{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:18:16Z","timestamp":1780773496320,"version":"3.54.1"},"reference-count":57,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,4,27]],"date-time":"2020-04-27T00:00:00Z","timestamp":1587945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 108-2621-M-008-002-"],"award-info":[{"award-number":["MOST 108-2621-M-008-002-"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hakka Affairs Council","award":["HAC-108-IP-0005-01-04"],"award-info":[{"award-number":["HAC-108-IP-0005-01-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rainfall-induced floods often cause significant loss of life as well as damage to infrastructure and crops. Synthetic Aperture Radar (SAR) Earth Observation Satellites (EOS) can be used to determine the extent of flooding over large geographical areas. Unlike optical sensors, SAR instruments are suitable for cloudy weather conditions, making them suitable for flood detection and mapping during extreme weather events. In this study, we explore the application of the Normalized Difference Sigma-Naught Index (NDSI) and Shannon\u2019s entropy of NDSI (SNDSI) of Sentinel-1 data for open water flooding detection, based on automatic thresholding and Bayesian probability. The proposed methodology was tested using the floods in Sofala province, Mozambique, caused by cyclone Idai on March 14\u201319 of 2019. Results show that thresholding of the NDSI Vertical Transmit-Horizontal Receive (VH) can produce results with Overall Accuracy above 90%, and Kappa higher than 0.6. Considerable performance improvements were obtained by our thresholding method over the entropy of NDSI, yielding results with Kappa of 0.70\u20130.77. Additionally, it was found that Weibull distribution can properly describe the properties of flooded pixels within the histogram of SNDSI, which allows us to generate a flood probability raster using a Bayesian approach. The final per-pixel flooding probability is useful to indicate certainty in the classification results. The SNDSI Bayesian model produced an AUC (Area Under the Receiver Operating Characteristic Curve) of 0.93\u20130.97, with cross-polarized data yielding the most accurate results.<\/jats:p>","DOI":"10.3390\/rs12091384","type":"journal-article","created":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T10:30:58Z","timestamp":1588069858000},"page":"1384","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon\u2019s Entropy"],"prefix":"10.3390","volume":"12","author":[{"given":"Noel","family":"Ivan Ulloa","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, National Central University, Taoyuan City 32001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6402-7048","authenticated-orcid":false,"given":"Shou-Hao","family":"Chiang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Central University, Taoyuan City 32001, Taiwan"},{"name":"Center for Space and Remote Sensing Research, National Central University, Taoyuan City 32001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6952-6156","authenticated-orcid":false,"given":"Sang-Ho","family":"Yun","sequence":"additional","affiliation":[{"name":"NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jha, M.K. (2010). Natural and Anthropogenic Disasters: Vulnerability, Preparedness and Mitigation, Springer.","DOI":"10.1007\/978-90-481-2498-5"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Keller, E., and DeVecchio, D. (2019). Natural Hazards\u2014Earth\u2019s Processes as Hazards, Disasters, and Catastrophes, Routledge. [5th ed.].","DOI":"10.4324\/9781315164298"},{"key":"ref_3","unstructured":"Organization for Economic Cooperation and Development (2016). Financial Management of Flood Risk, OECD."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sen, Z. (2018). Flood Modeling, Prediction and Mitigation, Springer.","DOI":"10.1007\/978-3-319-52356-9"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bohorquez, P., and del Moral-Erencia, D.J. (2017). 100 Years of Competition between Reduction in Channel Capacity and Streamflow during Floods in the Guadalquivir River (Southern Spain). Remote Sens., 9.","DOI":"10.3390\/rs9070727"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2017.11.006","article-title":"An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery","volume":"136","author":"Tong","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"012021","DOI":"10.1088\/1755-1315\/98\/1\/012021","article-title":"Flood Disaster Analysis Using Landsat-8 and SPOT-6 Imagery for Determination of Flooded Areas in Sampang, Madura","volume":"98","author":"Sukojo","year":"2017","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_8","first-page":"101951","article-title":"Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features","volume":"84","author":"Goffi","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Refice, A., D\u2019Addabbo, A., and Capolongo, D. (2018). Flood Monitoring through Remote Sensing, Springer.","DOI":"10.1007\/978-3-319-63959-8"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Schumann, G., Bates, P.D., Apel, H., and Aronica, G.T. (2018). Global Flood Hazard Mapping, Modeling, and Forecasting, American Geophysical Union. Global Flood Hazard.","DOI":"10.1002\/9781119217886"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.pce.2015.05.002","article-title":"Microwave remote sensing of flood inundation","volume":"83\u201384","author":"Schumann","year":"2015","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D., and Hossard, L. (2018). Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens., 10.","DOI":"10.1117\/12.2325160"},{"key":"ref_14","first-page":"857","article-title":"Mapping Seasonal Flooding in Forested Wetlands Using Multi-Temporal Radarsat SAR","volume":"67","author":"Townsend","year":"2001","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1002\/(SICI)1099-1085(199708)11:10<1415::AID-HYP532>3.0.CO;2-2","article-title":"Assessment of the mapping capabilities of ERS-1 SAR data for flood mapping: A case study in Germany","volume":"11","author":"Oberstadler","year":"1997","journal-title":"Hydrol. Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TGRS.2015.2482001","article-title":"Use of SAR Data for Detecting Floodwater in Urban and Agricultural Areas: The Role of the Interferometric Coherence","volume":"54","author":"Pulvirenti","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.isprsjprs.2014.07.014","article-title":"A fully automated TerraSAR-X based flood service","volume":"104","author":"Martinis","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","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_19","unstructured":"Schumann, G., Henry, J., Hoffmann, L., Pfister, L., Pappenberger, F., and Matgen, P. (2005, January 6\u20139). Demonstrating the high potential of remote sensing in hydraulic modelling and flood risk management. Proceedings of the Annual Conference of the Remote Sensing and Photogrammetry Society with the NERC Earth Observation Conference, Portsmouth, UK."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1177\/0309133316633570","article-title":"Progress in operational flood mapping using satellite synthetic aperture radar (SAR) and airborne light detection and ranging (LiDAR) data","volume":"40","author":"Brown","year":"2016","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1080\/01431160010014729","article-title":"Integration of remote sensing data and GIS for accurate mapping of flooded areas","volume":"23","author":"Brivio","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1080\/01431160500486724","article-title":"Envisat multi-polarized ASAR data for flood mapping","volume":"27","author":"Henry","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zoka, M., Psomiadis, E., and Dercas, N. (2018). The Complementary Use of Optical and SAR Data in Monitoring Flood Events and Their Effects. Proceedings, 2.","DOI":"10.3390\/proceedings2110644"},{"key":"ref_24","unstructured":"The Earth Observatory (2019, December 11). Devastation in Mozambique, Available online: https:\/\/earthobservatory.nasa.gov\/images\/144712\/devastation-in-mozambique."},{"key":"ref_25","unstructured":"Probst, P., and Annunziato, A. (2019). Tropical Cyclone IDAI: Analysis of the Wind, Rainfall and Storm Surge Impact, European Comission Joint Research Centre."},{"key":"ref_26","unstructured":"Global Facility for Disaster Reduction and Recovery (2019). Mozambique Cyclone Idai Post-Disaster Needs Assessment, Global Facility for Disaster Reduction and Recovery."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Buchhorn, M., Lesiv, M., Tsendbazar, N.E., Herold, M., Bertels, L., and Smets, B. (2020). Copernicus Global Land Cover Layers\u2014Collection 2. Remote Sens., 12.","DOI":"10.3390\/rs12061044"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Schubert, A., Miranda, N., Geudtner, D., and Small, D. (2017). Sentinel-1A\/B Combined Product Geolocation Accuracy. Remote Sens., 9.","DOI":"10.3390\/rs9060607"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tamm, T., Zalite, K., Voormansik, K., and Talgre, L. (2016). Relating Sentinel-1 Interferometric Coherence to Mowing Events on Grasslands. Remote Sens., 8.","DOI":"10.3390\/rs8100802"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9431","DOI":"10.3390\/rs70709431","article-title":"Sentinel-1A Product Geolocation Accuracy: Commissioning Phase Results","volume":"7","author":"Schubert","year":"2015","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1016\/j.rse.2018.03.006","article-title":"Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data","volume":"209","author":"Cian","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.rse.2019.01.036","article-title":"Natural color representation of Sentinel-2 data","volume":"225","author":"Sovdat","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2019.01.018","article-title":"Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities","volume":"223","author":"Rapinel","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Vuolo, F., \u017b\u00f3\u0142tak, M., Pipitone, C., Zappa, L., Wenng, H., Immitzer, M., Weiss, M., Baret, F., and Atzberger, C. (2016). Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples. Remote Sens., 8.","DOI":"10.3390\/rs8110938"},{"key":"ref_38","unstructured":"Furuta, R., and Tomiyama, N. (2011, January 7). A Study of Detection of Landslide Disasters due to the Pakistan Earthquake using ALOS data. Proceedings of the 34th International Symposium on Remote Sensing of Environment, Sydney, Australia."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Prieto-Guerrero, A., and Espinosa-Paredes, G. (2019). 7\u2014Nonlinear signal processing methods: DR estimation and nonlinear stability indicators. Linear and Non-Linear Stability Analysis in Boiling Water Reactors, Woodhead Publishing.","DOI":"10.1016\/B978-0-08-102445-4.00007-2"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Galar, D., and Kumar, U. (2017). Chapter 3\u2014Preprocessing and Features. eMaintenance, Academic Press.","DOI":"10.1016\/B978-0-12-811153-6.00003-8"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Navarro, P.J., Fern\u00e1ndez-Isla, C., Alcover, P.M., and Suard\u00edaz, J. (2016). Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level. Sensors, 16.","DOI":"10.3390\/s16081178"},{"key":"ref_42","unstructured":"Gonzalez, R.C., and Woods, R.E. (2001). Digital Image Processing, Addison-Wesley Longman Publishing."},{"key":"ref_43","unstructured":"Gonzalez, R.C., Woods, R.E., and Eddins, S.L. (2003). Digital Image Processing Using MATLAB, Prentice-Hall."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6958","DOI":"10.1109\/TGRS.2016.2592951","article-title":"Probabilistic Flood Mapping Using Synthetic Aperture Radar Data","volume":"54","author":"Giustarini","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lin, N.Y., Yun, S.-H., Bhardwaj, A., and Hill, M.E. (2019). Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew. Remote Sens., 11.","DOI":"10.3390\/rs11151778"},{"key":"ref_46","unstructured":"Morton, J.C. (2019). Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, CRC Press. [4th ed.]."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"651","DOI":"10.5194\/hess-17-651-2013","article-title":"Automated global water mapping based on wide-swath orbital synthetic-aperture radar. Hydrol","volume":"17","author":"Westerhoff","year":"2013","journal-title":"Earth Syst. Sci."},{"key":"ref_48","first-page":"1","article-title":"Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy","volume":"2019","author":"Yang","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.apm.2016.08.034","article-title":"Numerical optimization applying trust-region reflective least squares algorithm with constraints to optimize the non-linear creep parameters of soft soil","volume":"41","author":"Le","year":"2017","journal-title":"Appl. Math. Model."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6975","DOI":"10.1109\/TGRS.2017.2737664","article-title":"A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case","volume":"55","author":"Chini","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","unstructured":"Refice, A., D\u2019Addabbo, A., and Capolongo, D. (2018). Flood Mapping in Vegetated and Urban Areas and Other Challenges: Models and Methods. Flood Monitoring through Remote Sensing, Springer International Publishing.","DOI":"10.1007\/978-3-319-63959-8"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.1016\/j.rse.2008.03.018","article-title":"HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia","volume":"112","author":"Nobre","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Bioresita, F., Puissant, A., Stumpf, A., and Malet, J.P. (2018). A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020217"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.geomorph.2018.09.024","article-title":"Probabilistic floodplain mapping using HAND-based statistical approach","volume":"324","author":"Jafarzadegan","year":"2019","journal-title":"Geomorphology"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1384\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:23:57Z","timestamp":1760361837000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1384"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,27]]},"references-count":57,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["rs12091384"],"URL":"https:\/\/doi.org\/10.3390\/rs12091384","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,27]]}}}