Refined evaluation of maize flood disaster based on Google Earth Engine: A case study of the “23·7” heavy precipitation process in Baoding City, Hebei Province

Considering 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 2, 2023, Baoding City experienced significant rainfall and flooding (“23.7” flood disaster), resulting in substantial losses. In this study, we 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 of this study is to provide a scientific basis for agricultural disaster management and post-disaster recovery. Relying on the GEE platform, this study utilized Landsat satellite data and agricultural statistics from 2016 to 2020 to examine the correlation between maize yield and two vegetation indices: normalized difference vegetation index) and EVI2 (enhanced vegetation index). The extent of maize damage was delineated by analyzing the differences in vegetation indices between normal and disaster years. Furthermore, Sentinel-2 data were incorporated to classify the maize complete yield loss and reduction grades in the affected areas using supervised classification of remote sensing images and the natural breakpoint technique. The results indicated that: 1) NDVI was strongly positively correlated with maize yield, with a correlation coefficient of 0.841 (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, whereas the central and southern parts of the city were less affected. 3) The “23.7” flood disaster caused a complete yield loss of maize over an area of nearly 45 000 hm² in Baoding, representing about 5% of the total farmland. Additionally, approximately 66% of farmland experienced yield reductions, highlighting the widespread impact of the disaster. This study underscores the importance of leveraging remote sensing technology and cloud-based platforms such as 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. This offers valuable insights for large-scale crop damage assessments and serves as a reference for future disaster response.