{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:28:14Z","timestamp":1773383294412,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000240","name":"New Brunswick Innovation Foundation","doi-asserted-by":"publisher","award":["RAI2019-026"],"award-info":[{"award-number":["RAI2019-026"]}],"id":[{"id":"10.13039\/501100000240","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect flooded areas accurately; however, the complexity of urban structures makes flood mapping in urban areas a challenging task. In this study, we examine the synergistic use of SAR simulated reflectivity maps and Polarimetric and Interferometric SAR (PolInSAR) features in the improvement of flood mapping in urban environments. We propose a machine learning model employing simulated and PolInSAR features derived from TerraSAR-X images along with five auxiliary features, namely elevation, slope, aspect, distance from the river, and land-use\/land-cover that are well-known to contribute to flood mapping. A total of 2450 data points have been used to build and evaluate the model over four different areas with different vegetation and urban density. The results indicated that by using PolInSAR and SAR simulated reflectivity maps together with five auxiliary features, a classification overall accuracy of 93.1% in urban areas was obtained, representing a 9.6% improvement over using the five auxiliary features alone.<\/jats:p>","DOI":"10.3390\/rs14236154","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T05:31:32Z","timestamp":1670218292000},"page":"6154","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Urban Flood Detection Using TerraSAR-X and SAR Simulated Reflectivity Maps"],"prefix":"10.3390","volume":"14","author":[{"given":"Shadi Sadat","family":"Baghermanesh","sequence":"first","affiliation":[{"name":"Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB E3B5A3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8633-3847","authenticated-orcid":false,"given":"Shabnam","family":"Jabari","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB E3B5A3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6439-3339","authenticated-orcid":false,"given":"Heather","family":"McGrath","sequence":"additional","affiliation":[{"name":"Natural Resources Canada, Ottawa, ON K1A0E4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Olthof, I., and Svacina, N. (2020). Testing Urban Flood Mapping Approaches from Satellite and In-Situ Data Collected during 2017 and 2019 Events in Eastern Canada. Remote Sens., 12.","DOI":"10.3390\/rs12193141"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1038\/s41558-018-0173-2","article-title":"Global Economic Response to River Floods","volume":"8","author":"Willner","year":"2018","journal-title":"Nat. Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/02626667.2013.857411","article-title":"Flood Risk and Climate Change: Global and Regional Perspectives","volume":"59","author":"Kundzewicz","year":"2014","journal-title":"Hydrol. Sci. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1038\/nclimate1911","article-title":"Global Flood Risk under Climate Change","volume":"3","author":"Hirabayashi","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lin, Y.N., Yun, S.-H., Bhardwaj, A., and Hill, E.M. (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_6","doi-asserted-by":"crossref","unstructured":"Chini, M., Pelich, R., Pulvirenti, L., Pierdicca, N., Hostache, R., and Matgen, P. (2019). Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as a Test Case. Remote Sens., 11.","DOI":"10.3390\/rs11020107"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.isprsjprs.2019.04.014","article-title":"Urban Flood Mapping with an Active Self-Learning Convolutional Neural Network Based on TerraSAR-X Intensity and Interferometric Coherence","volume":"152","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3290","DOI":"10.1109\/TGRS.2018.2797536","article-title":"Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images","volume":"56","author":"Amitrano","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1080\/0143116031000150022","article-title":"Using Landsat 7 TM Data Acquired Days after a Flood Event to Delineate the Maximum Flood Extent on a Coastal Floodplain","volume":"25","author":"Wang","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","unstructured":"Reinartz, P., M\u00fcller, R., Suri, S., Schwind, P., and Schneider, M. (2022, November 08). Terrasar-x Data for Improving Geometric Accuracy of Optical High and Very High Resolution Satellite Data. Available online: https:\/\/www.isprs.org\/proceedings\/XXXVIII\/part1\/11\/11_01_Paper_17.pdf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2417","DOI":"10.1109\/TGRS.2012.2210901","article-title":"A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X","volume":"51","author":"Giustarini","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1109\/TGRS.2009.2029236","article-title":"Flood Detection in Urban Areas Using TerraSAR-X","volume":"48","author":"Mason","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2711","DOI":"10.1109\/JSTARS.2014.2305165","article-title":"SAR and InSAR for Flood Monitoring: Examples with COSMO-SkyMed Data","volume":"7","author":"Refice","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Uddin, K., Matin, M.A., and Meyer, F.J. (2019). Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh. Remote Sens., 11.","DOI":"10.3390\/rs11131581"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"303","DOI":"10.5194\/nhess-9-303-2009","article-title":"Towards Operational near Real-Time Flood Detection Using a Split-Based Automatic Thresholding Procedure on High Resolution TerraSAR-X Data","volume":"9","author":"Martinis","year":"2009","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1016\/j.rse.2010.12.002","article-title":"Flood Monitoring Using Multi-Temporal COSMO-SkyMed Data: Image Segmentation and Signature Interpretation","volume":"115","author":"Pulvirenti","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.rse.2017.06.042","article-title":"River Flood Mapping in Urban Areas Combining Radarsat-2 Data and Flood Return Period Data","volume":"198","author":"Tanguy","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kwak, Y., Yun, S., and Iwami, Y. (2017, January 23\u201328). A New Approach for Rapid Urban Flood Mapping Using ALOS-2\/PALSAR-2 in 2015 Kinu River Flood, Japan. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127344"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3041","DOI":"10.1109\/TGRS.2011.2178030","article-title":"Near Real-Time Flood Detection in Urban and Rural Areas Using High-Resolution Synthetic Aperture Radar Images","volume":"50","author":"Mason","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1080\/01431169008955095","article-title":"Radar Detection of Flooding beneath the Forest Canopy: A Review","volume":"11","author":"Hess","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.1080\/01431160116902","article-title":"Flood Boundary Delineation from Synthetic Aperture Radar Imagery Using a Statistical Active Contour Model","volume":"22","author":"Horritt","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shen, X., Wang, D., Mao, K., Anagnostou, E., and Hong, Y. (2019). Inundation Extent Mapping by Synthetic Aperture Radar: A Review. Remote Sens., 11.","DOI":"10.3390\/rs11070879"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.pce.2010.12.009","article-title":"Towards an Automated SAR-Based Flood Monitoring System: Lessons Learned from Two Case Studies","volume":"36","author":"Matgen","year":"2011","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2288","DOI":"10.1109\/JSTARS.2019.2911596","article-title":"Flood Area Detection Using PALSAR-2 Amplitude and Coherence Data: The Case of the 2015 Heavy Rainfall in Japan","volume":"12","author":"Ohki","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chaabani, C., Chini, M., Abdelfattah, R., Hostache, R., and Chokmani, K. (2018). Flood Mapping in a Complex Environment Using Bistatic TanDEM-X\/TerraSAR-X InSAR Coherence. Remote Sens., 10.","DOI":"10.3390\/rs10121873"},{"key":"ref_26","first-page":"4018405","article-title":"Mapping Floods in Urban Areas from Dual-Polarization InSAR Coherence Data","volume":"19","author":"Pelich","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4209121","DOI":"10.1109\/TGRS.2022.3199036","article-title":"Urban-Aware U-Net for Large-Scale Urban Flood Mapping Using Multitemporal Sentinel-1 Intensity and Interferometric Coherence","volume":"60","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Baghermanesh, S.S., Jabari, S., and McGrath, H. (2021, January 11\u201316). Urban Flood Detection Using Sentinel1-A Images. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554283"},{"key":"ref_29","unstructured":"Ferretti, A., Monti-Guarnieri, A., Prati, C., and Rocca, F. (2007). InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ustuner, M., and Balik Sanli, F. (2019). Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020097"},{"key":"ref_31","first-page":"1104","article-title":"Full Polarimetric SAR Classification Based on Yamaguchi Decomposition Model and Scattering Parameters","volume":"Volume 2","author":"Han","year":"2010","journal-title":"Proceedings of the 2010 IEEE International Conference on Progress in Informatics and Computing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"S298","DOI":"10.5589\/m10-062","article-title":"Compact Polarimetry Overview and Applications Assessment","volume":"36","author":"Charbonneau","year":"2010","journal-title":"Can. