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Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China

文献类型：期刊论文

作者Zhang, Shuangcheng7,8; Ma, Zhongmin8; Li, Zhenhong5,6,8; Zhang, Pengfei4; Liu, Qi3,8; Nan, Yang2; Zhang, Jingjiang1; Hu, Shengwei8; Feng, Yuxuan8; Zhao, Hebin8
刊名REMOTE SENSING
出版日期2021-12-01
卷号13期号:24页码:15
关键词Cyclone Global Navigation Satellite System flood inundation extreme precipitation global navigation satellite system-reflectometry Soil Moisture Active Passive
DOI10.3390/rs13245181
英文摘要On 20 July 2021, parts of China's Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars' worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth's surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.
WOS关键词SOIL-MOISTURE ; GPS SIGNALS ; GNSS ; OCEAN ; REFLECTOMETRY ; ORBIT
资助项目National Natural Science Foundation of China[42074041] ; National Natural Science Foundation of China[41731066] ; National Key Research and Development Program of China[2020YFC1512000] ; National Key Research and Development Program of China[2019YFC1509802] ; State Key Laboratory of Geo-Information Engineering[SKLGIE2019-Z-2-1] ; Shaanxi Natural Science Research Program[2020JM-227]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000742868700001
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Shaanxi Natural Science Research Program ; Shaanxi Natural Science Research Program ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Shaanxi Natural Science Research Program ; Shaanxi Natural Science Research Program ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Shaanxi Natural Science Research Program ; Shaanxi Natural Science Research Program ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Shaanxi Natural Science Research Program ; Shaanxi Natural Science Research Program
源URL[http://210.72.145.45/handle/361003/14001]  
专题国家授时中心_守时理论与方法研究室
通讯作者Ma, Zhongmin
作者单位1.China Meteorol Adm, Inst Urban Meteorol, Beijing 100089, Peoples R China
2.Wuhan Univ, GNSS Res Ctr, Wuhan 430079, Peoples R China
3.CSIC, Inst Space Sci ICE, Earth Observat Res Grp, Barcelona 08290, Spain
4.Chinese Acad Sci, Natl Time Serv Ctr, Xian 710600, Peoples R China
5.Minist Educ, Key Lab Western Chinas Mineral Resources & Geol E, Xian 710054, Peoples R China
6.Changan Univ, Big Data Ctr Geosci & Satellites, Xian 710054, Peoples R China
7.State Key Lab Geoinformat Engn, Xian 710054, Peoples R China
8.Changan Univ, Coll Geol Engn, Xian 710054, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Shuangcheng,Ma, Zhongmin,Li, Zhenhong,et al. Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China[J]. REMOTE SENSING,2021,13(24):15.
APA Zhang, Shuangcheng.,Ma, Zhongmin.,Li, Zhenhong.,Zhang, Pengfei.,Liu, Qi.,...&Zhao, Hebin.(2021).Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China.REMOTE SENSING,13(24),15.
MLA Zhang, Shuangcheng,et al."Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China".REMOTE SENSING 13.24(2021):15.

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