# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method
### Accuracy Assessment Method  
Green tide extraction based on RS is a pixel classification problem in image semantic segmentation, where the green tide elements are defined as positive samples (true value) and the background information as negative samples. The true value was the pixel that we manually interpreted as the green tide based on RS false-color images. For GF-1, we chose band 3 (_Red_), band 2 (_Green_), and band 1 (_Blue_), which correspond to R, G, and B, respectively. For Landsat, we chose band 4 (_Red_), band 5 (_NIR_), and band 4 (_Green_), respectively. For HJ-1 and HY-1C, we both chose band 3 (Red), band 4 (_NIR_), and band 2 (_Green_), respectively. For MODIS, we chose band 1 (_Red_), band 2 (_NIR_), and band 1, respectively. Moreover, the green tide true value pixels were shown as green ones in the image.  
The extraction results of all pixels can be classified into four categories (Table 1): true positive (TP) represents the number of correctly classified green tide pixels, true negative (TN) represents the number of correctly classified background pixels, false positive (FP) represents the number of background pixels misclassified as green tide pixels, and false negative (FN) represents the number of green tide pixels misclassified as background pixels. The true value was the pixel that we manually interpreted as the green tide; classification results for TP, FP, FN, and TN were shown in Figure 5 to descript the selection definition of true values. Green tide pixels are usually green in RS false-color images. The circular marks were green tide data points extracted by our method. The green circle represented TP, the pixels that were green in the image and determined by our method to be green tide pixels. The red circle represented FP that was not green in the image but also determined by our method to be green tide pixels. The orange pentagram was green in the image, but it was determined as a background pixel by our method, i.e., FN. TN were the pixels that were not marked, which means these were correctly  
To evaluate the accuracy of our method in extracting green tides from different RS data sources, three commonly used judgment indicators were selected: Precision, Recall, and F1-score. Precision was defined as the percentage of extracted green tide elements (all elements with a predicted value of 1) that were accurate, i.e., Precision = TP/(TP + FP). That was, Precision focused on the ability of green tide pixels to be correctly classified, which was biased to evaluate the percentage of the number of background pixels misclassified as green tide pixels (FP). Recall was defined as the percentage of green tide elements (all elements with a true value of 1) correctly extracted from the RS images, i.e., Recall = TP/(TP + FN). That was, Recall focused on the ability of green tide pixels not to be missed, which was biased to evaluate the percentage of the number of green tide pixels misclassified as background pixels (FN). F1-score was defined as the summed average of Precision and Recall, which was the balance point between the two, i.e., F1-score = 2 (Precision \(\times\) Recall)/(Precision + Recall). F1-score can comprehensively evaluate the ability of each data to find positive pixels (green tide pixels). Therefore, F1-score was taken as the final indicator to measure the accuracy; the higher the value of F1-score, the better the accuracy of the extraction effect.  
The annual average coefficient of determination, \(R^{2}\), as shown in Equation (3), was calculated to verify the fitting effect of the Gompertz model and the Logistic model.  
\[R^{2}=1-\frac{\sum\left(f_{i}-y_{i}\right)^{2}}{\sum\left(f_{i}-\overline{y} \right)^{2}} \tag{3}\]  
where \(f\) is the fitted value of the growth curve, \(y\) is the accumulative cover area (ACA) of the green tide for the corresponding date we input and the \(\overline{y}\) is the mean of the input data.