In May 2023, Rwanda experienced extensive flooding with deadly outcomes. Providing accurate and reliable emergency mapping to provide authorities with decision-making support for sustainable development for future disaster management is crucial. In this study, we aim to assess the flood extent and damage in the Lower Nyabarongo catchment in Rwanda using Google Earth Engine (GEE), Sentinel-1 Synthetic Aperture Radar (SAR) data and Sentinel-2 MultiSpectral Instrument (MSI) data with an in-situ approach for validation. An image differencing method with automatic thresholding based on Otsu’s method was used for change detection in flood mapping. For land cover and land use (LULC), a supervised Random Forest (RF) classification was used. The results revealed an extensive flooded area on the 3rd of May 2023 that based on the accuracy assessment showed high agreement with the UNOSAT reference data of the same event. VV polarization performed marginally better than VH polarization. The LULC, however, showed varying accuracy across different classes with challenges posed by the spectral similarities between certain land cover types. The integration of field visits and observations in combination with open-source intelligence (OSINT) further validated our findings. Using OSINT in combination with open source software GEE and open data assets such as Sentinel, especially showed the potential for use of this approach in data-scarce areas with limited monitoring infrastructure. Creating an accurate flood map and a reliable LULC free of charge and with only open available resources, with limited required knowledge of geospatial techniques.