Timely inputs for spatial planning are essential to support decisions about preventive or damage controlling measures, including flood. Climate change predictions suggest more frequent floods in the future, implying a need for flood mapping. The objectives of the study were to evaluate the suitability of Sentinel-1 SAR data to map the extent of flood and to explore how land cover classification through different machine learning techniques and optical Sentinel-2 imagery can be applied as an emergency mapping tool. The Australian floods in March 2021 were used as a case study. Google Earth Engine was used to process and classify the flood extent and affected land cover types. Our study revealed the great suitability of Sentinel-1 SAR data for emergency mapping of flooded areas. Furthermore, land cover maps were produced using random forest (RD) and support vector machines (SVM) on optical Sentinel-2 Imagery. The presented workflow can be implemented in other parts of the world for the rapid assessment of flooded areas.