{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T14:27:08Z","timestamp":1780583228293,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T00:00:00Z","timestamp":1622764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFC1505006"],"award-info":[{"award-number":["2018YFC1505006"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41977246"],"award-info":[{"award-number":["No. 41977246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Outburst floods resulting from giant landslide dams can cause devastating damage to hundreds or thousands of kilometres of a river. Accurate and timely delineation of flood inundated areas is essential for disaster assessment and mitigation. There have been significant advances in flood mapping using remote sensing images in recent years, but little attention has been devoted to outburst flood mapping. The short-duration nature of these events and observation constraints from cloud cover have significantly challenged outburst flood mapping. This study used the outburst flood of the Baige landslide dam on the Jinsha River on 3 November 2018 as an example to propose a new flood mapping method that combines optical images from Sentinel-2, synthetic aperture radar (SAR) images from Sentinel-1 and a Digital Elevation Model (DEM). First, in the cloud-free region, a comparison of four spectral indexes calculated from time series of Sentinel-2 images indicated that the normalized difference vegetation index (NDVI) with the threshold of 0.15 provided the best separation flooded area. Subsequently, in the cloud-covered region, an analysis of dual-polarization RGB false color composites images and backscattering coefficient differences of Sentinel-1 SAR data were found an apparent response to ground roughness\u2019s changes caused by the flood. We carried out the flood range prediction model based on the random forest algorithm. Training samples consisted of 13 feature vectors obtained from the Hue-Saturation-Value color space, backscattering coefficient differences\/ratio, DEM data, and a label set from the flood range prepared from Sentinel-2 images. Finally, a field investigation and confusion matrix tested the prediction accuracy of the end-of-flood map. The overall accuracy and Kappa coefficient were 92.3%, 0.89 respectively. The full extent of the outburst floods was successfully obtained within five days of its occurrence. The multi-source data merging framework and the massive sample preparation method with SAR images proposed in this paper, provide a practical demonstration for similar machine learning applications using remote sensing.<\/jats:p>","DOI":"10.3390\/rs13112205","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T01:56:40Z","timestamp":1623031000000},"page":"2205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Mapping Outburst Floods Using a Collaborative Learning Method Based on Temporally Dense Optical and SAR Data: A Case Study with the Baige Landslide Dam on the Jinsha River, Tibet"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhongkang","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, Chengdu 610065, China"},{"name":"College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinbing","family":"Wei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, Chengdu 610065, China"},{"name":"College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0476-0253","authenticated-orcid":false,"given":"Jianhui","family":"Deng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, Chengdu 610065, China"},{"name":"College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunjian","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, Chengdu 610065, China"},{"name":"College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, Chengdu 610065, China"},{"name":"College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiliang","family":"He","sequence":"additional","affiliation":[{"name":"College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"},{"name":"School of Environment and Resource, Southwest University of Science & Technology, Mianyang 621010, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Baker, V.R. (2013). 9.26 Global Late Quaternary Fluvial Paleohydrology: With Special Emphasis on Paleofloods and Megafloods. Treatise Geomorphol., 511\u2013527.","DOI":"10.1016\/B978-0-12-374739-6.00252-9"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1029\/2018RG000626","article-title":"Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts","volume":"57","author":"Fan","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s10346-014-0553-2","article-title":"Comprehensive analyses of the initiation and entrainment processes of the 2000 Yigong catastrophic landslide in Tibet, China","volume":"13","author":"Zhou","year":"2015","journal-title":"Landslides"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.1007\/s10346-020-01398-3","article-title":"Back analysis of breaching process of Baige landslide dam","volume":"17","author":"Zhong","year":"2020","journal-title":"Landslides"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102895","DOI":"10.1016\/j.earscirev.2019.102895","article-title":"Outburst floods in China: A review","volume":"197","author":"Liu","year":"2019","journal-title":"Earth Sci. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Markert, K.N., Markert, A.M., Mayer, T., Nauman, C., Haag, A., Poortinga, A., Bhandari, B., Thwal, N.S., Kunlamai, T., and Chishtie, F. (2020). Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12152469"},{"key":"ref_7","first-page":"150","article-title":"A lake detection algorithm (LDA) using Landsat 8 data: A comparative approach in glacial environment","volume":"38","author":"Bhardwaj","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","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_10","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_11","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2013.08.029","article-title":"Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery","volume":"140","author":"Feyisa","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1364\/JOT.81.000504","article-title":"Technique for calculating the effective scattering area of diffusely reflecting objects of complex shape","volume":"81","author":"Potapova","year":"2014","journal-title":"J. Opt. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cao, H., Zhang, H., Wang, C., and Zhang, B. (2019). Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas. Water, 11.","DOI":"10.3390\/w11040786"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.ijleo.2016.11.040","article-title":"Gabor filter based change detection in SAR images by KI thresholding","volume":"130","author":"Sumaiya","year":"2017","journal-title":"Optik"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/LGRS.2012.2189867","article-title":"A Novel SAR Image Change Detection Based on Graph-Cut and Generalized Gaussian Model","volume":"10","author":"Zhang","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"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":"522","DOI":"10.1111\/jfr3.12180","article-title":"Application of the design variable method to estimate coastal flood risk","volume":"10","author":"Zheng","year":"2015","journal-title":"J. Flood Risk Manag."