Sentinel-1 SAR and Google Earth Engine-Based Flood Inundation Mapping and Land-Cover Impact Assessment in the Upper Brahmaputra Valley, Assam, India: A Case Study of the July 2026 Flood Event

Abstract

Flood inundation is a recurrent hazard in the Brahmaputra Valley of Assam, where intense monsoonal rainfall, dynamic river channels, low-gradient floodplains and extensive agricultural land combine to increase exposure. This study develops a reproducible Google Earth Engine (GEE)-based framework for mapping the July 2026 flood event across Sivasagar, Charaideo and Jorhat districts in the Upper Brahmaputra Valley using Sentinel-1 Ground Range Detected (GRD) synthetic aperture radar (SAR) imagery. The workflow uses a homogeneous Sentinel-1 subset, temporal compositing, backscatter-based flood extraction, permanent-water masking, spatial cleaning, and overlay with ESA WorldCover 2021 and elevation data. GEE is used to automate image filtering, compositing, raster classification and district-wise zonal statistics, thereby improving processing efficiency and reproducibility. The supplied analysis indicates that inundation was concentrated in low-lying riverine and agricultural landscapes, with Sivasagar showing the largest mapped flood footprint, followed by widespread inundation in northern Jorhat and comparatively fragmented inundation in Charaideo. Agricultural land and wetlands constituted the principal affected land-cover categories. The study is contextualized using Kalita's assessment of flood chronology, river behaviour, inundation and human response in Morigaon, which demonstrates the broader relevance of floodplain dynamics and community vulnerability in Assam. The present work extends this regional perspective through an event-specific SAR mapping workflow. Importantly, SAR-derived inundation represents observed flood extent at the acquisition period and should not be interpreted as flood probability, depth or long-term risk without additional hydrological and vulnerability modelling.

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