Scholarlink

<p>Global climate change has increased flood frequency worldwide, yet many urban and flash floods remain poorly captured by satellite remote sensing. Here we integrate Global Satellite Mapping of Precipitation data with 92.98 million Sina Weibo posts to trace spatiotemporal flood footprints across China from 2012 to 2024. Using a cascading threshold method, we identify 6,018 rainstorm events across 370 cities. Topic modeling of posts within rainstorm affected areas detects 1,094 flood events, far exceeding the 114 and 45 events recorded in Emergency Events Database and Dartmouth Flood Observatory. Comparison with satellite imagery on Google Earth Engine shows that approximately 50% of these events were unobservable by remote sensing. While satellites capture riverine flooding in open areas, social media fills critical gaps in densely populated urban settings. This social sensing framework reveals previously undocumented flood events and their propagation patterns, offering a complementary approach that enhances traditional flood monitoring capabilities.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A combination of social media and satellite data improves flood monitoring in China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月21日

查看全文
  • Hongji Gu,
  • Jun Xiao,
  • Dingtao Shen,
  • Chunxiao Zhang,
  • Shuting Xiao,
  • Zhuang Niu,
  • Fei Yu

摘要

Global climate change has increased flood frequency worldwide, yet many urban and flash floods remain poorly captured by satellite remote sensing. Here we integrate Global Satellite Mapping of Precipitation data with 92.98 million Sina Weibo posts to trace spatiotemporal flood footprints across China from 2012 to 2024. Using a cascading threshold method, we identify 6,018 rainstorm events across 370 cities. Topic modeling of posts within rainstorm affected areas detects 1,094 flood events, far exceeding the 114 and 45 events recorded in Emergency Events Database and Dartmouth Flood Observatory. Comparison with satellite imagery on Google Earth Engine shows that approximately 50% of these events were unobservable by remote sensing. While satellites capture riverine flooding in open areas, social media fills critical gaps in densely populated urban settings. This social sensing framework reveals previously undocumented flood events and their propagation patterns, offering a complementary approach that enhances traditional flood monitoring capabilities.