{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T05:53:22Z","timestamp":1771998802807,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T00:00:00Z","timestamp":1579478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["4000126706\/19\/I-NB"],"award-info":[{"award-number":["4000126706\/19\/I-NB"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Spanish Ministry of Science, Innovation, and Universities","award":["FPU16\/01645"],"award-info":[{"award-number":["FPU16\/01645"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study provides a comparative analysis of two Sentinel-1 and one Sentinel-2 burned area (BA) detection and mapping algorithms over 10 test sites (100 \u00d7 100 km) in tropical and sub-tropical Africa. Depending on the site, the burned area was mapped at different time points during the 2015\u20132016 fire seasons. The algorithms relied on diverse burned area (BA) mapping strategies regarding the data used (i.e., surface reflectance, backscatter coefficient, interferometric coherence) and the detection method. Algorithm performance was compared by evaluating the detected BA agreement with reference fire perimeters independently derived from medium resolution optical imagery (i.e., Landsat 8, Sentinel-2). The commission (CE) and omission errors (OE), as well as the Dice coefficient (DC) for burned pixels, were compared. The mean OE and CE were 33% and 31% for the optical-based Sentinel-2 time-series algorithm and increased to 66% and 36%, respectively, for the radar backscatter coefficient-based algorithm. For the coherence based radar algorithm, OE and CE reached 72% and 57%, respectively. When considering all tiles, the optical-based algorithm provided a significant increase in agreement over the Synthetic Aperture Radar (SAR) based algorithms that might have been boosted by the use of optical datasets when generating the reference fire perimeters. The analysis suggested that optical-based algorithms provide for a significant increase in accuracy over the radar-based algorithms. However, in regions with persistent cloud cover, the radar sensors may provide a complementary data source for wall to wall BA detection.<\/jats:p>","DOI":"10.3390\/rs12020334","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T03:04:43Z","timestamp":1579575883000},"page":"334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0045-2299","authenticated-orcid":false,"given":"Mihai A.","family":"Tanase","sequence":"first","affiliation":[{"name":"Environmental Remote Sensing Research Group, Department of Geology, Geography and Environment, University of Alcala, 28801 Alcala de Henares, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1173-4379","authenticated-orcid":false,"given":"Miguel A.","family":"Belenguer-Plomer","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing Research Group, Department of Geology, Geography and Environment, University of Alcala, 28801 Alcala de Henares, Spain"}]},{"given":"Ekhi","family":"Roteta","sequence":"additional","affiliation":[{"name":"Department of Mining and Metallurgical Engineering and Materials Science, School of Engineering of Vitoria-Gasteiz, University of the Basque Country UPV\/EHU, 01006 Vitoria-Gasteiz, Spain"}]},{"given":"Aitor","family":"Bastarrika","sequence":"additional","affiliation":[{"name":"Department of Mining and Metallurgical Engineering and Materials Science, School of Engineering of Vitoria-Gasteiz, University of the Basque Country UPV\/EHU, 01006 Vitoria-Gasteiz, Spain"}]},{"given":"James","family":"Wheeler","sequence":"additional","affiliation":[{"name":"Centre for Landscape and Climate Research, School of Geography, Geology and Environment, University of Leicester, Leicester LE1 7RH, UK"}]},{"given":"\u00c1ngel","family":"Fern\u00e1ndez-Carrillo","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing Research Group, Department of Geology, Geography and Environment, University of Alcala, 28801 Alcala de Henares, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9116-8081","authenticated-orcid":false,"given":"Kevin","family":"Tansey","sequence":"additional","affiliation":[{"name":"Centre for Landscape and Climate Research, School of Geography, Geology and Environment, University of Leicester, Leicester LE1 7RH, UK"}]},{"given":"Werner","family":"Wiedemann","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions GmbH, 81673 Munich, Germany"}]},{"given":"Peter","family":"Navratil","sequence":"additional","affiliation":[{"name":"GAF AG, 80634 Munich, Germany"}]},{"given":"Sandra","family":"Lohberger","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions GmbH, 81673 Munich, Germany"}]},{"given":"Florian","family":"Siegert","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions GmbH, 81673 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5618-4759","authenticated-orcid":false,"given":"Emilio","family":"Chuvieco","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing Research Group, Department of Geology, Geography and Environment, University of Alcala, 28801 Alcala de Henares, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1175\/BAMS-D-13-00047.1","article-title":"The concept of essential climate variables in support of climate research, applications, and policy","volume":"95","author":"Bojinski","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_2","first-page":"1","article-title":"Vegetation burning in the year 2000: Global burned area estimates from spot vegetation data","volume":"109","author":"Tansey","year":"2004","journal-title":"J. Geophys. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s11027-006-1012-8","article-title":"Establishing a earth observation product service for the terrestrial carbon community: The globcarbon initiative","volume":"11","author":"Plummer","year":"2006","journal-title":"Mitig. Adapt. Strateg. Glob. Chang."