The ubiquitousness of satellite imagery and powerful, computationally efficient Deep Learning frameworks have found profound use in the field of remote sensing. Augmented with easy access to abundant image data made available by different satellites such as LANDSAT and European Space Agency's Copernicus missions, deep learning has opened various avenues of research in monitoring the world's oceans, land, rivers, etc. One significant problem in this direction is the accurate identification and subsequent segmentation of surface-water in images in the microwave spectrum. Typically, standard image processing tools are used to segment the images which are time inefficient. However, in recent years, deep learning methods for semantic segmentation is the preferred choice given its high accuracy and ease of use. This paper proposes the use of deep-learning approaches such as U-Net to perform an efficient segmentation of river and land. Experimental results show that our approach achieves vastly superior performance on SAR images with pixel accuracy of 0.98 and F1 score of 0.99.