Flood Monitoring by Integrating Normalized Difference Flood Index and Probability Distribution of Water Bodies

Climate change has caused an increase in the frequency of flood events. Rapid and accurate mapping is essential for disaster monitoring risk assessment. The normalized difference index (NDFI) a detection method with characteristics efficient processing less manual intervention, which can quickly obtain information. However, NDFI would misclassify some permanent water bodies lakes rivers into floods. We presented framework by combining calculated from synthetic aperture radar images summer (SPWB) exclusion layer derived optical remote sensing surface reflectance data, abbreviated as NDFI-SPWB. This was further verified event Yangtze river basin July 2020. Results show that NDFI-SPWB user accuracy approximately 10% Kappa coefficient 0.08 compared original method, verifies feasibility effectiveness proposed framework.