## 1 Introduction
Nevertheless, NDWI is unable to completely remove the interference caused by gloomy metropolitan areas that are combined with bodies of water. In order to solve that problem, a modified version of the NDWI [24] was suggested. With this modification, the Shortwave Infrared(SWIR) band is employed instead of the Near-Infrared (NIR) band used in the NDWI. This change allows for the purpose of extracting water surfaces while simultaneously reducing the interference caused by developed areas. It is significant to note that several urban features (such as shadows, roadways, and other dark objects) make MNDWI and NDWI unsuitable for precisely detecting water bodies in urban high-resolution images that also produce strong signals in these two indices [25; 26]. Ref. [27] presented the Automated Water AWEI, a novel method that offers a consistently reliable threshold value for water extraction. Some studies in which these indexes are used are examined; the demarcation of the boundaries of the glacial lake in the Tibetan Plateau by [20] using the Landsat 8 satellite image and the MNDWI. In another study [1], annual lake maps of the Mongolian plateau were extracted from water mass maps created using all available Landsat images with the water mass mapping algorithm using vegetation and water indices (MNDWI, EVI, NDVI). In another study, lakes and reservoirs were determined completely automatically by combining Landsat 8 OLI data, GEE, AWEI, and for the period 2014-2018 in New Zealand [21]. Each pixel in the generated indices is assigned a gray value. Different thresholding techniques are employed to separate water surfaces from other elements based on their respective gray values. There are various thresholding methods, including manual thresholding and automatic thresholding methods like the Otsu threshold method and Canny edge detection [28; 29]. The Canny edge detection and Otsu method is a fusion of the Otsu algorithm and the Canny edge detector. The Canny edge detector and the Otsu algorithm are frequently employed in the tasks of edge detection and image segmentation. The Otsu threshold algorithm determines the threshold by identifying the highest variance across classes in the data histogram [30; 31], whereas the Canny detector identifies edge regions by applying Gaussian filtering and calculating the gradient magnitude between neighboring pixels [32]. The Otsu and Canny edge detection algorithm utilizes the Otsu threshold to provide automatic threshold segmentation, thus addressing the issue of subjective dual threshold settings in Canny edge detection [33; 34]. The Canny algorithm is an edge detection algorithm used to clearly extract the boundaries of the object by determining the appropriate parameters. The Otsu threshold detection algorithm calculates the high threshold value that is important for the Canny algorithm and then uses this threshold value to detect the edge of the object in the Canny algorithm. The Otsu algorithm is used to select the threshold value to be used in the Canny algorithm, and in this way, the edge extraction effect of the Canny algorithm can be increased [35]. Machine learning-based image classification methods effectively extract water surface areas of water bodies from remote sensing images [2; 36; 37]. With the ongoing advancements in remote sensing technology and the accessibility of data with high resolution sets, valuable data and information must be extracted utilizing RS images. Commonly used methods for the classification of images in RS and related fields include RF, SVM, and CART [38; 39; 40; 41; 42; 43; 44]. Machine learning algorithms utilize training samples for image classification, relying on an important amount of training data [45]. The Otsu technique addresses the limitation of a uniform threshold in conventional algorithms [46]. However, it encounters threshold anomalies when there is a wide range of water pixels in the image.