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Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China
Zhang, Shuangcheng1,2; Ma, Zhongmin1; Li, Zhenhong1,3,4; Zhang, Pengfei5; Liu, Qi1,6; Nan, Yang7; Zhang, Jingjiang8; Hu, Shengwei1; Feng, Yuxuan1; Zhao, Hebin1
2021-12-01
Source PublicationREMOTE SENSING
Volume13Issue:24Pages:15
AbstractOn 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.
KeywordCyclone Global Navigation Satellite System flood inundation extreme precipitation global navigation satellite system-reflectometry Soil Moisture Active Passive
Funding OrganizationNational 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
DOI10.3390/rs13245181
WOS KeywordSOIL-MOISTURE ; GPS SIGNALS ; GNSS ; OCEAN ; REFLECTOMETRY ; ORBIT
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000742868700001
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://210.72.145.45/handle/361003/14001
Collection守时理论与方法研究室
Corresponding AuthorMa, Zhongmin
Affiliation1.Changan Univ, Coll Geol Engn, Xian 710054, Peoples R China
2.State Key Lab Geoinformat Engn, Xian 710054, Peoples R China
3.Changan Univ, Big Data Ctr Geosci & Satellites, Xian 710054, Peoples R China
4.Minist Educ, Key Lab Western Chinas Mineral Resources & Geol E, Xian 710054, Peoples R China
5.Chinese Acad Sci, Natl Time Serv Ctr, Xian 710600, Peoples R China
6.CSIC, Inst Space Sci ICE, Earth Observat Res Grp, Barcelona 08290, Spain
7.Wuhan Univ, GNSS Res Ctr, Wuhan 430079, Peoples R China
8.China Meteorol Adm, Inst Urban Meteorol, Beijing 100089, Peoples R China
Recommended Citation
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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