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.1016\/j.rse.2008.05.013","article-title":"The collection 5 modis burned area product\u2014Global evaluation by comparison with the modis active fire product","volume":"112","author":"Roy","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tansey, K., Gr\u00e9goire, J.-M., Defourny, P., Leigh, R., Pekel, J.-F., Bogaert, E., and Bartholome, E. (2008). A new, global, multi-annual (2000\u20132007) burnt area product at 1 km resolution. Geophys. Res. Lett., 35.","DOI":"10.1029\/2007GL031567"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.rse.2008.10.006","article-title":"An active-fire based burned area mapping algorithm for the modis sensor","volume":"113","author":"Giglio","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2015.03.011","article-title":"Global burned area mapping from envisat-meris and modis active fire data","volume":"163","author":"Chuvieco","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1111\/geb.12440","article-title":"A new global burned area product for climate assessment of fire impacts","volume":"25","author":"Chuvieco","year":"2016","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.5194\/essd-10-2015-2018","article-title":"Generation and analysis of a new global burned area product based on modis 250 m reflectance bands and thermal anomalies","volume":"10","author":"Chuvieco","year":"2018","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2018.08.005","article-title":"The collection 6 modis burned area mapping algorithm and product","volume":"217","author":"Giglio","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.rse.2015.01.005","article-title":"Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation","volume":"160","author":"Padilla","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111490","DOI":"10.1016\/j.rse.2019.111490","article-title":"Global validation of the collection 6 modis burned area product","volume":"235","author":"Boschetti","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_13","first-page":"64","article-title":"Ten years of global burned area products from spaceborne remote sensing\u2014A review: Analysis of user needs and recommendations for future developments","volume":"26","author":"Mouillot","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Randerson, J.T., Chen, Y., Werf, G.R., Rogers, B.M., and Morton, D.C. (2012). Global burned area and biomass burning emissions from small fires. J. Geophys. Res. Biogeosci., 117.","DOI":"10.1029\/2012JG002128"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.12.011","article-title":"Development of a sentinel-2 burned area algorithm: Generation of a small fire database for sub-saharan africa","volume":"222","author":"Roteta","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"697","DOI":"10.5194\/essd-9-697-2017","article-title":"Global fire emissions estimates during 1997\u20132016","volume":"9","author":"Randerson","year":"2017","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3147","DOI":"10.5194\/bg-16-3147-2019","article-title":"Theoretical uncertainties for global satellite-derived burned area estimat","volume":"16","author":"Brennan","year":"2019","journal-title":"Biogeosceinces"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2019.02.013","article-title":"Historical background and current developments for mapping burned area from satellite earth observation","volume":"225","author":"Chuvieco","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2012.03.001","article-title":"A method for extracting burned areas from landsat tm\/etm+ images by soft aggregation of multiple spectral indices and a region growing algorithm","volume":"69","author":"Stroppiana","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.rse.2014.03.021","article-title":"Development of an automated method for mapping fire history captured in landsat tm and etm+ time series across queensland, Australia","volume":"148","author":"Goodwin","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.3390\/rs70201320","article-title":"Integration of optical and sar data for burned area mapping in mediterranean regions","volume":"7","author":"Stroppiana","year":"2015","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.rse.2017.06.027","article-title":"Mapping burned areas using dense time-series of landsat data","volume":"198","author":"Hawbaker","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Long, T., Zhang, Z., He, G., Jiao, W., Tang, C., Wu, B., Zhang, X., Wang, G., and Yin, R. (2019). 30 m resolution global annual burned area mapping based on landsat images and google earth engine. Remote Sens., 11.","DOI":"10.3390\/rs11050489"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/LGRS.2018.2888641","article-title":"Identification of burned areas and severity using sar sentinel-1","volume":"16","author":"Lasaponara","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Engelbrecht, J., Theron, A., Vhengani, L., and Ke, J. (2017). A simple normalized difference approach to burnt area mapping using multi-polarisation c-band sar. Remote Sens., 9.","DOI":"10.3390\/rs9080764"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Verhegghen, A., Eva, H., Ceccherini, G., Achard, F., Gond, V., Gourlet-Fleury, S., and Cerutti, P.O. (2016). The potential of sentinel satellites for burnt area mapping and monitoring in the congo basin forests. Remote Sens., 8.","DOI":"10.3390\/rs8120986"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mathieu, R., Main, R., Roy, D., Naidoo, L., and Yang, H. (2018, January 22\u201327). Detection of burned areas in southern african savannahs using time series of c-band sentinel-1 data. