The Ganga-Brahmaputra basin is highly sensitive to the impacts of climate change and experiences recurrent flooding, which affects large agricultural areas and poses a high risk to the population. The present study is focused on the recent flood disaster in the Ganga-Brahmaputra basin, which mainly affected the regions of Bihar, West Bengal, and Assam in India and neighboring Bangladesh during July, August, and September 2020. Using the Sentinel-1A Synthetic Aperture Radar (SAR) data, the flood extent was derived in the Google Earth Engine (GEE) platform. The composite area under flood inundation for July–September was estimated to be 25,889.1 km² for Bangladesh, followed by Bihar (20,837 km²), West Bengal (17,307.1 km²), and Assam (13,460.1 km²). The Copernicus Global Land Cover dataset was used to extract the affected agricultural area and flood-affected settlement. Floods have caused adverse impacts on agricultural lands and settlements, affecting 23.68–28.47% and 5.66–9.15% of these areas, respectively. The Gridded Population of the World (GPW) population density and Global Human Settlement Layer (GHSL) population dataset were also employed to evaluate flood impacts, which revealed that 23.29 million of the population was affected by floods in the Ganga-Brahmaputra basin. The highest impacts of floods can be seen from the Bihar state, as people reside in the lower valley and near to the riverbank due to their dependency on river water. Similarly, the highest impact was from Bangladesh because of the high population density as well as the settlement density. The study provided a holistic spatial assessment of flood inundation in the region due to the combined impact of the Ganga-Brahmaputra River basin. The identification of highly flood-prone areas with an estimated impact on cropland and build-up will provide necessary information to decision-makers for flood risk reduction, mitigation activities, and management.

It is pre-requisite to conserve and protect the forest cover, therefore mapping of the forest distribution and monitoring of their temporal changes are also necessary. In the field of forestry, radar datasets have a high potential due to its ability to derive/extract information from the surface, sub-surface and even from the depth. The current work tries to utilize the capability of C-band radar datasets provided by Sentinel 1A/B mission to derive the required information for sensing the disturbances in the forest areas. Application of SAR or microwave remote sensing for forest disturbance mapping with dual-polarization is partially developed and have been attempted by limited researchers to process and interpret the derived results. Microwave datasets can map the areas with frequent cloud-cover due to its cloud penetrating capabilities in day-night operation mode. The present work tries to identify and locate the disturbances in forest areas to organize better understanding of detailed information for further analysis with the help of open archive microwave datasets incoherent to optical datasets.