Floods wreak havoc in the state of Assam in India every year; indeed, the year 2020 has been no different. Torrential monsoonal rain from May to September 2020 produced a severe flood that became catastrophic for Assam. Changes in the distributional patterns of monsoonal rainfall result in irregularities in the state of Assam’s tropical flood occurrence, duration, and intensity. This chapter assesses the flood situation in Assam using the Sentinel-1A and Sentinel-1B SAR images in combination with other ancillary datasets such as precipitation, ground-based river gauge data, and gridded population data. Seventy-five pre-flood and 106 post-flood images were processed within the Google Earth Engine platform for developing the flood model. Difference and pixel-by-pixel ratio change method are used to highlight the flood occurrence patterns and trends across various land use and landforms within the state. Seasonality of flood and its duration were the main concerns. Three long and heavy spells of rain triggered three distinct flood phases. The river Brahmaputra and its tributaries flowed above danger level for almost all the flood phases. In the second phase, all experienced the most noticeably awful flooding scenarios. The result suggests that Synthetic Aperture Radar (SAR) data is more effective for mapping floods from space than optical data because SAR data can capture images in any weather situation. The developed flood model indicates that around 12.27 lakh hectares (ha) of land were flooded, over 3650 villages were under water, and more than 20 lakh people were directly affected. This chapter will be helpful in accurately delineating the spatial extent of flood footprints and major hotspot areas and putting in place the right flood prevention actions for the affected regions of any part of the globe.