%0 Dataset
%T A Flood Inundation Extent Dataset for the Chaohu Basin Based on Radar Satellite Imagery Recognition
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/233f8aa7-8f70-4abd-b828-3a34507ed8e0
%W NCDC
%R 10.12072/ncdc.nhri.db6788.2025
%A Li Lingjie
%K Radar image data;Sentinel-1;classification ternary combination;dual polarization water index
%X With the intensification of global climate change and the increasing frequency of extreme weather events, flood disasters pose an escalating threat to human society and the ecological environment, severely endangering people's lives and property. Traditional ground-based observation methods have limitations in large-scale flood monitoring and are unable to meet the fast and efficient emergency response needs. This study, based on radar satellite imagery, introduces several methods such as the Dual-Polarization Water Index (SDWI-OSTU), Support Vector Machine (SVM), and Random Forest (RF), and uses the Classification Ternary Combination (CTC) ensemble strategy to identify and evaluate the accuracy of flood inundation areas on typical dates during the flood seasons of May to July 2016 and June to August 2020 in the Chaohu Basin. The monitoring error of flood inundation areas is kept within 10%. Data files are named using the format "Algorithm Name + Date".