{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T13:52:51Z","timestamp":1783605171661,"version":"3.55.0"},"reference-count":50,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese Academy of Sciences","award":["XDA19080304"],"award-info":[{"award-number":["XDA19080304"]}]},{"name":"Chinese Academy of Sciences","award":["210101007"],"award-info":[{"award-number":["210101007"]}]},{"name":"Henan Academy of Sciences","award":["XDA19080304"],"award-info":[{"award-number":["XDA19080304"]}]},{"name":"Henan Academy of Sciences","award":["210101007"],"award-info":[{"award-number":["210101007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Continuous and accurate acquisitions of surface water distribution are important for water resources evaluation, especially high-precision flood monitoring. During surface water extraction, optical imagery is strongly affected by clouds, while synthetic aperture radar (SAR) imagery is easily influenced by numerous physical factors; thus, the water extraction method based on single-sensor imagery cannot obtain high-precision water range under multiple scenarios. Here, we integrated the radar backscattering coefficient of ground objects into the Normalized Difference Water Index to construct a novel SAR and Optical Imagery Water Index (SOWI), and the water ranges of five study areas were extracted. We compared two previous automatic extraction methods based on single-sensor imagery and evaluated the accuracy of the extraction results. Compared with using optical and SAR imagery alone, the accuracy of all five regions was improved by up to 1\u201318%. The fusion-derived products resulted in user accuracies ranging 95\u201399% and Kappa coefficients varying by 85\u201397%. SOWI was then applied to monitor the 2021 heavy rainfall-induced Henan Province flood disaster, obtaining a time-series change diagram of flood inundation range. Our results verify SOWI\u2019s continuous high-precision monitoring capability to accurately identify waterbodies beneath clouds and algal blooms. By reducing random noise, the defects of SAR are improved and the roughness of water boundaries is overcome. SOWI is suitable for high-precision water extraction in myriad scenarios, and has great potential for use in flood disaster monitoring and water resources statistics.<\/jats:p>","DOI":"10.3390\/rs14215316","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"5316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Novel Water Index Fusing SAR and Optical Imagery (SOWI)"],"prefix":"10.3390","volume":"14","author":[{"given":"Bin","family":"Tian","sequence":"first","affiliation":[{"name":"Department of Cartography and Geographical Information Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9628-1817","authenticated-orcid":false,"given":"Fangfang","family":"Zhang","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3602-5731","authenticated-orcid":false,"given":"Fengkai","family":"Lang","sequence":"additional","affiliation":[{"name":"Department of Cartography and Geographical Information Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of the People\u2019s Republic of China, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shenglei","family":"Wang","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8590-9736","authenticated-orcid":false,"given":"Junsheng","family":"Li","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Druce, D., Tong, X.Y., Lei, X., Guo, T., Kittel, C.M.M., Grogan, K., and Tottrup, C. (2021). An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China. Remote Sens., 13.","DOI":"10.3390\/rs13091663"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Irwin, K., Beaulne, D., Braun, A., and Fotopoulos, G. (2017). Fusion of SAR, Optical Imagery and Airborne LiDAR for Surface Water Detection. Remote Sens., 9.","DOI":"10.3390\/rs9090890"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1038\/nature12760","article-title":"Global carbon dioxide emissions from inland waters","volume":"503","author":"Raymond","year":"2013","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huang, W.L., DeVries, B., Huang, C.Q., Lang, M.W., Jones, J.W., Creed, I.F., and Carroll, M.L. (2018). Automated Extraction of Surface Water Extent from Sentinel-1 Data. Remote Sens., 10.","DOI":"10.3390\/rs10050797"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gasparovic, M., and Klobucar, D. (2021). Mapping Floods in Lowland Forest Using Sentinel-1 and Sentinel-2 Data and an Object-Based Approach. Forests, 12.","DOI":"10.3390\/f12050553"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2017.05.005","article-title":"Satellite-based water use dynamics using historical Landsat data (1984-2014) in the southwestern United States","volume":"202","author":"Senay","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, C., Jia, M.M., Chen, N.C., and Wang, W. (2018). Long-Term Surface Water Dynamics Analysis Based on Landsat Imagery and the Google Earth Engine Platform: A Case Study in the Middle Yangtze