# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 4 Results
### Semi-Automated Green Tide Extraction
#### 4.1.2 Accuracy Assessment  
Accuracy Assessment for Different RS Data  
We assessed the accuracy of the different RS data in Table 3. As shown in Table 4, the Precision values of different satellites (GF-1, Landsat, HJ-1, HY-1C, and MODIS) were all up to 98.0%, the Recall values of different satellites were all higher than 96.0%, and all of the F1-score values can reach 97.0%. The average Precision, Recall, and F1-score values of all datasets were 99.6%, 98.3%, and 99.0%, respectively. Regardless of the other indicators (Precision and Recall), or the aggregative indicator F1-score used to evaluate the extraction ability of green tide, all of them were up to 97.0%. In conclusion, our method worked well for both high- and low-resolution RS images for green tide extraction, indicating that our extraction method can be applied to the images of most satellites. Accuracy Assessment for Different Environments  
We uniformly selected Landsat RS images with high spatial resolution, a large amount of data, and comprehensive coverage of the study area as datasets for accuracy assessment in different environments. Detailed information is shown in Table 5. In addition to the comparison of extraction accuracy in different water depth environments, we also included a group covered by thin clouds (C3). As shown in Figure 9, the minimum number of pixels needed for the accuracy assessment was around \(10^{4}\), and the optimal number of pixels was around \(10^{5}\)-\(10^{6}\). So the total number of pixels in Table 5 was nearly on the order of \(10^{5}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Satellite**} & **Precision** & **Recall** & **F1-Score** \\ \cline{2-4}  & \(\begin{array}{c}TP\\ \textbf{TP+FP}\end{array}\) & \(\begin{array}{c}TP\\ \textbf{TP+FN}\end{array}\) & \(\begin{array}{c}2\times\begin{array}{c}\textbf{Precision\times Recall}\\ \textbf{Precision+Recall}\end{array}\end{array}\) \\ \hline GF-1 & 99.8\% & 99.3\% & 99.6\% \\ Landsat & 99.9\% & 99.0\% & 99.5\% \\ HJ-1 & 99.9\% & 98.5\% & 99.2\% \\ HY-1C & 99.9\% & 98.3\% & 99.1\% \\ MODIS & 98.6\% & 96.6\% & 97.6\% \\ Mean & 99.6\% & 98.3\% & 99.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy evaluation of the extraction effect. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Zone**} & \multirow{2}{*}{**Serial Number**} & \multicolumn{2}{c}{**Corresponding RS**} & **Location of the** \\  & & **Image** & **Images** \\ \hline \multirow{6}{*}{(Depth of \(<\)\(-35\) m)} & A1 & Landsat 8 (4 June 2020) & \(\begin{array}{c}121.75^{\circ}-121.27^{\circ}\text{E},\\ 35.65^{\circ}-35.73^{\circ}\text{N}\text{N}\\ \text{120.53}^{\circ}\text{N}\text{20.63}^{\circ}\text{E},\\ 35.67^{\circ}\text{N}\text{20.57}^{\circ}\text{N}\\ 121.25^{\circ}-121.35^{\circ}\text{E},\\ 35.98^{\circ}\text{N}\text{20.66}^{\circ}\text{N}\end{array}\) \\ \hline \multirow{6}{*}{(Depth of \(\geq\)\(-35\) m)} & B1 & Landsat 8 (23 June 2021) & \(\begin{array}{c}121.68^{\circ}-121.77^{\circ}\text{E},\\ 34.95^{\circ}-35.03^{\circ}\text{N}\\ 121.93^{\circ}-122.03^{\circ}\text{E},\\ 34.51^{\circ}-34.59^{\circ}\text{N}\\ 121.67^{\circ}-121.77^{\circ}\text{E},\\ 34.58^{\circ}-34.66^{\circ}\text{N}\end{array}\) \\ \hline \multirow{6}{*}{(Muddy water area)} & C1 & Landsat 8 (22 May 2021) & \(\begin{array}{c}120.85^{\circ}-120.94^{\circ}\text{E},\\ 33.73^{\circ}-33.81^{\circ}\text{N}\\ 121.30^{\circ}-121.39^{\circ}\text{E},\\ 33.98^{\circ}-34.06^{\circ}\text{N}\\ 120.56^{\circ}-120.66^{\circ}\text{E},\\ 33.87^{\circ}-33.95^{\circ}\text{N}\end{array}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detailed information on RS images for accuracy assessment in different environments. The accuracy assessment results of the extraction effect in different environments are shown in Table 6. The Precision was not much different across different environments and was as high as 99.0%.