Multi-Temporal Flood Assessment of Mahad, Maharashtra Hybrid Method of Using Sentinel-1 SAR and NDWI Technique on Google Earth Engine

Department of Civil Engineering, Dr. Babasaheb Ambedkar Technological University, Maharashtra State, 402103, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Floods are an acute environmental hazard and affect pliable geographic areas in multiple portions of India, low-lying urban centres like Mahad in the Raigad district of Maharashtra are even more susceptible to flooding. This study presents a multi-temporal application of a hybrid flood detection approach that combines information from the Sentinel-1 Satellite (Synthetic Aperture Radar (SAR)) and Sentinel-2 and Normalized Difference Water Index (NDWI) percentages to assess flood inundation status from 2020 to 2024. While the individual techniques are well established in flood mapping literature, the contribution of this work lies in their consistent integration and repeated application over multiple years using Google Earth Engine (GEE), a hybrid approach to flood detection, particularly during cloud cover of the monsoon season, is very feasible. The hybrid approach employs SAR backscatter ratio thresholding to identify potential flood pixels, followed by NDWI-based filtering to reduce misclassification in urban and vegetated areas, with results visually cross-referenced using false colour composite (FCC) imagery. The combined SAR–NDWI results enabled improved spatial consistency in identifying flood-prone zones. The hybrid model appeared to localise 'true' flood zones effectively. From the results, a significant flood event occurred in 2021 with an inundated area of 2,887.93 hectares based on both SAR and NDWI data, while in 2023 only a small interactive flooding area was noted after dredging. Overall, the study demonstrates a repeatable and scalable hybrid workflow that supports long-term flood assessment and regional flood.