Two automated approaches, including Bayesian probability thresholding and regression tree based methods were utilized to detect the surface water extent with training dataset from prior class probabilities of water and non-water from two datasets. First, prior water and non-water masks were classified using SRTM Water Body Dataset (SWBD) and long-term summarized Dynamic Surface Water Extent (DSWE) class probabilities. Then, fully automatic algorithms were developed to derive water probability and classify surface water extent using Sentinel-1 data. Results over three representative study regions, including the Delmarva Peninsula, Florida Everglades and Prairie Pothole regions, indicate that the automated algorithm is efficient in monitoring open water inundation extent, and detection of partial water extent is possible using Sentienl-1 SAR data.