Abstract: In light of the frequent occurrence of extreme weather events, the assessment of agricultural disasters caused by large-scale flooding is crucial for food production, agricultural insurance, and disaster prevention and mitigation. From July 29 to August 1, 2023, Baoding City experienced significant rainfall and flooding (23.7 flood disaster), resulting in substantial losses. This study focused on this event, aiming to develop a rapid assessment method for maize disaster damage using remote sensing technology, specifically leveraging the Google Earth Engine (GEE) platform. The goal is to provide a scientific basis for agricultural disaster management and post-disaster recovery. Relying on the GEE platform, the study utilized Landsat satellite data and agricultural statistics from 2016 to 2020. It examined the correlation between two vegetation indices of NDVI (normalized difference vegetation index) and EVI2 (enhanced vegetation index) and maize yield. The range of maize damage was delineated by analyzing the difference in vegetation indices between normal and disaster years. Furthermore, Sentinel-2 data was incorporated to classify maize yield extinction and reduction grades in the affected areas using supervised classification of remote sensing images and the natural breakpoint technique. The study ultimately assessed the extent of maize damage in Baoding City. The results indicated that: 1) NDVI was strongly positively correlated with maize yield, with a correlation coefficient of 0.84 (P<0.01), demonstrating its suitability for maize yield inversion. 2) Spectral analysis revealed varying degrees of maize yield reduction across Baoding. The eastern part of the city (Zhuozhou and Gaobeidian) experienced the most severe yield reductions, while the central and southern parts of the city were less affected. 3) The “23-7” flood disaster caused maize yield extinction over an area of 45 000 hectares in Baoding, representing about 5% of the total farmland. Additionally, approximately 66% of the farmland experienced yield reductions, highlighting the widespread impact of the disaster. This study underscored the importance of leveraging remote sensing technology and cloud-based platforms like GEE for improving agricultural resilience in the face of increasing climate variability and extreme weather events, and provides a rapid and reliable methodological framework for assessing maize damage from heavy rainfall and mapping damage distribution. It offers valuable insights for large-scale crop damage assessments and serves as a reference for future disaster response efforts.