River Basin. Remote Sens., 10.","DOI":"10.3390\/rs10101635"},{"key":"ref_8","first-page":"1036","article-title":"Phase research and practice of upgrading earth observation from test application to system effectiveness in China","volume":"23","author":"Zhao","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1109\/LGRS.2018.2886422","article-title":"Automatic and Unsupervised Water Body Extraction Based on Spectral-Spatial Features Using GF-1 Satellite Imagery","volume":"16","author":"Zhang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","first-page":"147","article-title":"Review of water body information extraction based on satellite remote sensing","volume":"60","author":"Dan","year":"2020","journal-title":"J. Tsinghua Univ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4694","DOI":"10.1029\/2019GL082562","article-title":"Active-Passive Surface Water Classification: A New Method for high-Resolution monitoring of Surface Water Dynamics","volume":"46","author":"Slinski","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Deng, Y., Jiang, W.G., Tang, Z.H., Li, J.H., Lv, J.X., Chen, Z., and Jia, K. (2017). Spatio-Temporal Change of Lake Water Extent in Wuhan Urban Agglomeration Based on Landsat Images from 1987 to 2015. Remote Sens., 9.","DOI":"10.3390\/rs9030270"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3810","DOI":"10.1073\/pnas.1719275115","article-title":"Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016","volume":"115","author":"Zou","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"13807","DOI":"10.3390\/rs71013807","article-title":"Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data","volume":"7","author":"Sharma","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2013.08.029","article-title":"Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery","volume":"140","author":"Feyisa","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_16","first-page":"37","article-title":"Recent research progress on long time series and large scale optical remote sensing of inland water","volume":"25","author":"Bing","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_17","first-page":"358","article-title":"Surface water extraction in Yangtze River Basin based on sentinel time series image","volume":"26","author":"Yuchen","year":"2022","journal-title":"J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/22797254.2017.1297540","article-title":"Object-based water body extraction model using Sentinel-2 satellite imagery","volume":"50","author":"Kaplan","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12503","DOI":"10.3390\/rs70912503","article-title":"Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network","volume":"7","author":"Jones","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"509","DOI":"10.14358\/PERS.83.7.509","article-title":"Automated Water Classification in the Tibetan Plateau Using Chinese GF-1 WFV Data","volume":"83","author":"Zhang","year":"2017","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the normalized difference water index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens"},{"key":"ref_22","first-page":"589","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_23","first-page":"1370","article-title":"Water extraction model of multispectral optical remote sensing image","volume":"50","author":"Kaiyuan","year":"2021","journal-title":"Acta. Geod. Et. Cartogr. Sin."},{"key":"ref_24","first-page":"4301","article-title":"Multiscale Water Body Extraction in Urban Environments from Satellite Images","volume":"7","author":"Zhou","year":"2014","journal-title":"IEEE J-Stars"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1109\/TGRS.2019.2950705","article-title":"A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions","volume":"58","author":"Ahmad","year":"2020","journal-title":"IEEE Trans Geosci Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111582","DOI":"10.1016\/j.rse.2019.111582","article-title":"Flood mapping under vegetation using single SAR acquisitions","volume":"237","author":"Grimaldi","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.isprsjprs.2019.10.017","article-title":"A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery","volume":"159","author":"Liang","year":"2020","journal-title":"ISPRS J. Photogramm"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shen, X.Y., Wang, D.C., Mao, K.B., Anagnostou, E., and Hong, Y. (2019). Inundation Extent Mapping by Synthetic Aperture Radar: A Review. Remote Sens., 11.","DOI":"10.3390\/rs11070879"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, X.Y., Liu, L., Shao, Y., Zhao, Q.H., Zhang, Q.J., and Lou, L.J. (2018). Water Detection in Urban Areas from GF-3. Sensors, 18.","DOI":"10.3390\/s18041299"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4631","DOI":"10.1029\/2001GL013263","article-title":"Remote sensing of global wetland dynamics with multiple satellite data sets","volume":"28","author":"Prigent","year":"2001","journal-title":"Geophys. Res. