Inland water bodies play a crucial role in both ecological and sociological contexts. They serve as significant sources of freshwater, meeting various agricultural, domestic, and industrial water demands. The distribution of these water bodies can change over time due to natural or human-induced factors. Monitoring the extent of surface water is vital for understanding extreme events such as floods and droughts. The availability of dense temporal Earth observation data from sensors like Landsat and Sentinel, coupled with advancements in cloud computing, has enabled the analysis of surface water extent over extended periods. In this study, an automated thresholding approach was applied within the Google Earth Engine platform to extract the surface water extent of the Chembarambakkam reservoir in Chennai. Sentinel-2 data spanning from 2016 to 2023 were used to derive two key indices, namely the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). These indices were then thresholded to determine the presence of water. The performance of two different thresholding techniques, namely Deterministic thresholding and Otsu, a histogram-based thresholding method, was compared to achieve better results. To enhance the accuracy of the deterministic technique, an iterative method was implemented that averaged the mean values of water and non-water areas to establish a new threshold. While the threshold values were generally similar for both techniques, the Otsu algorithm outperformed the deterministic technique in water classification. Furthermore, a frequency of water occurrence image was obtained using the temporal images, providing insights into the surface dynamism of the reservoir. This information is valuable for understanding the temporal changes in the reservoir's water presence. Overall, this study highlights the significance of surface water monitoring using remote sensing and cloud computing techniques. The comparative analysis of thresholding methods emphasizes the superior performance of the Otsu algorithm in classifying water. The frequency of water occurrence image adds an additional layer of understanding regarding the reservoir's surface dynamics.