## 6 Conclusions  
Based on Landsat-8 images, decision tree, logistic regression, random forest, neural network, support vector machine, and XGBoost algorithms are used to extract water bodies. Firstly, the effect of each machine learning algorithm on the test set is discussed. Secondly, each machine learning algorithm is applied to three different local areas, and the consistency between the accuracy of each machine learning algorithm on the test set and the accuracy of the local area is evaluated. Finally, each machine learning algorithm is applied to remote sensing images in different periods, the model transfer performance of each machine learning algorithm is examined, and three threshold methods are compared. The following conclusions are drawn:  
(1) There are great differences in the numbers of samples required for various algorithms to reach their optimal. The logistic regression algorithm requires a minimum number of samples, about 110. The SVM algorithm has the best performance when the number of samples reaches 150. As the number of samples increases, the optimal order of the model is neural network, random forest, decision tree, and XGBoost. (2) The accuracy evaluation effect of each machine learning on the test set cannot represent the effect on the local area, because the surface complexity is not same in the three local areas. In Area1 with a single surface type, its AUC range is 0.982-0.985; in Area2 with complex surface environment (numerous vegetation and mountain shadow), its AUC range is 0.789-0.973; in Area3 with wide water distribution, its AUC range is 0.923-0.941 in an urban built-up area. (3) When the models are directly applied to remote sensing images in different periods, the model accuracy is greatly reduced, the AUC indicators of each machine learning algorithm for three regions all show a significant decline, with a decreasing range of 0.33-66.52%. In general, among the machine learning algorithms, the decision tree algorithm has good transfer performance, with an average AUC of 0.668, and the AUC indexes in the three regions are 0.790, 0.518, and 0.697 respectively. Among the threshold methods, the Otsu threshold algorithm is the optimal, with an average AUC of 0.832 and AUC indexes in the three regions are 0.970, 0.617, and 0.908, respectively. (4) Owing to the complex distribution of ground objects and many influential factors in the remote sensing image classification, it is difficult to collect small and dispersed water bodies in this research. This limits the performances of these models in the environment with many hill shadows and complex ground objects. The accuracy of these models needs to be further improved; more samples should be collected from images over different areas and periods to train the models in the future. The following are available online at [[https://www.mdpi.com/article/10.3390/app112110062/s1](https://www.mdpi.com/article/10.3390/app112110062/s1)]([https://www.mdpi.com/article/10.3390/app112110062/s1](https://www.mdpi.com/article/10.3390/app112110062/s1)), Detailed descriptions of Figure S1: Classification results of each algorithm in Area2 on October 20; Figure S3: Classification results of each algorithm in Area3 on October 20; Figure S4: Classification results of each algorithm in Area1 on October 4; Figure S5: Classification results of each algorithm in Area2 on October 4; Figure S6: Classification results of each algorithm in Area3 on October 4. Supervision, A.L. ; Writing--original draft, M.F. ; Writing--review and editing, M.F., G.Q., Y.X. and H.W. All authors have read and agreed to the published version of the manuscript. The work is supported by the Joint Funds of National Natural Science Foundation of China (Grant no. U1704125). Not applicable.