## 5 Discussion  
The single-band threshold method is a good method to distinguish surface water from land. Through manual visual interpretation, this method could roughly extract the surface water in the study area. However, most of the small tributaries and discrete surface waters on the southwest and southeast shores of the Poyang Lake area were not successfully separated; only some of the rougher rivers were extracted. Thus, there are still some imperfections at the extraction of river's edge. The water index extraction method can be used to extract the basic shape of the main body of Poyang Lake. However, the NDWI is sensitive to sediment, and the area of Poyang Lake and a large number of rivers are mixed with a great deal of sediment. Moreover, the water index method only extracts wider rivers. The decision tree classification method can extract the general water bodies and rivers of Poyang Lake. However, lakes with a large amount of suspended sediment and mixed water cannot be distinguished by decision tree classification. The high concentration suspended sediment water body was incorrectly classified as a non-water body. Moreover, this method could not distinguish the shallow bank at the edge of the lake or extract the small tributary water body. At the same time, the knowledge-based decision tree method requires that researchers have a good understanding of the image features in the early stage when constructing the decision tree. The precision of the classification results of the decision tree was determined by sample selection. Images in different regions are needed to build special decision trees, so their utility is low. There were many misclassifications in the supervised classifications for the shadowed part of the western mountains. At the same time, the surface water extraction results were also not perfect for the area of the lake edge with a complex shape. In addition, supervisory classification methods require prior field investigations or prior knowledge and are complex to operate. The unsupervised classification results were not accurately extracted from the shoals on the lake boundary. At the same time, for the junction between the river and the building, part of the building was divided into land surface waters. The unsupervised classification does not need to be supervised during the selection and implementation of classifiers. However, after the implementation of classifiers, it is necessary to judge the results according to the classification characteristics and then merge the classifications, which requires accurate discrimination. The SMDPO extracted not only the basic surface water of Poyang Lake but also some relatively small tributaries. The general directions of the rivers were also clearly indicated, and some discrete lakes were successfully distinguished. However, the land surface water in the building area was relatively confused. The results of SMDPSO are better than those of the 5 previous methods. On the basis of SMDPSO, the SMDPO+LFE method extracted some relatively small tributary waters. In addition, some small tributaries and lake water that take up only one pixel were extracted. Figure 16: The number of iterations and the relationship between the OA and Kappa coefficients. According to the stability study of SMDPSO+LFE, the spectral reflectance of vegetation, cloud, and ice is quite different from the spectral value of standard water, so the water probability value is relatively low. This method can accurately distinguish vegetation, clouds, and ice, as well as thin clouds. The SMDPSO+LFE method was used to extract the images of the Poyang Lake research area, the Magdalena River in Colombia, and the coastal lake in Mexico. This method was shown to be highly sensitive to shoal and silt, as it could correctly distinguish between shoal, silt, and surface water. At the same time, this method offers good feedback for small tributaries and lakes with a pixel width of less than two and can effectively extract small tributaries, small lakes, and small puddles.