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7615","DOI":"10.3390\/rs70607615","article-title":"A Collection of SAR Methodologies for Monitoring Wetlands","volume":"7","author":"White","year":"2015","journal-title":"Remote Sens."},{"key":"ref_32","unstructured":"Wang, G.L. (2015). Active and Passive Remote Sensing Monitoring of Cyanobacterial Blooms in Inland Waters, East China Normal University."},{"key":"ref_33","unstructured":"Zeng, C.Q., Wang, J.F., Huang, X.D., Bird, S., and Luce, J.J. (April, January 30). Urban water body detection from the combination of high-resolution optical and SAR images. Proceedings of the JURSE, Lausanne, Switzerland."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6652","DOI":"10.3390\/s150306652","article-title":"Water Area Extraction Using RADARSAT SAR Imagery Combined with Landsat Imagery and Terrain Information","volume":"15","author":"Hong","year":"2015","journal-title":"Sensors"},{"key":"ref_35","first-page":"1331","article-title":"Complex Coherence Estimation Based on Adaptive Refined Lee Filter","volume":"44","author":"Long","year":"2015","journal-title":"Acta Geod. Et Cartogr. Sin."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jiang, W.H., Yu, A.X., Dong, Z., and Wang, Q.S. (2016). Comparison and Analysis of Geometric Correction Models of Spaceborne SAR. Sensors, 16.","DOI":"10.3390\/s16070973"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2990","DOI":"10.1080\/01431161.2016.1192304","article-title":"Sentinel-1-based flood mapping: A fully automated processing chain","volume":"37","author":"Twele","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1111\/jfr3.12303","article-title":"Multi-temporal synthetic aperture radar flood mapping using change detection","volume":"11","author":"Clement","year":"2018","journal-title":"J. Flood Risk Manag."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, G., Wu, M., Wei, X., and Song, H. (2020). Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12050795"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jiang, W., He, G., Long, T., Ni, Y., Liu, H., Peng, Y., Lv, K., and Wang, G. (2018). Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images. Remote Sens., 10.","DOI":"10.3390\/rs10050755"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1080\/01431160310001618103","article-title":"Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices","volume":"25","author":"Rogers","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, L.F., Zhang, P., Xing, J., Li, Z.H., Xing, X.M., and Yuan, Z.H. (2020). A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas. Remote Sens., 12.","DOI":"10.3390\/rs12193205"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1007\/s00343-008-0434-4","article-title":"Depth inversion in coastal water based on SAR image of waves","volume":"26","author":"Fan","year":"2008","journal-title":"Chin. J. Oceanol. Limnol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1080\/01431161.2018.1444292","article-title":"A simple automated dynamic threshold extraction method for the classification of large water bodies from landsat-8 OLI water index images","volume":"39","author":"Zhang","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"781","DOI":"10.11834\/jrs.20229340","article-title":"Small water body extraction method based on Sentinel-2 satellite multi-spectral remote sensing image","volume":"26","author":"Wu","year":"2022","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_46","first-page":"555","article-title":"Study on water information extraction using domestic GF-3 image","volume":"23","author":"Gu","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_47","first-page":"135","article-title":"RADARSAT-2 Beam Mode Selection for Surface Water and Flooded Vegetation Mapping","volume":"40","author":"White","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_48","first-page":"152","article-title":"Remote sensing analysis and simulation of change of Ulan Ul Lake in the past 40 years","volume":"26","author":"Yan","year":"2014","journal-title":"Remote Sens. Land Resour."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.14358\/PERS.75.11.1307","article-title":"Analysis of Dynamic Thresholds for the Normalized Difference Water Index","volume":"75","author":"Ji","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_50","first-page":"2177","article-title":"Study on the spectral characteristics of the snow layer moisture content in the snowmelt period on the northern slope of Tianshan Mountains","volume":"33","author":"Qunzhu","year":"2013","journal-title":"Spectrosc Spect. Anal."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5316\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:01:46Z","timestamp":1760144506000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,24]]},"references-count":50,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215316"],"URL":"https:\/\/doi.org\/10.3390\/rs14215316","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,24]]}}}