Floods are natural hazards triggered by intense rainfall and are particularly destructive in low-lying areas such as floodplains. In flood-prone regions, effective disaster management relies on prevention, monitoring, and emergency response strategies. In this context, remote sensing, especially Synthetic Aperture Radar (SAR), has become indispensable for flood mapping and monitoring due to its ability to acquire data under adverse weather conditions and persistent cloud cover. Multi-temporal SAR imagery processed into RGB composites allows rapid visualization of inundation patterns, while the Geographic Object-Based Image Analysis (GEOBIA) approach improves the classification of flooded areas through the integration of backscatter thresholds and terrain elevation data. This study investigates the spatial and temporal dynamics of flooding in the Lower Tubarão River Sub-basin (LTRSb), southern Brazil, following an extreme precipitation event that produced 260 mm of accumulated rainfall between May 24 and 25, 2019. The Sentinel-1B SAR images, acquired pre- and post-event, were used to map flooded areas with an overall classification accuracy of 88%. The results indicate that three days after the event, flooding covered 140 km² (29%) of the LTRSb, predominantly affecting agricultural (86.3 km²) and pasture areas (47.6 km²). The flooded extent decreased to 62 km² after 15 days and to 15 km² after two months, with agricultural land consistently accounting for 97% of the flooded area. Urbanized areas (≈1 km²) were also impacted, indicating significant risks to infrastructure and public health. These findings highlight the importance of SAR-based flood monitoring for risk assessment and disaster management in hydrographic basins.