{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T02:37:44Z","timestamp":1774319864809,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,9]],"date-time":"2018-04-09T00:00:00Z","timestamp":1523232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the similarity of the radar backscatter over open water and over sand surfaces a reliable near real-time flood mapping based on satellite radar sensors is usually not possible in arid areas. Within this study, an approach is presented to enhance the results of an automatic Sentinel-1 flood processing chain by removing overestimations of the water extent related to low-backscattering sand surfaces using a Sand Exclusion Layer (SEL) derived from time-series statistics of Sentinel-1 data sets. The methodology was tested and validated on a flood event in May 2016 at Webi Shabelle River, Somalia and Ethiopia, which has been covered by a time-series of 202 Sentinel-1 scenes within the period June 2014 to May 2017. The approach proved capable of significantly improving the classification accuracy of the Sentinel-1 flood service within this study site. The Overall Accuracy increased by ~5% to a value of 98.5% and the User\u2019s Accuracy increased by 25.2% to a value of 96.0%. Experimental results have shown that the classification accuracy is influenced by several parameters such as the lengths of the time-series used for generating the SEL.<\/jats:p>","DOI":"10.3390\/rs10040583","type":"journal-article","created":{"date-parts":[[2018,4,10]],"date-time":"2018-04-10T13:06:08Z","timestamp":1523365568000},"page":"583","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":121,"title":["The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas"],"prefix":"10.3390","volume":"10","author":[{"given":"Sandro","family":"Martinis","sequence":"first","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5793-052X","authenticated-orcid":false,"given":"Simon","family":"Plank","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2129-9577","authenticated-orcid":false,"given":"Kamila","family":"\u0106wik","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany"},{"name":"Department of Civil, Geo and Environmental Engineering, Technical University of Munich, D-80333 Munich, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,9]]},"reference":[{"key":"ref_1","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. Harzad. Earth Syst. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Schumann, G., Di Baldassarre, G., Alsdorf, D., and Bates, P.D. (2010). Near real-time flood wave approximation on large rivers from space: Application to the River Po, Italy. Water Resour. Res., 46.","DOI":"10.1029\/2008WR007672"},{"key":"ref_3","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"},{"key":"ref_4","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 using fuzzy logic","volume":"11","author":"Pulvirenti","year":"2011","journal-title":"Nat. Harzad. Earth Syst. Sci."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","unstructured":"Cao, W., Martinis, S., and Plank, S. (2017, January 23\u201328). Automatic SAR-based flood detection using hierarchical tile-ranking thresholding and fuzzy logic. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128301"},{"key":"ref_8","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_9","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_10","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":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","first-page":"317","article-title":"The International \u2018Charter Space and Major Disasters\u2019: DLR\u2019s contributions to emergency response world-wide","volume":"85","author":"Martinis","year":"2017","journal-title":"J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1080\/10106049.2011.622051","article-title":"Monitoring disasters with a constellation of satellites-type examples from the International Charter \u201cSpace and Major Disasters\u201d","volume":"27","author":"Mahmood","year":"2012","journal-title":"Geocarto Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/0031-3203(86)90030-0","article-title":"Minimum error thresholding","volume":"19","author":"Kittler","year":"1986","journal-title":"Pattern Recognit."},{"key":"ref_14","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_15","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_16","first-page":"15","article-title":"Flood detection from multi-temporal SAR data using harmonic analysis and change detection","volume":"38","author":"Schlaffer","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/JSTARS.2012.2210999","article-title":"TanDEM-X Water Indication Mask: Generation and first evaluation results","volume":"6","author":"Wendleder","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","unstructured":"Bertram, A., Wendleder, A., Schmitt, A., and Huber, M. (2017, January 12\u201319). Long-term Monitoring of water dynamics in the Sahel region using the Multi-SAR-System. Proceedings of the Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS), Prague, Czech Republic."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3475","DOI":"10.5194\/hess-15-3475-2011","article-title":"Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan","volume":"15","author":"Leblanc","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_20","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","volume":"17","author":"Westerhoff","year":"2013","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Martinis, S. (2017, January 23\u201328). Improving flood mapping in arid areas using Sentinel-1 time series data. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8126927"},{"key":"ref_22","unstructured":"(2017, September 20). Reliefweb. Available online: https:\/\/reliefweb.int\/disaster\/fl-2015-000153-irq."},{"key":"ref_23","unstructured":"(2018, March 06). FAO-SWALIM. Available online: http:\/\/www.faoswalim.org\/water\/climate-somalia."},{"key":"ref_24","unstructured":"(2018, January 03). FloodList. Available online: http:\/\/www.floodlist.com."},{"key":"ref_25","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1982). Microwave Remote Sensing: Active and Passive, Addison-Wesley Publishing Company. Volume II\u2014Radar Remote Sensing and Surface Scattering and Emission Theory."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/36.551937","article-title":"An empirical model for interpreting the relationship between backscattering and arid land surface roughness as seen with the SAR","volume":"35","author":"Deroin","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/0034-4257(96)00018-1","article-title":"Radar backscatter characteristics of a desert surface","volume":"57","author":"Ridley","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1109\/36.964990","article-title":"Radar Attenuation by Sand: Laboratory measurements of radar transmission","volume":"39","author":"Williams","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/583\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:00:07Z","timestamp":1760194807000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/583"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,9]]},"references-count":29,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040583"],"URL":"https:\/\/doi.org\/10.3390\/rs10040583","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,9]]}}}