},{"key":"ref_19","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_20","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_21","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_22","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_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.rse.2019.01.017","article-title":"A highly automated algorithm for wetland detection using multi-temporal optical satellite data","volume":"224","author":"Ludwig","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.3390\/rs2071751","article-title":"Wide Area Wetland Mapping in Semi-Arid Africa Using 250-Meter MODIS Metrics and Topographic Variables","volume":"2","author":"Landmann","year":"2010","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.rse.2014.10.015","article-title":"Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data","volume":"158","author":"Lehner","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"180309","DOI":"10.1038\/sdata.2018.309","article-title":"GFPLAIN250m, a global high-resolution dataset of Earth\u2019s floodplains","volume":"6","author":"Nardi","year":"2019","journal-title":"Sci. Data"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chakhar, A., Hern\u00e1ndez-L\u00f3pez, D., Ballesteros, R., and Moreno, M.A. (2021). Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data. Remote Sens., 13.","DOI":"10.3390\/rs13020243"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Annis, A., Nardi, F., Petroselli, A., Apollonio, C., Arcangeletti, E., Tauro, F., Belli, C., Bianconi, R., and Grimaldi, S. (2020). UAV-DEMs for Small-Scale Flood Hazard Mapping. Water, 12.","DOI":"10.3390\/w12061717"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"258","DOI":"10.3844\/jcssp.2019.258.268","article-title":"Multi-Temporal Sentinel-2 Images for Classification Accuracy","volume":"15","author":"Yuhendra","year":"2019","journal-title":"J. Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"136","DOI":"10.7809\/b-e.00315","article-title":"Monitoring flood and drought events\u2014Earth observation for multiscale assessment of water-related hazards and exposed elements","volume":"6","author":"Hipondoka","year":"2018","journal-title":"Biodivers. Ecol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, W., DeVries, B., Huang, C., Lang, M.W., Jones, J.W., Creed, I.F., and Carroll, M.L. (2018). Automated Extraction of Surface Water Extent from Sentinel-1 Data. Remote Sens., 10.","DOI":"10.3390\/rs10050797"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"012013","DOI":"10.1088\/1755-1315\/479\/1\/012013","article-title":"Flood mapping using Sentinel-1 SAR Imagery: Case study of the November 2017 flood in Penang","volume":"479","author":"Saleh","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.envsoft.2018.01.023","article-title":"A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms","volume":"104","author":"Whyte","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Maskooni, E.K., Naghibi, S., Hashemi, H., and Berndtsson, R. (2020). Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data. Remote Sens., 12.","DOI":"10.3390\/rs12172742"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103116","DOI":"10.1016\/j.earscirev.2020.103116","article-title":"The formation and impact of landslide dams\u2014State of the art","volume":"203","author":"Fan","year":"2020","journal-title":"Earth Sci. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3475","DOI":"10.1007\/s10064-021-02146-0","article-title":"Kinematic process and mechanism of the two slope failures at Baige Village in the upper reaches of the Jinsha River, China","volume":"80","author":"Chen","year":"2021","journal-title":"Bull. Int. Assoc. Eng. Geol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s10346-019-01313-5","article-title":"Prediction of a multi-hazard chain by an integrated numerical simulation approach: The Baige landslide, Jinsha River, China","volume":"17","author":"Fan","year":"2019","journal-title":"Landslides"},{"key":"ref_42","first-page":"102009","article-title":"Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa","volume":"86","author":"Slagter","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1080\/01431161.2017.1285503","article-title":"Flood mapping in the lower Mekong River Basin using daily MODIS observations","volume":"38","author":"Fayne","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2018.09.016","article-title":"Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery","volume":"219","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Xu, X., Chen, C., and Yang, D. (2013). Color Image Segmentation Algorithm of Rapid Level Sets Based on HSV Color Space. Lect. Notes Electr. Eng., 483\u2013489.","DOI":"10.1007\/978-1-4471-4844-9_65"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Esfandiari, M., Abdi, G., Jabari, S., McGrath, H., and Coleman, D. (2020). Flood Hazard Risk Mapping Using a Pseudo Supervised Random Forest. Remote Sens., 12.","DOI":"10.3390\/rs12193206"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s10994-013-5346-7","article-title":"Using random forests to diagnose aviation turbulence","volume":"95","author":"Williams","year":"2014","journal-title":"Mach. Learn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2014.01.005","article-title":"Classification of dual- and single polarized SAR images by incorporating visual features","volume":"90","author":"Uhlmann","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","first-page":"102164","article-title":"Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data","volume":"92","author":"Cai","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"6446","DOI":"10.1080\/01431161.2012.689116","article-title":"Assessment of specular radar backscatter from a planar surface using a physical optics approach","volume":"33","author":"Yurchak","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2019.02.027","article-title":"Roughness and vegetation change detection: A pre-processing for soil moisture retrieval from multi-temporal SAR imagery","volume":"225","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","unstructured":"R\u00fcetschi, M., Small, D., and Waser, L.T. (2019). Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11020115"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2205\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:11:06Z","timestamp":1760163066000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,4]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112205"],"URL":"https:\/\/doi.org\/10.3390\/rs13112205","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,4]]}}}