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Aiming at the problems of large spatial differences in the spectral characteristics of water bodies in the complex environment of arid zones and the poor applicability of water body extraction methods, based on the multispectral data of Sentinel-2 satellite, 10m spatial resolution multispectral images were reconstructed by super-resolution algorithm. The short-wave infrared (SWIR) reconstruction band and the near-infrared (NIR) reconstruction band were used as the feature bands for water body identification, on the basis of which the super-pixel segmentation algorithm was used to determine the water body image elements, and a total of 60 water body extraction methods were constructed based on 24 kinds of spectral indices, support vector machine (SVM), neural network (NN) and K-means. Overall accuracy (OA), precision, F1-score, Matthews correlation coefficient (MCC) and other water body extraction accuracy indicators were used as for comprehensive evaluation, to determine the best water body extraction method in the Heihe Basin. The Heihe Basin was taken as typical study area to determine the best water body extraction method in arid areas. The results showed that the improved normalized water body index method constructed based on Sentinel-2 green band (center wavelength of 560nm) and super-resolution reconstruction of the short-wave infrared band (center wavelength of 1.610nm) significantly enhanced the ability to identify the fine water bodies, shadows, and cloud elements in the arid zone during the extraction of the water body. The overall accuracy of water extraction was 99.81%, the accuracy was 92.04%, the F1-score was 88.02%, and the G-mean and Mathews correlation coefficient were both greater than 0.88, which was better than other methods. The research results can quickly and accurately extract water bodies in arid zones and provide theoretical support for the application field of water bodies in arid zones.