# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 4 Results
### Green Tide Growth Curve  
We extracted single-day data on the Yellow Sea green tide from 2008 to 2021 based on cloud-free/low-cloud multi-source RS images to effectively monitor the green tide in real time and analyze its development each year in terms of the annual green tide bloom, development duration, and impact range and route. We chose the commonly used growth curve models--Gompertz [37] and Logistic [38]--to fit the relationship between the ACA and the time of the green tide. The cover area of the green tide (2008-2021) was extracted from RS images, and an approximate nonlinear method (see Table 2) was used to fit the two curve models separately (Figure 10). The input was the ACA extracted from remote sensing images without cloud or with thin cloud from 2008 to 2021. The output was the formula of Gompertz and the Logistic curve fitted by the approximate nonlinear method described in Table 2. The \(R^{2}\) (see Equation (3)) was calculated to verify the fitting effect and it was respectively labeled in Figure 10. The mean \(R^{2}\) of Gompertz model was 0.9740 and was slightly better than that of the Logistic model (0.9720). The two curves fit similarly to the annual data of green tide, so the best of the two models was chosen to fit the growth curve trends of the green tide cover area for each year. Combined with the approximate nonlinear method, the data from more than three scenes extracted by real-time monitoring could fit the growth curve of the current year and predict the general bloom situation, providing a reference for the next steps toward green tide prevention and control. The larger the amount of basic data, the higher the accuracy of the fitted growth curve. For any type of optical sensor, _NDVI_ is more or less affected by atmospheric aerosol, muddy water, and shallow water layer. Similarly, the change of environmental factors in satellite images will lead to the uncertainty of extraction results. In addition, the ACA depends on surface currents and the number of images. Therefore, an uncertainty for the growth curve was estimated, as shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Satellite** & **GF-1** & **Landsat** & **HJ-1** & **HY-1C** & **MODIS** \\ \hline GF-1 & 100\% & 67.6\% & 89.7\% & 65.4\% & 59.2\% \\ Landsat & 52.1\% & 100\% & 67.2\% & 96.7\% & 87.5\% \\ HJ-1 & 88.6\% & 75.3\% & 100\% & 72.8\% & 65.9\% \\ HY-1C & 47.1\% & 96.6\% & 62.7\% & 100\% & 90.5\% \\ MODIS & 31.0\% & 85.7\% & 48.3\% & 89.5\% & 100\% \\ \hline \end{tabular}
\end{table} 
Table 7: Area consistency coefficient of green tide extraction results. After determining the growth curve of the green tide cover area versus time, the duration of development of the green tide was estimated, and the approximate start and dissipation times of the green tide were projected. We found that the first-order derivative of the growth curve, i.e., the rate of increase in the green tide cover area, conformed to the normal distribution, so a Gaussian fit to the first-order derivative was performed to obtain the curve equation \(y^{\prime}\) (see Figure 11). We calculated the proportion area of the first-order derivative of the growth curve corresponding to the start and dissipation times of the green tide according to the growth curve area proportion calculation method described in Section 3.8. The start and dissipation times of the green tide event were determined according to the China Marine Disaster  
Figure 11: First derivatives of growth curve and its Gaussian fitting curve. Figure 10: Growth curve fitting effect with 95% confidence interval. Bulletin and the Jiangsu Marine Disaster Bulletin, as shown in Table 8. For periods that were not accurate to days, the 15 th day was considered as the middle, the 20 th day as mid-late, and the 25 th day as late.