# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 6 Conclusions  
For many years, the repeated green tide blooms in the Yellow Sea have not only posed a great threat to the coastal ecology but also caused huge economic losses along the Yellow Sea coast. Therefore, there is an urgent need to predict future blooms of the Yellow Sea green tide to guide appropriate measures to prevent and control these events. In this paper, we propose a semi-automated method for extracting data on the Yellow Sea green tide based on the _NDVI_, which greatly improved the efficiency of green tide extraction and enabled analysis of the events and monitoring of the green tide in real-time. In this study, we first compared the presentation of the green tide from various RS data sources (GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS). High-resolution RS images were shown to present the sporadic floating green algae more completely than low-resolution images, with the resolution being highly positively correlated with the completeness of representation of the details. To evaluate the accuracy of the newly proposed semi-automated green tide extraction method, Precision, Recall, and the composite index F1-score were used as indicators. For different satellites, the mean Precision, Recall, and F1-score were all over 98.0%, which indicated that the universality of this method is good. For different environments, the Recall varied little under different water depths, but it was significantly higher than that in muddy water areas (Zone C) and under thin cloud cover (C3). For the results of Precision, there was not much difference between different environments, and it was as high as 99.0%. F1-score were all above 90%, except when the data were covered by thin clouds. Overall, our method can be well adapted to different environments. To obtain earlier and more comprehensive green tide traceability results and promote the early prevention, control, and targeted management of the Yellow Sea green tide, we further advanced the tracing time to early May by using the combination of multi-source RS images, except for HY-1C, for which the tracing time was advanced to early April. Using \(k\)-means clustering analysis, the multi-year traceability results (i.e., the central location of the early-stage green tide) were divided into two categories: the Subei shoal along the Jiangsu coast and the southeastern Yellow Sea. To analyze and predict the future development of green tides, single-day data on the Yellow Sea green tide were extracted from 2008 to 2022, the ACA data of the green tide over time were fitted using Gompertz and Logistic growth curves, and the relationships between the start and dissipation percentiles and the kurtosis coefficients were obtained. Using the approximate nonlinear method and the extraction results from at least the three Rs images, the green tide growth curve for 2022 was fitted, and the approximate bloom extent was successfully predicted. Such predictions can provide a reference basis for green tide prevention and control measures. The green tide prediction results showed that compared with recent years, the predicted bloom duration in 2022 would be shorter and have a smaller ACA. That prediction was in line with the actual duration and bloom condition in 2022. Conceptualization, S.X. and T.Y. ; methodology, S.X. and T.Y. ; software, S.X. ; validation, S.X. ; formal analysis, S.X. and T.Y. ; investigation, J.X., X.P., W.S., J.Z. and Y.Y. ; resources, W.S. ; data curation, J.X. ; writing--original draft preparation, S.X. and T.Y. ; writing--review and editing, S.X. and T.Y. ; visualization, S.X. ; supervision, T.Y. ; project administration, T.Y. ; funding acquisition, T.Y., X.P., W.S. and J.Z. All authors have read and agreed to the published version of the manuscript.