###### Abstract  
Monitoring open water bodies accurately is important for assessing the role of ecosystem services in the context of human survival and climate change. There are many methods available for water body extraction based on remote sensing images, such as the normalized difference water index (NDWI), modified NDWI (MNDWI), and machine learning algorithms. Based on Landsat-8 remote sensing images, this study focuses on the effects of six machine learning algorithms and three threshold methods used to extract water bodies, evaluates the transfer performance of models applied to remote sensing images in different periods, and compares the differences among these models. The results are as follows. (1) Various algorithms require different numbers of samples to reach their optimal consequence. The logistic regression algorithm requires a minimum of 110 samples. As the number of samples increases, the order of the optimal model is support vector machine, neural network, random forest, decision tree, and XGBoost. (2) The accuracy evaluation performance of each machine learning on the test set cannot represent the local area performance. (3) When these models are directly applied to remote sensing images in different periods, the AUC indicators of each machine learning algorithm for three regions all show a significant decline, with a decrease range of 0.33-66.52%, and the differences among the different algorithm performances in the three areas are obvious. Generally, the decision tree algorithm has good transfer performance among the machine learning algorithms with area under curve (AUC) indexes of 0.790, 0.518, and 0.697 in the three areas, respectively, and the average value is 0.668. The Otsu threshold algorithm is the optimal among threshold methods, with AUC indexes of 0.970, 0.617, and 0.908 in the three regions respectively and an average AUC of 0.832.  
water extraction; modified normalized difference water index (MNDWI); remote sensing: machine learning algorithm +
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water bodies from multispectral images [11; 12; 13]. Using remote sensing images to monitor a water body is mainly based on spectral bands and each image's spatial feature, so the identification methods can be categorized into three types from different perspectives. (1) Water body index method: This method is based on the spectral curves of water bodies, and thresholds are utilized to effectively distinguish water bodies from the background [14]. Different water indexes have already been proposed in the past few decades. Specifically, in 1996, [PERSON] [15] introduced the normalized difference water index (NDWI) model to extract water bodies. However, this model is unable to distinguish between dark shadow and water bodies. To overcome the shortcomings of NDWI, in 2006, [PERSON] [16] proposed the modification of normalized difference water index (MNDWI) to enhance open water features in remotely sensed imagery, and this model has better results for urban water bodies extraction. The water body index method has the characteristics of high precision and low computational cost, which has been widely used in practical applications. In the last few decades, the MNDWI of Xu is one of the most widely used water indices for various fields, including surface water mapping, land use/cover change analyses, and ecological research [17; 18; 19; 20]. (2) Machine learning methods: These methods feature pixel-based pattern recognition analysis, mainly including supervised and unsupervised classification techniques. The supervised methods mainly include neural network [21; 22; 23; 24; 25], support vector machine (SVM) [26; 27; 28], logistic regression [29; 30], and random forest [31; 32; 33], and the unsupervised classification methods mainly include K-means clustering [34] and ISODATA clustering [35; 36] methods. The machine learning algorithm has been widely used in remote sensing water extraction due to its high accuracy.