{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T15:57:37Z","timestamp":1778774257117,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,14]],"date-time":"2020-02-14T00:00:00Z","timestamp":1581638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011099","name":"Deutsche Gesellschaft f\u00fcr Internationale Zusammenarbeit","doi-asserted-by":"publisher","award":["81220843"],"award-info":[{"award-number":["81220843"]}],"id":[{"id":"10.13039\/501100011099","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Flood duration is a crucial parameter for disaster impact assessment as it can directly influence the degree of economic losses and damage to structures. It also provides an indication of the spatio-temporal persistence and the evolution of inundation events. Thus, it helps gain a better understanding of hydrological conditions and surface water availability and provides valuable insights for land-use planning. The objective of this work is to develop an automatic procedure to estimate flood duration and the uncertainty associated with the use of multi-temporal flood extent masks upon which the procedure is based. To ensure sufficiently high observation frequencies, data from multiple satellites, namely Sentinel-1, Sentinel-2, Landsat-8 and TerraSAR-X, are analyzed. Satellite image processing and analysis is carried out in near real-time with an integrated system of dedicated processing chains for the delineation of flood extents from the range of aforementioned sensors. The skill of the proposed method to support satellite-based emergency mapping activities is demonstrated on two cases, namely the 2019 flood in Sofala, Mozambique and the 2017 flood in Bihar, India.<\/jats:p>","DOI":"10.3390\/rs12040643","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T03:20:03Z","timestamp":1582168803000},"page":"643","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Michaela","family":"R\u00e4ttich","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), 82234 Oberpfaffenhofen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandro","family":"Martinis","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), 82234 Oberpfaffenhofen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Wieland","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), 82234 Oberpfaffenhofen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"529","DOI":"10.5194\/nhess-11-529-2011","article-title":"An algorithm for operational flood mapping from synthetic aperture radar (SAR) data based on the fuzzy logic","volume":"11","author":"Pulvirenti","year":"2011","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_2","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":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s11069-007-9197-z","article-title":"Flood hazard in Hunan province of China: An economic loss analysis","volume":"47","author":"Huang","year":"2008","journal-title":"Nat. Hazards"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1023\/B:NHAZ.0000037035.65105.95","article-title":"Application of remote sensing in flood management with special reference to monsoon Asia: A review","volume":"33","author":"Sanyal","year":"2004","journal-title":"Nat. Hazards"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Thenkabail, P. (2015). Flood studies using Synthetic Aperture Radar data. Remote Sensing Handbook Volume III - Remote Sensing of Water Resources, Disasters, and Urban Studies, Taylor and Francis.","DOI":"10.1201\/b19321"},{"key":"ref_6","unstructured":"Wallemarq, P., Below, R., and McLean, D. (2018). UNISDR and CRED Report: Economic Losses, Poverty & Disasters (1998\u20132017), Centre for Research on the Epidemiology of Disasters (CRED)."},{"key":"ref_7","unstructured":"Hellmuth, M.E., Osgood, D.E., Hess, U., Moorhead, A., and Bhojwani, H. (2009). Index Insurance and Climate Risk: Prospects for Development and Disaster Management, International Research Institute for Climate and Society (IRI), Columbia University."},{"key":"ref_8","unstructured":"Chuvieco, E. (1997). A Review of Remote Sensing Methods for the Study of Large Wildland Fires, Universidad de Alcal\u00e1. Megafires Project ENV-CT96-0256."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/17538947.2015.1026420","article-title":"A global, high-resolution (30-m) inland water body dataset for 2000: First results of a topographic\u2013spectral classification algorithm","volume":"9","author":"Feng","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Dong, J., Xiao, X., Xiao, T., Yang, Z., Zhao, G., Zou, Z., and Qin, Y. (2017). Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors. Water, 9.","DOI":"10.3390\/w9040256"},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4909","DOI":"10.1109\/JSTARS.2017.2735443","article-title":"Surface Water Mapping by Deep Learning","volume":"10","author":"Isikdogan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fan, R., Yang, X., Wang, J., and Latif, A. (2018). Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning. Water, 10.","DOI":"10.3390\/w10050585"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wieland, M., and Martinis, S. (2019). A modular processing chain for automated flood monitoring from multi-spectral satellite data. Remote Sens., 11.","DOI":"10.3390\/rs11192330"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.2112\/JCOASTRES-D-14-00160.1","article-title":"Remote sensing of floods and flood-prone areas: An overview","volume":"314","author":"Klemas","year":"2015","journal-title":"J. Coast. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lin, L., Di, L., Yu, E.G., Kang, L., Shrestha, R., Rahman, M.S., Tang, J., Deng, M., Sun, Z., and Zhang, C. (2016, January 18\u201320). A review of remote sensing in flood assessment. Proceedings of the Fifth International Conference on Agro-Geoinformatics, Tianjin, China.","DOI":"10.1109\/Agro-Geoinformatics.2016.7577655"},{"key":"ref_19","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. EarthParts A\/B\/C"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/19475705.2016.1218943","article-title":"Satellite-based assessment of the catastrophic Jhelum floods of September 2014, Jammu & Kashmir, India","volume":"8","author":"Bhatt","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s41748-019-00123-y","article-title":"Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis","volume":"3","author":"Rahman","year":"2019","journal-title":"Earth Syst. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/JSTARS.2010.2096201","article-title":"Monitoring duration and extent of storm-surge and flooding in western coastal Louisiana marshes with Envisat ASAR data","volume":"4","author":"Ramsey","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","first-page":"44","article-title":"The agricultural impact of the 2015\u20132016 floods in Ireland as mapped through Sentinel 1 satellite imagery","volume":"58","author":"Green","year":"2019","journal-title":"Ir. J. Agric. Food Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s11069-014-1478-8","article-title":"Flood monitoring using microwave remote sensing in a part of Nuna river basin, Odisha, India","volume":"76","author":"Kundu","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_25","first-page":"37","article-title":"Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: A case study from the Kendrapara District of Orissa State of India","volume":"21","author":"Rahman","year":"2018","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yu, W., Li, G., and Zhang, H. (2016, January 10\u201315). An approach for flood inundated duration extraction based on Level Set Method using remote sensing data. Proceedings of the International Geoscience and Remote Sensing Symposium, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729467"},{"key":"ref_27","first-page":"188","article-title":"Flood hazard assessment of 2014 floods in Sonawari sub-district of Bandipore district (Jammu & Kashmir): An application of geoinformatics","volume":"4","author":"Kumar","year":"2016","journal-title":"Remote. Sen. Appl. Soc. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s002679910025","article-title":"Locational probability for a dammed, urbanizing stream: Salt River, Arizona, USA","volume":"25","author":"Graf","year":"2000","journal-title":"Environ. Manage."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kurte, K., Potnis, A., and Durbha, S. (2019, January 5). Semantics-enabled Spatio-Temporal Modeling of Earth Observation Data: An application to Flood Monitoring. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities, Chicago, IL, USA.","DOI":"10.1145\/3356395.3365545"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1111\/j.1753-318X.2010.01074.x","article-title":"Flood inundation map of Bangladesh using MODIS time-series images","volume":"3","author":"Islam","year":"2010","journal-title":"J. Flood Risk Manage."},{"key":"ref_31","unstructured":"(2019, December 16). United Nations Office for the Coordination of Humanitarian Affairs. Available online: https:\/\/www.unocha.org\/southern-and-eastern-africa-rosea\/cyclones-idai-and-kenneth."},{"key":"ref_32","unstructured":"NASA JPL (2019, December 19). NASA Shuttle Radar Topography Mission Global 1 arc second [Data set]. NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/MEaSUREs\/SRTM\/SRTMGL1.003."},{"key":"ref_33","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":"2019","journal-title":"Nat. Hazards Earth Syst. Sciences."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Martinis, S., Plank, S., and Cwik, K. (2018). The use of Sentinel-1 time-series data to improve flood monitoring in arid areas. Remote Sens., 10.","DOI":"10.3390\/rs10040583"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1080\/17538940902951401","article-title":"A new global raster water mask at 250 meter resolution","volume":"2","author":"Carroll","year":"2009","journal-title":"Int. J. Digital Earth"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5598","DOI":"10.3390\/rs5115598","article-title":"A multi-scale flood monitoring system based on fully automatic MODIS and TerraSAR-X processing chains","volume":"5","author":"Martinis","year":"2013","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2019.05.022","article-title":"Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network","volume":"230","author":"Wieland","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/4\/643\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:57:56Z","timestamp":1760173076000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/4\/643"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,14]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12040643"],"URL":"https:\/\/doi.org\/10.3390\/rs12040643","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,14]]}}}