## 3 Results and Discussion
Water features are enhanced due to their positive values, while soil and vegetation generally exhibit zero or negative values, indicating their suppression [23]. In [24], it is suggested to replace the NIR band with the SWIR band in the NDWI to form a new band known as the MNDWI band. This modification addresses the limitations of NDWI in effectively suppressing the signal from structured surfaces and accurately distinguishing them from water pixels when using a threshold of 0. Specific factors highlight the significance of individual bands as characteristics. Water displays high reflectance in the visible spectrum while displaying low reflectance in the NIR and SWIR wavelengths. Within the NIR band, water exhibits lower light reflection than the red band. With the exception of pixels that represent unclear water, water has the ability to absorb NIR and SWIR radiation. This leads to a noticeable difference in color intensity between the green and NIR bands, as well as the green and SWIR bands [41]. Although the MNDWI offers a high level of precision in separating objects, it is not adequate for accurately identifying water bodies in this study. However, as was the main purpose of formulating AWEI in which the B7 band was included, the study found that the distinction between water and non-water pixels was optimized by employing band differentiation, addition, and different coefficients [27]. These reflectance variations are used by spectral indices, including NDWI, MNDWI, and AWEI, to distinguish between pixels that contain water and non-water. When terrain pixels exhibit a pronounced NDWI response, which may occur in regions with high reflectivity or shadow, using supplementary data such as the SWIR band (B7) improves the outcomes. This improvement can be observed visually in Figure 6 of the classification results [27; 77]. Upon visual inspection of Figure 6, the lake surface boundaries obtained by the MNDWI and CART, RF, and SVM methods are determined, but there is a lot of noise in the lake. It is seen that accuracy assessment parameters also support this situation. The accuracy of classifying water surfaces varies depending on several criteria, including the combination of bands used in the satellite image, the information required including the date when the satellite image was obtained, the atmospheric conditions prevailing on that particular day, the spatial resolutions of the images, and the methods employed. Lake surface boundaries were successfully obtained with NDWI, MNDWI, and AWEI indices using CART, RF, and SVM classification methods for Landsat-8 (OLI) images; they have a spatial resolution of 30 m. In general, the accuracy of SVM classification processes utilized to the AWEI combination is more successful. Supporting the study conducted by [67], it was noted that the SVM method showed a greater kappa value than the CART and RF methods. Figure 6: _Cont_. In general, when Lake Van is compared to Lake Urmia, which is approximately 150 km away from it, Lake Van exhibits climatic, orographic, and geographical characteristics comparable to Urmia, although it has lower salinity and greater depth. Despite the relatively short distance between them, water volume and surface changes have varied considerably during the last two decades. The proportions and configuration of Van Lake have remained unaltered. Nevertheless, Urmia Lake saw a substantial alteration, wherein its southern section nearly disappeared, leading to a decrease of approximately 60% from its initial size in the early 2000s. The primary factor contributing to the significant decrease in water levels in the southern and eastern perimeters of the lake is the shallow depth and substantial decrease in water discharge from the Zarineh River subsequent to the construction of the dam. The significant differences in land use and land cover (LULC) changes observed in the two lakes indicate that the drying up of Urmia cannot be solely attributable to climatic changes in the Middle East [81].