{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:22:00Z","timestamp":1774628520213,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,18]],"date-time":"2018-05-18T00:00:00Z","timestamp":1526601600000},"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>Mapping the historical occurrence of flood water in time and space provides information that can be used to help mitigate damage from future flood events. In Canada, flood mapping has been performed mainly from RADARSAT imagery in near real-time to enhance situational awareness during an emergency, and more recently from Landsat to examine historical surface water dynamics from the mid-1980s to present. Here, we seek to integrate the two data sources for both operational and historical flood mapping. A main challenge of a multi-sensor approach is ensuring consistency between surface water mapped from sensors that fundamentally interact with the target differently, particularly in areas of flooded vegetation. In addition, automation of workflows that previously relied on manual interpretation is increasingly needed due to large data volumes contained within satellite image archives. Despite differences between data received from both sensors, common approaches to surface water and flooded vegetation mapping including multi-channel classification and region growing can be applied with sensor-specific adaptations for each. Historical open water maps from 202 Landsat scenes spanning the years 1985\u20132016 generated previously were enhanced to improve flooded vegetation mapping along the Saint John River in New Brunswick, Canada. Open water and flooded vegetation maps were created over the same region from 181 RADARSAT 1 and 2 scenes acquired between 2003\u20132016. Comparisons of maps from different sensors and hydrometric data were performed to examine consistency and robustness of products derived from different sensors. Simulations reveal that the methodology used to map open water from dual-pol RADARSAT 2 is insensitive to up to about 20% training error. Landsat depicts open water inundation well, while flooded vegetation can be reliably mapped in leaf-off conditions. RADARSAT mapped approximately 8% less open water area than Landsat and 0.5% more flooded vegetation, while the combined area of open water and flooded vegetation agreed to within 0.2% between sensors. Derived historical products depicting inundation frequency and trends were also generated from each sensor\u2019s time-series of surface water maps and compared.<\/jats:p>","DOI":"10.3390\/rs10050780","type":"journal-article","created":{"date-parts":[[2018,5,21]],"date-time":"2018-05-21T04:07:30Z","timestamp":1526875650000},"page":"780","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Comparing Landsat and RADARSAT for Current and Historical Dynamic Flood Mapping"],"prefix":"10.3390","volume":"10","author":[{"given":"Ian","family":"Olthof","sequence":"first","affiliation":[{"name":"Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 560 Rochester St, Ottawa, ON K1S 5K2, Canada"}]},{"given":"Simon","family":"Tolszczuk-Leclerc","sequence":"additional","affiliation":[{"name":"Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 560 Rochester St, Ottawa, ON K1S 5K2, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Intergovernmental Panel on Climate Change (2014). Climate Change 2013\u2014The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.","DOI":"10.1017\/CBO9781107415324"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4545","DOI":"10.1080\/01431161.2010.489064","article-title":"Landsat mapping of annual inundation (1979\u20132006) of the Macquarie Marshes in semi-arid Australia","volume":"32","author":"Thomas","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Olthof, I. (2017). Mapping Seasonal Inundation Frequency (1985\u20132016) along the St-John River, New Brunswick, Canada using the Landsat Archive. Remote Sens., 9.","DOI":"10.3390\/rs9020143"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.rse.2015.11.003","article-title":"Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia","volume":"174","author":"Mueller","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2014GL060641","DOI":"10.1002\/2014GL060641","article-title":"A global inventory of lakes based on high-resolution satellite imagery","volume":"41","author":"Verpoorter","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2015.10.014","article-title":"Development of a global ~90 m water body map using multi-temporal Landsat images","volume":"171","author":"Yamazaki","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"L08403","DOI":"10.1029\/2012GL051276","article-title":"Changes in land surface water dynamics since the 1990s and relation to population pressure","volume":"39","author":"Prigent","year":"2012","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"336","DOI":"10.5589\/m09-025","article-title":"A semi-automated tool for surface water mapping with Radarsat-1","volume":"35","author":"Brisco","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1080\/01431160110114484","article-title":"An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data","volume":"23","author":"Wang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Yu, Y., and Saatchi, S. (2016). Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests. Remote Sens., 8.","DOI":"10.3390\/rs8060522"},{"key":"ref_13","unstructured":"Mackey, H.E., and Riley, R.S. (1994). Mapping of flood patterns in a 10,000-acre southeastern river swamp with SPOT HRV data. ASPRS\/ACSM Annual Convention and Exposition, ASPRS. Technical Paper 1."