Recurrent flooding has been prevalent in the south-east Asia, especially Indian sub-continent for centuries now and has been unprecedented in the last two decades, causing devastating effects on life and property. This has been as a result of increased frequency of anomalous precipitation by virtue of increased carbon emission and climate change effect. The Indo-Gangetic Brahmaputra plains has been the worst effected, causing damage every year on large scale in terms of loss of lives and property. The region of Assam, which lies on the bank of river Brahmaputra and Megha rivers, has led to enormous amount of water being led to the plains and cities as the flood plains gets captured every year. To prevent flooding and take precautionary measure to handle the crisis better, remote sensing technology can play a major role in quick and effective event response by providing operational information on potential flood damaged area and provide corresponding assistance for relief work. Sentinel-1, SAR sensor are especially suited to provide information on water inundation in case of flooding with temporal resolution of 6 days and spatial resolution varying from 10-30 meters, thus providing highly accurate and quick data on areas flooded. The analysis has been performed for Assam, dated from 4th April to 24 June, 2020 which has one of the worst floods the northern states have ever received. Google Earth Engine, a cloud computing platform provides access to petabytes of pre-processed satellite imagery, which expedites the process of analysis and helps in reproducing the analysis over multiple images simultaneously. C band, which the sentinel-1 satellite uses, has been used to detect floods. The analysis shows a large part of Assam inundated. VH Polarization is found to be better suited for Assam due to low vegetative area surrounding the riverine and plane terrain. Only part of Assam was analysed due to unavailability of data. Additionally, GEE does not pre-process for speckle effect and thus the filter is applied. A threshold of 2.5 cm is applied to classify it as flooded region or otherwise.