## 4 Results
### Aquaculture Map and Accuracy Assessment  
With optimal feature combination, threshold, and window size, the final aquaculture maps were obtained (Figure 11). The aquacultures in the Pearl River Basin (Guangdong) had a total area of 1445.91 km\({}^{2}\) in 2020. The inland aquacultures were concentrated in the coast of bays, including Zhenhai Bay (Figure 11a), Yamen watercourse (Figure 11b), Pearl River Estuary (Figure 11g) and Niwammen watercourse (Figure 11h). The area along the coast had the most concentrated aquacultures, which are mainly enclosed sea aquacultures. Moreover, a great deal of inland aquacultures were widely distributed in the inland river network.  
Figure 8: Overall accuracies and Kappa coefficients using different thresholds.  
Figure 9: Typical classification maps using different thresholds: (**a**) Sentinel-2A MSI image, (**b**) \(T=-0.2\), (**c**) \(T=-0.1\), (**d**) \(T=0\), (**e**) \(T=0.1\), and (**f**) \(T=0.2\).  
Marine aquacultures accounted for 22.8% of the total area of aquaculture in the Pearl River Basin (Guangdong), and they were concentrated in the bays along the coast. The raft cultures were mainly distributed in Yamen watercourse (Figure 11b) and Baisha Bay (Figure 11d), and cage culture in Shenzhen Bay (Figure 11d) and and Baisha lake (Figure 11e). The aquaculture areas of the major administrative regions in the Pearl River Basin (Guangdong) in 2020 are shown in Figure 11h. Jiangmen City had the largest area of aquaculture with about 443.56 km\({}^{2}\), which accounts for about 30.7% of the total aquaculture area in the study area. The second was Zhuhai City, with an aquaculture area of 251.52 km\({}^{2}\), accounting for 17.4% of the total aquaculture area.  
Figure 11: Aquaculture maps in the Pearl River Basin (Guangdong) in 2020: (**a**) Zhenhai Bay, (**b**)Yamen watercourse, (**c**) Shenzhen Bay, (**d**) Baisha Bay, (**e**) Baisha lake, (**f**) Histogram of aquaculture area in major administrative regions, (**g**) Pearl River Estuary, and (**h**) Niwammen watercourse.  
Figure 10: Maximum sum average texture of VH using different window sizes and the aquaculture maps: (**a**) Sentinel-2A false color images, (**b–d**) image features with a window size of 5 \(\times\) 5 pixels, 15 \(\times\) 15 pixels and 30 \(\times\) 30 pixels, and (**e–g**) aquaculture maps.  
The validation of aquaculture area map with the validation samples held an overall accuracy of 89.5% with a kappa coefficient of 0.776, a Producer's Accuracy of 82.82%, and a User's Accuracy of 89.47% (Table 4). Some commission and omissions errors for the proposed approach were still observed. Using a window of 150 \(\times\) 150 m, some water bodies and tidal flats adjacent to aquaculture were misclassified as aquaculture because of their similar edge texture values. In addition, the aquacultures with high sediment content have similar spectral characteristics with non-aqueous bodies, and they were frequently misclassified.  
The high user's and overall accuracies indicated the satisfactory performance of the proposed approach. However, the producer's accuracy was slightly lower than user's accuracy, for some mixed pixels containing parts of ponds were classified as water bodies at the stage of NDWI thresholding. This problem could be overcome by using sub-pixel mapping or high spatial resolution images.