The state of Jammu and Kashmir in North India experienced one of the worst floods in the past 60 years, during the first week of September 2014. In the present study, multi-temporal synthetic aperture radar (SAR) satellite images acquired from Indian Remote Sensing (IRS) satellite RISAT-1 and Canadian satellite Radarsat-2 during the peak flood period (08th–23rd September 2014) are used for extraction of flood disaster footprints, mapping spatial and temporal dynamics of flood inundation and assessing the disaster impact. With the aid of pre- and post-flood satellite images, coupled with hydro-meteorological data, the unprecedented flood situation is analyzed. It is estimated that about 557 km² of the Kashmir Valley's geographical area was inundated. Bandipora, Pulwama, Srinagar, Baramulla and Budgam were the worst flood affected districts, having more than 50 km² of their area affected by flood waters. Of the total inundated area, about 80% of the area under agricultural activity was submerged, followed by built-up areas constituting about 12% of geographical area. About 22 lakh people in 287 villages were affected by floods. The flood waters persisted in the northern and central part of the valley for more than two weeks.

The future projections of climate change envisage a global increase in extreme precipitation events and subsequent flooding. The reliable and rapid flood maps are the critical parameters in preparing the disaster management plans. This study demonstrated an effective flood mapping framework using freely available multi-temporal Earth Observation (EO) images, including C-band Sentinel-1A & 1B Synthetic Aperture Radar (SAR) images and optical WorldView-3 images, for analyzing the 2018 flood event of Kerala, India. Two Change Detection (CD) techniques, i.e. Ratio Index (RI) and Normalized Change Index (NCI) combined with semi-automatic thresholding are implemented on temporal descending pass SAR images for flood identification. For ascending pass SAR images, the statistical-based thresholding method is implemented. The results indicate that combined use of ascending and descending pass SAR images contributed to a better understanding of flood conditions. It is also inferred that the use of a pre-flood image can enhance flood area estimation and helps in minimizing the overestimation errors. The results also found that NCI outperforms RI for Kerala flood event. Flood area extracted from these techniques is plotted against the Indian Meteorological Department (IMD) rainfall datasets, which showed a similar trend. Field photographs and optical images are used for validation purposes.

The aim of the present study is motivated to build an inundation library for a range of gauge heights, which can be used by decision-makers to anticipate the likely extent of inundation and provide quick response towards warning the habitation at threat. In the present study, two approaches for developing a series of static flood-inundation extent libraries for a range of potential flood levels using historical satellite images, gauge heights and digital elevation model (DEM) are demonstrated. First method is based on the geotagging of gauge height data with corresponding satellite observed inundation extent and the other method supplements the first method in the absence of adequate satellite data-sets by simulating inundation using gauge data and DEM for a range of gauge heights. Simulated inundation extents are validated with the satellite-derived reference inundation extents using spatial statistics, which measure the correspondence between the estimated and observed occurrence of events like probability of detection (POD), false-alarm ratio, and critical success index (CSI). A good correlation between the simulated inundation and satellite-derived inundation extents, with POD varying between 87% and 94%, CSI between 75% and 80% is observed.

During late September, 2019 Bihar was struggling with severe flooding problem, which otherwise is marked as a period of flood recession due to withdrawal of south-east monsoons. The present study assess the flood situation using Sentinel-1 SAR images and complements the understanding about the flood event using long term (2000-18) multi-temporal space based flood sensitive proxy indicators like precipitation (GPM), soil moisture (AMSR-2), vegetation condition (MODIS) together with ground based river gauge (CWC) data. The study reveals that in 2019 during the 39^th week of the year (late September) the central and eastern parts of Bihar witnessed heavy precipitation (176 percent higher than average), leading to enhanced soil moisture build up (19 percent higher than average) and consequently triggering severe flooding. River Ganga was observed to be flowing above danger level for almost two weeks. Due to the prolonged submergence by floodwaters a significant drop was observed in the NDVI and EVI values of about 13.7 and 11.1 percent respectively from the normal. About 8.36 lakh ha area was observed to be inundated, impacting about 9.26 million population. Patna followed by Bhagalpur were the two worst affected districts with almost 30% and 36% of districts geographical area being flooded.

The present study focuses on the unprecedented flood situation captured through multi-temporal satellite images, witnessed along the Ganga River in Uttar Pradesh during September 2010. At three gauge stations (Kannauj, Ankinghat and Kanpur), river water level exceeded the previous high-flood level attained by river more than a decade ago. The present communication with the aid of pre- and post-flood satellite images, coupled with hydrological (river water level) and meteorological (rainfall) data, explains about the unprecedented flood situation. In the latter part of the study, a novel and cost-effective method for building a library of flood inundation extents based on historical satellite data analysis and tagging the inundation layer with observed water level is demonstrated. During flood season, based on the forecasted water level, the library can be accessed to fetch the spatial inundation layer corresponding to the forecasted stage and anticipate in advance, likely spatial inundation pattern and submergence of villages and hence in alerting the habitation at risk. This method can be helpful in anticipating the areas to be affected in situations where satellite images cannot be effectively utilized due to cloud cover and also for providing information about the areas being partially covered in satellite data.