Remote sensing analysis demonstrates accurate cloud-independent flood detection using radar backscatter in river basin floodplains, indicating improved disaster assessment during storm events.
Flood mapping in tropical river basins is frequently constrained by cloud cover during major flood events. This study integrated Sentinel-1 Synthetic Aperture Radar (SAR) with Sentinel-2, CHIRPS, ERA5-Land, SRTM, JRC Global Surface Water, ESA WorldCover and GHSL data to characterise flood dynamics and exposure in the Lower Volta Basin, Ghana. Sentinel-1 VV and VH backscatter were used to map seasonal water, event-based temporary flooding, cumulative and recurrent inundation, and flood-period surface change. Threshold sensitivity, Otsu benchmarking and a 600-point Sentinel-1/Sentinel-2 comparison were used to assess classification performance. Event-composite temporary water covered 5.868 km², while the cumulative footprint reached 7.386 km², of which 4.487 km² (60.7%) was recurrently detected. A broader 2,032.146 km² backscatter-change zone indicated hydrological surface responses extending beyond directly detectable open water. Flooding was concentrated mainly in low-relief wetland and floodplain environments, with herbaceous wetland accounting for 62.08% of the cumulative land-cover overlay. The Sentinel-1/Sentinel-2 comparison achieved 95.833% overall agreement, a 95.697% F1-score, and κ = 0.917. However, this represents inter-sensor consistency rather than independent field validation. The results demonstrate the value of combining direct open-water detection, flood recurrence, surface-change analysis and exposure information for flood assessment in cloud-prone tropical basins.
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George Owusu Amoah (2026) studied this question.
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