This paper explores and compares various segmentation techniques, including the K-means clustering technique, Fuzzy C-means (FCM) clustering algorithm, Self-Organizing Map (SOM), and Local Adaptive Thresholding (LAT) with mathematical morphological operations, applied to time series Sentinel-1A imagery during flood and post-flood scenarios. Synthetic Aperture Radar (SAR) imagery, with its diverse pixel intensities, presents a significant challenge in accurate segmentation, particularly for distinguishing foreground (water) from background (non-water) areas. The primary objective is to identify a robust segmentation technique that can enhance SAR image analysis performance. The findings consistently demonstrate that the FCM clustering algorithm outperforms other methods in terms of result quality and average execution time, highlighting its potential for improving SAR image analysis. This research plays a crucial role in advancing the field of SAR image segmentation, with practical applications in flood detection, change identification, target detection, map updating, and crop identification. The study focuses on a region in Uttar Pradesh, India, covering multiple districts, and utilizes multi-temporal SAR products acquired by Sentinel-1A in vertical transmit-vertical receive (VV) polarization between August 13 and September 9, 2017. These significant findings not only contribute to the scientific understanding of SAR image segmentation but also offer practical solutions for addressing critical challenges in various image-related tasks.