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mohammadimanesh, F., Salehi, B., Mahdianpari, M., Brisco, B., and Gill, E. (2019). Full and Simulated Compact Polarimetry Sar Responses to Canadian Wetlands: Separability Analysis and Classification. Remote Sens., 11.","DOI":"10.3390\/rs11050516"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/JSTARS.2013.2275928","article-title":"Automatic SAR Simulation Technique for Object Identification in Complex Urban Scenarios","volume":"7","author":"Tao","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","first-page":"150","article-title":"Detection of Flooded Urban Areas in High Resolution Synthetic Aperture Radar Images Using Double Scattering","volume":"28","author":"Mason","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"609","DOI":"10.5194\/isprs-annals-V-3-2020-609-2020","article-title":"Flood mapping using random forest and identifying the essential conditioning factors; a case study in fredericton, new brunswick, canada","volume":"5","author":"Esfandiari","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s12665-011-1504-z","article-title":"An Artificial Neural Network Model for Flood Simulation Using GIS: Johor River Basin, Malaysia","volume":"67","author":"Kia","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_38","unstructured":"Ottawa River Regulation Planning Board (2018). Summary of the 2017 Spring Flood."},{"key":"ref_39","unstructured":"Hooper, A.J. (2006). Persistent Scatter Radar Interferometry for Crustal Deformation Studies and Modeling of Volcanic Deformation, Stanford University."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/MGRS.2018.2873644","article-title":"Phase Unwrapping in InSAR: A Review","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_41","first-page":"159","article-title":"Multibaseline Phase Unwrapping for InSAR Topography Estimation","volume":"24","author":"Ferretti","year":"2001","journal-title":"Nuovo Cim. C"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bouchemakh, L., Smara, Y., Boutarfa, S., and Hamadache, Z. (2008, January 7\u201311). A Comparative Study of Speckle Filtering in Polarimetric Radar SAR Images. Proceedings of the 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, Syria.","DOI":"10.1109\/ICTTA.2008.4530040"},{"key":"ref_43","unstructured":"Auer, S.J. (2011). 3D Synthetic Aperture Radar Simulation forInterpreting Complex Urban Re Scenarios. [Doctoral Dissertation, Technische Universit\u00e4t M\u00fcnchen]."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1109\/TGRS.2009.2029339","article-title":"Ray-Tracing Simulation Techniques for Understanding High-Resolution SAR Images","volume":"48","author":"Auer","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","unstructured":"Whitted, T. (2022, November 08). An Improved Illumination Model for Shaded Display. ACM Siggraph 2005 Courses 2005, 4-es. Available online: https:\/\/dl.acm.org\/doi\/abs\/10.1145\/1198555.1198743?casa_token=ZbzPioz44b0AAAAA:zzFPIwPE7A6sxS2AzuxUfGNyV9l6H7x7XcDKqkTSQivavwXtxsA63_HC8H8EAIGBPfO9hbUrS5BeYMQ."},{"key":"ref_46","unstructured":"Glassner, A.S. (1989). An Introduction to Ray Tracing, Morgan Kaufmann."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_48","unstructured":"Huo, J., Shi, T., and Chang, J. (2016, January 26\u201328). Comparison of Random Forest and SVM for Electrical Short-Term Load Forecast with Different Data Sources. Proceedings of the 2016 7th IEEE International conference on software engineering and service science (ICSESS), Beijing, China."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.oregeorev.2015.01.001","article-title":"Machine Learning Predictive Models for Mineral Prospectivity: An Evaluation of Neural Networks, Random Forest, Regression Trees and Support Vector Machines","volume":"71","year":"2015","journal-title":"Ore Geol. Rev."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Auer, S., Bamler, R., and Reinartz, P. (2016, January 10\u201315). RaySAR-3D SAR Simulator: Now Open Source. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730757"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6154\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:34:01Z","timestamp":1760146441000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6154"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"references-count":50,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14236154"],"URL":"https:\/\/doi.org\/10.3390\/rs14236154","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,5]]}}}