{"title":"基于深度学习应用和Sentinel-1A数据的河内市洪水对水稻生物量的影响","authors":"Anh Ngoc Thi Do, Tuyet Anh Thi Do","doi":"10.1007/s12518-025-00631-9","DOIUrl":null,"url":null,"abstract":"<div><p>Despite being Vietnam's largest city, Hanoi's economy still relies on agriculture. Recent weather events, like floods, have significantly impacted rice biomass. Mapping and monitoring rice growth using synthetic aperture radar (SAR) data and the Artificial Bee Colony—Deep Neural Network (ABC-DNN) can provide reliable data on rice production affected by floods. Sentinel-1 satellite images from January to October 2022 showed that VH polarization yielded more detailed information than VV polarization. Field data and Support Vector Machine (SVM) classification estimated rice cultivation areas at approximately 81 ha for Winter-Spring and 77 ha for Summer-Autumn crops, with over 90% accuracy. The ABC-DNN model predicted aboveground biomass (AGB) with coefficients of determination (R2) ranging from 0.722 to 0.745. The model effectively identified flood-prone areas, aiding policymakers in developing strategies to mitigate agricultural damage, particularly in lowland regions of Hanoi.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 3","pages":"501 - 518"},"PeriodicalIF":2.3000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effects of flooding on rice biomass in Hanoi city on the basis of deep learning application and Sentinel-1A data\",\"authors\":\"Anh Ngoc Thi Do, Tuyet Anh Thi Do\",\"doi\":\"10.1007/s12518-025-00631-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Despite being Vietnam's largest city, Hanoi's economy still relies on agriculture. Recent weather events, like floods, have significantly impacted rice biomass. Mapping and monitoring rice growth using synthetic aperture radar (SAR) data and the Artificial Bee Colony—Deep Neural Network (ABC-DNN) can provide reliable data on rice production affected by floods. Sentinel-1 satellite images from January to October 2022 showed that VH polarization yielded more detailed information than VV polarization. Field data and Support Vector Machine (SVM) classification estimated rice cultivation areas at approximately 81 ha for Winter-Spring and 77 ha for Summer-Autumn crops, with over 90% accuracy. The ABC-DNN model predicted aboveground biomass (AGB) with coefficients of determination (R2) ranging from 0.722 to 0.745. The model effectively identified flood-prone areas, aiding policymakers in developing strategies to mitigate agricultural damage, particularly in lowland regions of Hanoi.</p></div>\",\"PeriodicalId\":46286,\"journal\":{\"name\":\"Applied Geomatics\",\"volume\":\"17 3\",\"pages\":\"501 - 518\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Geomatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12518-025-00631-9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geomatics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s12518-025-00631-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Effects of flooding on rice biomass in Hanoi city on the basis of deep learning application and Sentinel-1A data
Despite being Vietnam's largest city, Hanoi's economy still relies on agriculture. Recent weather events, like floods, have significantly impacted rice biomass. Mapping and monitoring rice growth using synthetic aperture radar (SAR) data and the Artificial Bee Colony—Deep Neural Network (ABC-DNN) can provide reliable data on rice production affected by floods. Sentinel-1 satellite images from January to October 2022 showed that VH polarization yielded more detailed information than VV polarization. Field data and Support Vector Machine (SVM) classification estimated rice cultivation areas at approximately 81 ha for Winter-Spring and 77 ha for Summer-Autumn crops, with over 90% accuracy. The ABC-DNN model predicted aboveground biomass (AGB) with coefficients of determination (R2) ranging from 0.722 to 0.745. The model effectively identified flood-prone areas, aiding policymakers in developing strategies to mitigate agricultural damage, particularly in lowland regions of Hanoi.
期刊介绍:
Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences.
The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology.
Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements