## 5 Discussion  
### Comparison with Other Aquaculture Maps  
The proposed approach in this study was compared with the methods proposed by [PERSON] et al. [42] and [PERSON] et al. [39]. The corresponding reference images were manually labeled. The maps are presented in Figure 12, and the confusion matrixes are presented in Tables 5-7. [PERSON] et al. [42] adopted a spectrum, and spatial and morphological features of 30 m resolution Landsat images to build a Random Forest classifier to implement an automatic extraction of large-scale aquacultures. [PERSON]'s et al. [39] extracted the water surface from Sentinel-2A images using multiple thresholds, described the water patches using geometric and spectral features, and finally applied a Random Forest classifier to classify inland aquaculture ponds. \begin{table}
\begin{tabular}{c c c c c} \hline
**[PERSON]’s Method** & **Aquaculture** & **Others** & **Classified** & **User’s Accuracy (\%)** \\ \hline Aquaculture. & 0.250 & 0.071 & 0.320 & 77.97 \\ Others & 0.014 & 0.666 & 0.680 & 97.94 \\ Reference & 0.264 & 0.736 & \(1\) & \\ \hline Producer’s accuracy (\%) & 94.70 & 90.42 & & \\  & Overall Accuracy = 91.55\%, Kappa = 0.796 & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of Xia’s method in typical sites [39]. \begin{table}
\begin{tabular}{c c c c c} \hline
**Quaculture** & **Others** & **Classified** & **User’s Accuracy (\%)** \\ \hline Aquaculture & 0.323 & 0.038 & 0.358 & 89.47 \\ Others & 0.067 & 0.572 & 0.642 & 89.51 \\ Reference & 0.390 & 0.610 & \(1\) & \\ \hline Producer’s accuracy (\%) & 82.82 & 93.77 & & \\  & Overall Accuracy = 89.5\%, Kappa = 0.776 & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of proposed approach using 1000 validation samples. \begin{table}
\begin{tabular}{c c c c c} \hline
**Duan’s Method** & **Aquaculture** & **Others** & **Classified** & **User’s Accuracy (\%)** \\ \hline Aquaculture & 0.231 & 0.168 & 0.400 & 57.92 \\ Others & 0.032 & 0.568 & 0.600 & 94.65 \\ Reference & 0.264 & 0.736 & \(1\) & \\ \hline Producer’s accuracy (\%) & 87.81 & 77.16 & & \\  & Overall Accuracy = 79.97\%, Kappa = 0.557 & & \\ \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of [PERSON]’s method in typical sites [42]. The map provided by [PERSON] et al. (2018) presented a general distribution of aquaculture ponds; however, almost all the details were missed (Figure 12b,j), for it was obtained using Landsat TM images with a spatial resolution of 30 m. Moreover, they conducted morphological closing and erosion operations post process, which further smoothed the details. [PERSON]'s method and the proposed approach in this study were applied to two typical regions, and their derived aquaculture maps were compared. [PERSON]'s (2018) and the proposed approach in this study produced similar aquaculture maps, and most of their details are clearer than [PERSON]'s map (Figure 12c,d,k,l) for Sentinel images of 10 m spatial resolution were applied and the post processing did not eliminate the details. The proposed method produced similar results to [PERSON]'s method (Figure 12c,d,k,l); however, less embankment pixels were misclassified. Therefore, the User's Accuracy of aquaculture areas (92.40%) was higher than that of [PERSON]'s method (77.97%). The main problem of the proposed method is that some isolated ponds were missed, resulting in a lower Producer's Accuracy (Table 7). However, the overall accuracy and Kappa coefficient indicated that the proposed method performed better over the tested areas. The proposed approach in this study achieved better performance than [PERSON]'s and [PERSON]'s methods, which might be explained by that: (1) the texture features from normalized difference spectral index images increased the distinguishability of aquaculture area from other objects; and (2) radar images were integrated with optical ones, as they are sensitive to the texture structure of aquaculture areas, resulting in further improvement for aquaculture mapping.