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/S0169-555X(97)00069-X","article-title":"Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing","volume":"21","author":"Townsend","year":"1998","journal-title":"Gemorphology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s13157-012-0359-8","article-title":"Topographic Metrics for Improved Mapping of Forested Wetlands","volume":"33","author":"Lang","year":"2013","journal-title":"Wetlands"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2016.01.023","article-title":"A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance","volume":"176","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7615","DOI":"10.3390\/rs70607615","article-title":"A Collection of SAR Methodologies for Monitoring Wetlands","volume":"7","author":"White","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","unstructured":"Bolanos, S., Stiff, D., Brisco, B., and Pietroniro, A. (2016). Operational surface water detection and monitoring using RADARSAT 2. Remote Sens., 8.","DOI":"10.3390\/rs8040285"},{"key":"ref_22","first-page":"42","article-title":"Relationship of local incidence angle with satellite radar backscatter for different surface conditions","volume":"24","author":"Leblanc","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1080\/01431161.2015.1009653","article-title":"An automatic method for mapping inland surface waterbodies with RADARSAT-2 imagery","volume":"36","author":"Li","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","unstructured":"Woodhouse, I.H. (2005). Introduction to Microwave Remote Sensing, CRC Press."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1080\/01431160500486724","article-title":"Envisat multi-polarized ASAR data for flood mapping","volume":"27","author":"Henry","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","first-page":"135","article-title":"RADARSAT-2 Beam Mode Selection for Surface Water and Flooded Vegetation Mapping","volume":"40","author":"White","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_27","unstructured":"Schowengerdt, R.A. (2006). Remote Sensing, Third Edition: Models and Methods for Image Processing, Academic Press. [3rd ed.]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(01)00208-5","article-title":"Forest mapping with a generalized classifier and Landsat TM data","volume":"77","author":"Woodcock","year":"2001","journal-title":"Remote Sens. Envion."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2557","DOI":"10.1109\/TGRS.2003.818367","article-title":"Monitoring forest succession with multitemporal Landsat images: Factors of uncertainty","volume":"41","author":"Song","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"687","DOI":"10.3390\/rs5020687","article-title":"Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses","volume":"5","author":"Kuenzer","year":"2013","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_34","unstructured":"Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"16398","DOI":"10.3390\/rs71215841","article-title":"Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Latifovic, R., Pouliot, D., and Olthof, I. (2017). Circa 2010 land cover of Canada: Local optimization methodology and product development. Remote Sens., 9.","DOI":"10.3390\/rs9111098"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/S0034-4257(96)00148-4","article-title":"The use of imaging radars for ecological applications\u2014A review","volume":"39","author":"Kasischke","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Manwani, N., and Sastry, P.S. (arXiv, 2016). On the Robustness of Decision Tree Learning under Label Noise, arXiv.","DOI":"10.1007\/978-3-319-57454-7_53"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1038\/386698a0","article-title":"Increased plant growth in the northern high latitudes from 1981 to 1991","volume":"386","author":"Myneni","year":"1997","journal-title":"Nature"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1080\/01431160802302090","article-title":"Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985\u20132006","volume":"30","author":"Pouliot","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.rse.2015.07.001","article-title":"Landsat-based mapping of thermokarst lake dynamics on the Tuktoyaktuk Coastal Plain, Northwest Territories, Canada since 1985","volume":"168","author":"Olthof","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"4646","article-title":"Analysis of a multiyear global vegetation leaf area index data set","volume":"107","author":"Buermann","year":"2002","journal-title":"J. Geophys. Res."},{"key":"ref_44","first-page":"1","article-title":"Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing","volume":"9","author":"Pradhan","year":"2010","journal-title":"J. Spat. Hydrol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1080\/01431160412331331012","article-title":"Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data","volume":"26","author":"Lee","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","unstructured":"Kendall, M.G., and Stuart, A. (1967). Influence and Relationship. The Advanced Theory of Statistics, Griffin."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Olthof, I., Tolszczuk-Leclerc, S., Lehrbass, B., Shelat, Y., Neufeld, V., and Decker, V. (2018). New Flood Mapping Methods Implemented during the 2017 Spring Flood Activation in Southern Quebec, Natural Resources Canada. Open File 38.","DOI":"10.4095\/306577"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/780\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:04:51Z","timestamp":1760195091000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,18]]},"references-count":47,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["rs10050780"],"URL":"https:\/\/doi.org\/10.3390\/rs10050780","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,18]]}}}