{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:49:55Z","timestamp":1782866995380,"version":"3.54.5"},"reference-count":69,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:00:00Z","timestamp":1604361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003130","name":"Fonds Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["G.0179.16N"],"award-info":[{"award-number":["G.0179.16N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The European Space Agency\u2019s Sentinel-1 constellation provides timely and freely available dual-polarized C-band Synthetic Aperture Radar (SAR) imagery. The launch of these and other SAR sensors has boosted the field of SAR-based flood mapping. However, flood mapping in vegetated areas remains a topic under investigation, as backscatter is the result of a complex mixture of backscattering mechanisms and strongly depends on the wave and vegetation characteristics. In this paper, we present an unsupervised object-based clustering framework capable of mapping flooding in the presence and absence of flooded vegetation based on freely and globally available data only. Based on a SAR image pair, the region of interest is segmented into objects, which are converted to a SAR-optical feature space and clustered using K-means. These clusters are then classified based on automatically determined thresholds, and the resulting classification is refined by means of several region growing post-processing steps. The final outcome discriminates between dry land, permanent water, open flooding, and flooded vegetation. Forested areas, which might hide flooding, are indicated as well. The framework is presented based on four case studies, of which two contain flooded vegetation. For the optimal parameter combination, three-class F1 scores between 0.76 and 0.91 are obtained depending on the case, and the pixel- and object-based thresholding benchmarks are outperformed. Furthermore, this framework allows an easy integration of additional data sources when these become available.<\/jats:p>","DOI":"10.3390\/rs12213611","type":"journal-article","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T09:09:32Z","timestamp":1604394572000},"page":"3611","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7905-4882","authenticated-orcid":false,"given":"Lisa","family":"Landuyt","sequence":"first","affiliation":[{"name":"Hydro-Climate Extremes Lab (H-CEL), Ghent University, Coupure Links 653, 9000 Ghent, Belgium"},{"name":"Remote Sensing|Spatial Analysis Lab (REMOSA), Ghent University, Coupure Links 653, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4116-8881","authenticated-orcid":false,"given":"Niko E. C.","family":"Verhoest","sequence":"additional","affiliation":[{"name":"Hydro-Climate Extremes Lab (H-CEL), Ghent University, Coupure Links 653, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3161-2144","authenticated-orcid":false,"given":"Frieke M. B.","family":"Van Coillie","sequence":"additional","affiliation":[{"name":"Remote Sensing|Spatial Analysis Lab (REMOSA), Ghent University, Coupure Links 653, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"ref_1","unstructured":"Centre for Research on the Epidemiology of Disasters (CRED), and United Nations Office for Disaster Risk Reduction (UNISDR) (2020, July 30). The Human Cost of Weather-Related Disasters 1995\u20132015. Available online: https:\/\/www.cred.be\/sites\/default\/files\/HCWRD_2015.pdf."},{"key":"ref_2","unstructured":"Centre for Research on the Epidemiology of Disasters (CRED) (2020, July 30). Natural Disasters 2019. Available online: https:\/\/emdat.be\/sites\/default\/files\/adsr_2019.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1038\/415514a","article-title":"Increasing risk of great floods in a changing climate","volume":"415","author":"Milly","year":"2002","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1126\/science.aad8728","article-title":"Global trends in satellite-based emergency mapping","volume":"353","author":"Voigt","year":"2016","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4870","DOI":"10.3390\/rs6064870","article-title":"Rapid Damage Assessment by Means of Multi-Temporal SAR\u2014A Comprehensive Review and Outlook to Sentinel-1","volume":"6","author":"Plank","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","unstructured":"Van Wesemael, A., Verhoest, N.E.C., and Lievens, H. (2019). Assessing the Value of Remote Sensing and In Situ Data for Flood Inundation Forecasts. [Ph.D. Thesis, Ghent University]."},{"key":"ref_7","unstructured":"Woodhouse, I.H. (2005). Introduction to Microwave Remote Sensing, CRC Press."},{"key":"ref_8","unstructured":"Meyer, F. (2019). Spaceborne Synthetic Aperture Radar: Principles, Data Access, and Basic Processing Techniques. The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation, NASA. Chapter 1."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Adeli, S., Salehi, B., Mahdianpari, M., Quackenbush, L.J., Brisco, B., Tamiminia, H., and Shaw, S. (2020). Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review. Remote Sens., 12.","DOI":"10.3390\/rs12142190"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1007\/s10712-016-9378-y","article-title":"Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges","volume":"37","author":"Grimaldi","year":"2016","journal-title":"Surv. Geophys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1109\/TGRS.2009.2017937","article-title":"The Utility of Spaceborne Radar to Render Flood Inundation Maps Based on Multialgorithm Ensembles","volume":"47","author":"Schumann","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"035002","DOI":"10.1088\/1748-9326\/9\/3\/035002","article-title":"Flood extent mapping for Namibia using change detection and thresholding with SAR","volume":"9","author":"Long","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_15","first-page":"123","article-title":"An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data","volume":"73","author":"Li","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.rse.2018.06.019","article-title":"Towards operational SAR-based flood mapping using neuro-fuzzy texture-based approaches","volume":"215","author":"Dasgupta","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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_18","first-page":"77","article-title":"Probabilistic mapping of flood-induced backscatter changes in SAR time series","volume":"56","author":"Schlaffer","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tsyganskaya, V., Martinis, S., Marzahn, P., and Ludwig, R. (2018). Detection of temporary flooded vegetation using Sentinel-1 time series data. Remote Sens., 10.","DOI":"10.3390\/rs10081286"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rinp.2018.02.054","article-title":"On the merging of optical and SAR satellite imagery for surface water mapping applications","volume":"9","author":"Markert","year":"2018","journal-title":"Results Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111664","DOI":"10.1016\/j.rse.2020.111664","article-title":"Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine","volume":"240","author":"DeVries","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, W., DeVries, B., Huang, C., Lang, M., Jones, J., Creed, I., and Carroll, M. (2018). Automated Extraction of Surface Water Extent from Sentinel-1 Data. Remote Sens., 10.","DOI":"10.3390\/rs10050797"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bonafilia, D., Tellman, B., Anderson, T., and Issenberg, E. (2020, January 14\u201319). Sen1Floods11: A Georeferenced Dataset to Train and Test Deep Learning Flood Algorithms for Sentinel-1. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00113"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pierdicca, N., Pulvirenti, L., and Chini, M. (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_7"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.12.045011","article-title":"Robust algorithm for detecting floodwater in urban areas using synthetic aperture radar images","volume":"12","author":"Mason","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Li, Y., Martinis, S., Wieland, M., Schlaffer, S., and Natsuaki, R. (2019). Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion. Remote Sens., 11.","DOI":"10.3390\/rs11192231"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"82","DOI":"10.5589\/m11-017","article-title":"Evaluation of C-band polarization diversity and polarimetry for wetland mapping","volume":"37","author":"Brisco","year":"2011","journal-title":"Can. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1080\/01431161.2017.1420938","article-title":"SAR-based detection of flooded vegetation\u2014A review of characteristics and approaches","volume":"39","author":"Tsyganskaya","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.rse.2006.11.012","article-title":"Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data","volume":"108","author":"Martinez","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s11273-014-9381-3","article-title":"Mapping wetlands in the Hudson Highlands ecoregion with ALOS PALSAR: An effort to identify potential swamp forest habitat for golden-winged warblers","volume":"23","author":"Pistolesi","year":"2015","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1080\/01431161.2019.1673915","article-title":"The contribution of ALOS\/PALSAR-1 multi-temporal data to map permanently and temporarily flooded coastal wetlands","volume":"41","author":"Morandeira","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1109\/JSTARS.2010.2089042","article-title":"Using ALOS\/PALSAR and RADARSAT-2 to Map Land Cover and Seasonal Inundation in the Brazilian Pantanal","volume":"3","author":"Evans","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1109\/JSTARS.2013.2283340","article-title":"Flood Mapping with TerraSAR-X in Forested Regions in Estonia","volume":"7","author":"Voormansik","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hess, L.L., and Melack, J.M. (2003). Remote sensing of vegetation and flooding on Magela Creek Floodplain (Northern Territory, Australia) with the SIR-C synthetic aperture radar. Aquatic Biodiversity: A Celebratory Volume in Honour of Henri J. Dumont, Springer.","DOI":"10.1007\/978-94-007-1084-9_4"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3898","DOI":"10.1016\/j.rse.2008.06.013","article-title":"Influence of incidence angle on detecting flooded forests using C-HH synthetic aperture radar data","volume":"112","author":"Lang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1080\/01431168708954756","article-title":"An explanation of enhanced radar backscattering from flooded forests","volume":"8","author":"Richards","year":"1987","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/S0034-4257(00)00164-4","article-title":"Multisensor Hydrologic Assessment of a Freshwater Wetland","volume":"75","author":"Pietroniro","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Refice, A., Zingaro, M., D\u2019Addabbo, A., and Chini, M. (2020). Integrating C- and L-Band SAR Imagery for Detailed Flood Monitoring of Remote Vegetated Areas. Water, 10.","DOI":"10.3390\/w12102745"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7732","DOI":"10.3390\/rs70607732","article-title":"Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany","volume":"7","author":"Martinis","year":"2015","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/07038992.2019.1612236","article-title":"Evaluation of C-Band SAR for Identification of Flooded Vegetation in Emergency Response Products","volume":"45","author":"Brisco","year":"2019","journal-title":"Can. J. Remote Sens."