High-resolution flood monitoring based on CYGNSS surface reflectivity: a case study of Sofala Province, Mozambique

Abstract

Extreme precipitation associated with tropical cyclones frequently triggers severe flooding in low-lying coastal regions, highlighting the importance of timely and reliable flood monitoring. Conventional remote sensing approaches are often constrained by limited temporal resolution and cloud interference. To address these limitations, this study investigates the application of Cyclone Global Navigation Satellite System (CYGNSS) Level 1 observations for flood detection in Sofala Province, Mozambique, which was severely affected by Cyclone Idai in March 2019. Surface reflectivity (SR) is derived from delay Doppler map (DDM) measurements and employed to characterize surface water conditions. A threshold-based criterion is then applied to delineate inundated areas. The results are evaluated using Soil Moisture Active Passive (SMAP) soil moisture data, together with precipitation records and time series analysis. The findings indicate that SR responds sensitively to variations in surface water, enabling effective identification of flood extent. A strong consistency is observed between CYGNSS-derived results and SMAP observations, while CYGNSS additionally provides improved temporal resolution and all-weather monitoring capability. Time series analysis further reveals a clear linkage among precipitation, soil moisture dynamics, and flood evolution. Overall, this study demonstrates the effectiveness of GNSS-R observations for rapid flood monitoring, particularly in data-scarce regions.