Satellite-derived flood inundation over South Sudan: a multi-sensor assessment

South Sudan has experienced recurrent and widespread flooding, particularly following the extreme events of 2019–2020, resulting in extensive inundation and population displacement. In such data-scarce and hard-to-reach environments, satellite remote sensing provides the primary means of monitoring flood dynamics. Although a range of sensors, including optical, synthetic aperture radar, and passive microwave, are used for flood monitoring, forecasting, and risk assessment, no single sensor performs consistently across all land cover and hydrological conditions. These limitations are pronounced in regions with extensive wetlands, and persistent cloud cover.This study presents a comparative assessment of widely used satellite-derived flood inundation products over South Sudan for 2019–2024, focusing on their spatiotemporal consistency and sensitivity to environmental conditions. Flood extent datasets derived from MODIS LST, VIIRS, CYGNSS, Sentinel-1, and SWOT were processed to generate monthly inundation and harmonized to a common spatial resolution of 1 km. The products were evaluated across wet and dry seasons and under major land cover classes, including open water, wetlands, vegetation, croplands, rangelands, and built-up areas.All products capture the seasonal flooding pattern, with substantial differences across particular areas and land cover types. MODIS and CYGNSS detect larger inundation extents and exhibit greater sensitivity in densely vegetated floodplains, while Sentinel-1 and VIIRS perform more consistently over open water but underestimate flooding in vegetated areas and exhibit higher false detections over dry and burned surfaces. These findings highlight sensor-specific limitations affecting flood monitoring accuracy and indicate that product selection should be aligned to land cover and seasonal conditions.