The monitoring of extreme precipitation events is an important task in environmental research, but the ability of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation products to monitor extreme precipitation events remains poorly understood. In this study, three precipitation products for IMERG version 6, early-, late-, and final-run products (IMERG-E, IMERG-L, and IMERG-F, respectively), were used to capture extreme precipitation, and their applicability to monitor extreme precipitation events over Hubei province in China was evaluated. We found that the accuracy of the three IMERG precipitation products is inconsistent in areas of complex and less complex topography. Compared with gauge-based precipitation data, the results reveal the following: (1) All products can accurately capture the spatiotemporal variation patterns in precipitation during extreme precipitation events. (2) The ability of IMERG-F was good in areas of complex topography, followed by IMERG-E and IMERG-L. In areas of less complex topography, IMERG-E and IMERG-L produced outcomes that were consistent with those of IMERG-F. (3) The three IMERG precipitation products can capture the actual hourly precipitation tendencies of extreme precipitation events. (4) In areas of complex topography, the rainfall intensity estimation ability of IMERG-F is better than those of IMERG-E and IMERG-L.

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