multifractal characteristics. In addition, we propose a sea-level prediction method that combines EEMD with Long Short-Term Memory (LSTM) neural networks and conducts empirical research on sea-level changes in the northern South China Sea. The results indicate that the EEMD-LSTM model outperforms the standalone LSTM model in terms of predictive accuracy, effectively eliminating noise from signals and providing a valuable reference. In summary, this research delves into the multiscale characteristics and influencing factors of sea-level changes in the northern SCS, proposing an improved sea-level prediction method that integrates EEMD and LSTM. The findings lay the groundwork for evaluating the risks of sea-level rise in low-lying regions of the northern SCS and inform future response strategies.