{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T05:11:35Z","timestamp":1774588295631,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T00:00:00Z","timestamp":1665705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2019YFC1509202"],"award-info":[{"award-number":["2019YFC1509202"]}]},{"name":"National Key Research and Development Program of China","award":["61371189"],"award-info":[{"award-number":["61371189"]}]},{"name":"National Key Research and Development Program of China","award":["41701513"],"award-info":[{"award-number":["41701513"]}]},{"name":"National Key Research and Development Program of China","award":["41772350"],"award-info":[{"award-number":["41772350"]}]},{"name":"National Key Research and Development Program of China","award":["2019GGX101033"],"award-info":[{"award-number":["2019GGX101033"]}]},{"name":"National Natural Science Foundation of China","award":["2019YFC1509202"],"award-info":[{"award-number":["2019YFC1509202"]}]},{"name":"National Natural Science Foundation of China","award":["61371189"],"award-info":[{"award-number":["61371189"]}]},{"name":"National Natural Science Foundation of China","award":["41701513"],"award-info":[{"award-number":["41701513"]}]},{"name":"National Natural Science Foundation of China","award":["41772350"],"award-info":[{"award-number":["41772350"]}]},{"name":"National Natural Science Foundation of China","award":["2019GGX101033"],"award-info":[{"award-number":["2019GGX101033"]}]},{"name":"Key Research and Development Program of Shandong Province","award":["2019YFC1509202"],"award-info":[{"award-number":["2019YFC1509202"]}]},{"name":"Key Research and Development Program of Shandong Province","award":["61371189"],"award-info":[{"award-number":["61371189"]}]},{"name":"Key Research and Development Program of Shandong Province","award":["41701513"],"award-info":[{"award-number":["41701513"]}]},{"name":"Key Research and Development Program of Shandong Province","award":["41772350"],"award-info":[{"award-number":["41772350"]}]},{"name":"Key Research and Development Program of Shandong Province","award":["2019GGX101033"],"award-info":[{"award-number":["2019GGX101033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Microwave remote sensing is widely applied in flood monitoring due to its independence from severe weather conditions, which usually restrict the usage of optical sensors. However, it is challenging to track the variation process of flood events in a timely manner by traditional active and passive microwave techniques, since they cannot simultaneously provide measurements with high spatial and temporal resolution. The emerging Global Navigation Satellite System Reflectometry (GNSS-R) technique with high spatio-temporal resolution offers a new solution to the dynamic monitoring of flood inundation. Considering the high sensitivity of GNSS-R signals to flooding, this paper proposes a dual-branch neural network (DBNN) with a convolution neural network (CNN) and a back propagation (BP) neural network for flood monitoring. The CNN module is used to automatically extract the abstract features from delay-Doppler maps (DDMs), while the BP module is fed with GNSS-R typical features, such as surface reflectivity and power ratio, as well as vegetation information from Soil Moisture Active Passive satellite (SMAP) data. In the experiments, the superiority of the DBNN method is firstly demonstrated by comparing it with the surface reflectivity and power ratio methods. Then, the spatio-temporal variation process of the 2020 South Asian flood events is analyzed by the proposed method based on Cyclone Global Navigation Satellite System (CYGNSS) data. The understanding of flood change processes could help enhance the capacity for resisting flood disasters.<\/jats:p>","DOI":"10.3390\/rs14205129","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"5129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Novel Dual-Branch Neural Network Model for Flood Monitoring in South Asia Based on CYGNSS Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Dongmei","family":"Song","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5928-260X","authenticated-orcid":false,"given":"Qiqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2565-1013","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Cong","family":"Yin","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Junming","family":"Xia","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101542","DOI":"10.1016\/j.ijdrr.2020.101542","article-title":"A systematic assessment of the effects of extreme flash floods on transportation infrastructure and circulation: The example of the 2017 Mandra flood","volume":"47","author":"Diakakis","year":"2020","journal-title":"Int. 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