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517838"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111254","DOI":"10.1016\/j.rse.2019.111254","article-title":"Landsat-8 and sentinel-2 burned area mapping\u2014A combined sensor multi-temporal change detection approach","volume":"231","author":"Roy","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Stavrakoudis, D., Katagis, T., Minakou, C., and Gitas, I.Z. (2019). Towards a Fully Automatic Processing Chain for Operationally Mapping Burned Areas Countrywide Exploiting Sentinel-2 Imagery, SPIE.","DOI":"10.1117\/12.2535816"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Filipponi, F. (2019). Exploitation of sentinel-2 time series to map burned areas at the national level: A case study on the 2017 italy wildfires. Remote Sens., 11.","DOI":"10.3390\/rs11060622"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1175\/BAMS-D-11-00254.1","article-title":"The esa climate change initiative: Satellite data records for essential climate variables","volume":"94","author":"Hollmann","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2017.07.014","article-title":"The esa climate change initiative (cci): A european contribution to the generation of the global climate observing system","volume":"203","author":"Plummer","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1002\/jgrg.20042","article-title":"Analysis of daily, monthly, and annual burned area using the fourth generation global fire emissions database (gfed)","volume":"118","author":"Giglio","year":"2013","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lohberger, S., St\u00e4ngel, M., Atwood, E.C., and Siegert, F. (2017). Spatial evaluation of indonesia\u2019s 2015 fire-affected area and estimated carbon emissions using sentinel-1. Glob. Chang. Biol.","DOI":"10.1111\/gcb.13841"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111345","DOI":"10.1016\/j.rse.2019.111345","article-title":"Burned area detection and mapping using sentinel-1 backscatter coefficient and thermal anomalies","volume":"233","author":"Tanase","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fernandez-Carrillo, A., Belenguer-Plomer, M.A., Chuvieco, E., and Tanase, M.A. (2018). Effects of Sample Size on Burned Areas Accuracy Estimates in the Amazon Basin, SPIE.","DOI":"10.1117\/12.2325686"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Melchiorre, A., and Boschetti, L. (2018). Global analysis of burned area persistence time with modis data. Remote Sens., 10.","DOI":"10.3390\/rs10050750"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_39","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn."},{"key":"ref_40","unstructured":"Key, C.H., and Benson, N.C. (2004). Remote Sensing Measure of Severity: The Normalized Burn Ratio, Firemon Landscape Assessment (La) V4, Sampling and Analysis Methods."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., M\u00fcller-Wilm, U., and Gascon, F. (2017). Sen2cor for Sentinel-2, SPIE.","DOI":"10.1117\/12.2278218"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"12360","DOI":"10.3390\/rs61212360","article-title":"Bams: A tool for supervised burned area mapping using landsat data","volume":"6","author":"Bastarrika","year":"2014","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/29.60107","article-title":"Adaptive multiple-band cfar detection of an optical pattern with unknown spectral distribution","volume":"38","author":"Reed","year":"1990","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rse.2004.02.015","article-title":"Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The pareto boundary","volume":"91","author":"Boschetti","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_45","unstructured":"Boschetti, L., Roy, D., and Justice, C.O. (2009). International Global Burned Area Satellite Product Validation Protocol. Part I\u2014Production and Standardization of Validation Reference Data, Committee on Earth Observation Satellites."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1109\/TGRS.2008.2009000","article-title":"Southern africa validation of the modis, L3JRC, and globcarbon burned-area products","volume":"47","author":"Roy","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.rse.2016.09.016","article-title":"A stratified random sampling design in space and time for regional to global scale burned area product validation","volume":"186","author":"Boschetti","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1890\/06-2148.1","article-title":"Global burned land estimation in latin america using modis composite data","volume":"18","author":"Chuvieco","year":"2008","journal-title":"Ecol. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.rse.2014.01.008","article-title":"Validation of the 2008 modis-mcd45 global burned area product using stratified random sampling","volume":"144","author":"Padilla","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.rse.2017.06.041","article-title":"Stratification and sample allocation for reference burned area data","volume":"203","author":"Padilla","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.rse.2003.11.016","article-title":"Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data","volume":"90","author":"Latifovic","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_53","unstructured":"Kirches, G., Brockmann, C., Boettcher, M., Peters, M., Bontemps, S., Lamarche, C., Schlerf, M., Santoro, M., and Defourny, P. (2014). Land Cover CCI\u2014Product User Guide\u2014Version 2.4, European Union. ESA Public Document CCI-LC-PUG."},{"key":"ref_54","unstructured":"Padilla, M., Wheeler, J., and Tansey, K. (2018). Esa Climate Change Initiative\u2014Fire_cci D4.1.1 Product Validation Report (PVR), Universidad de Alcala. Available online: https:\/\/www.esa-fire-cci.org\/Documents."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/2\/334\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:05:28Z","timestamp":1760364328000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/2\/334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,20]]},"references-count":54,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12020334"],"URL":"https:\/\/doi.org\/10.3390\/rs12020334","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,20]]}}}