},{"key":"ref_43","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_44","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_45","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1080\/2150704X.2015.1066520","article-title":"A method for monitoring hydrological conditions beneath herbaceous wetlands using multi-temporal ALOS PALSAR coherence data","volume":"6","author":"Zhang","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1080\/17538947.2011.608813","article-title":"SAR polarimetric change detection for flooded vegetation","volume":"6","author":"Brisco","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1080\/01431161.2017.1306143","article-title":"Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2\/PALSAR-2 imagery","volume":"38","author":"Plank","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tsyganskaya, V., Martinis, S., and Marzahn, P. (2019). Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features. Water, 11.","DOI":"10.3390\/w11091938"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Olthof, I., and Tolszczuk-Leclerc, S. (2018). Comparing Landsat and RADARSAT for Current and Historical Dynamic Flood Mapping. Remote Sens., 10.","DOI":"10.3390\/rs10050780"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2650","DOI":"10.1109\/JSTARS.2017.2711960","article-title":"Mapping Flooded Vegetation Using COSMO-SkyMed: Comparison With Polarimetric and Optical Data Over Rice Fields","volume":"10","author":"Pierdicca","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","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_52","unstructured":"International Federation of Red Cross (IFRC), and Red Crescent Societies (2019). Ghana: Floods in Upper East Region\u2014Emergency Plan of Action Final Report, IFRC."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Campany\u00e0 i Llovet, J., McCormack, T., and Naughton, O. (2020, January 4\u20138). Remote Sensing for Monitoring and Mapping Karst Groundwater Flooding in the Republic of Ireland. Proceedings of the EGU General Assembly 2020, Vienna, Austria.","DOI":"10.5194\/egusphere-egu2020-18921"},{"key":"ref_54","unstructured":"Copernicus Emergency Management Service (\u00a92015 European Union), EMSR149."},{"key":"ref_55","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_56","first-page":"205","article-title":"Change detection approaches for flood extent mapping: How to select the most adequate reference image from online archives?","volume":"19","author":"Hostache","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/LGRS.2015.2409982","article-title":"First Results From the Phenology-Based Synthesis Classifier Using Landsat 8 Imagery","volume":"12","author":"Simonetti","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1029\/2008EO100001","article-title":"New Global Hydrography Derived From Spaceborne Elevation Data","volume":"89","author":"Lehner","year":"2008","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Soatto, S. (2008). Quick shift and kernel methods for mode seeking. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-540-88693-8_52"},{"key":"ref_60","first-page":"100","article-title":"Algorithm AS 136: A k-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J. R. Stat. Soc. Ser. C Appl. Stat."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2018.2860054","article-title":"Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches","volume":"57","author":"Landuyt","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Debusscher, B., and Van Coillie, F. (2019). Object-Based Flood Analysis Using a Graph-Based Representation. Remote Sens., 11.","DOI":"10.3390\/rs11161883"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4928","DOI":"10.1002\/hyp.9979","article-title":"Problems with binary pattern measures for flood model evaluation","volume":"28","author":"Stephens","year":"2014","journal-title":"Hydrol. Process."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1080\/01431160600993447","article-title":"SRTM DEM accuracy assessment over vegetated areas in Norway","volume":"28","author":"Weydahl","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Olthof, I., and Rainville, T. (2020). Evaluating Simulated RADARSAT Constellation Mission (RCM) Compact Polarimetry for Open-Water and Flooded-Vegetation Wetland Mapping. Remote Sens., 12.","DOI":"10.3390\/rs12091476"},{"key":"ref_66","unstructured":"Jet Propulsion Laboratory (JPL) (2020, September 01). NISAR: Mission Concept, Available online: https:\/\/nisar.jpl.nasa.gov\/mission\/mission-concept\/."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Pierdicca, N., Davidson, M., Chini, M., Dierking, W., Djavidnia, S., Haarpaintner, J., Hajduch, G., Laurin, G.V., Lavalle, M., and L\u00f3pez-Mart\u00ednez, C. (2019, January 9\u201312). The Copernicus L-band SAR mission ROSE-L (Radar Observing System for Europe) (Conference Presentation). Proceedings of the SPIE Remote Sensing\u2014Active and Passive Microwave Remote Sensing for Environmental Monitoring, Strasbourg, France.","DOI":"10.1117\/12.2534743"},{"key":"ref_68","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_69","doi-asserted-by":"crossref","unstructured":"Esch, T., Bachofer, F., Heldens, W., Hirner, A., Marconcini, M., Palacios-Lopez, D., Roth, A., \u00dcreyen, S., Zeidler, J., and Dech, S. (2018). Where We Live\u2014A Summary of the Achievements and Planned Evolution of the Global Urban Footprint. Remote Sens., 10.","DOI":"10.3390\/rs10060895"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3611\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:28:42Z","timestamp":1760178522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3611"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,3]]},"references-count":69,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12213611"],"URL":"https:\/\/doi.org\/10.3390\/rs12213611","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,3]]}}}