{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T17:09:52Z","timestamp":1783444192099,"version":"3.54.6"},"reference-count":50,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (FCT), Portugal","award":["PCIF\/GRF\/0204\/2017"],"award-info":[{"award-number":["PCIF\/GRF\/0204\/2017"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (FCT), Portugal","award":["PD\/BD\/142779\/2018"],"award-info":[{"award-number":["PD\/BD\/142779\/2018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping burned areas using satellite imagery has become a subject of extensive research over the past decades. The availability of high-resolution satellite data allows burned area maps to be produced with great detail. However, their increasing spatial resolution is usually not matched by a similar increase in the temporal domain. Moreover, high-resolution data can be a computational challenge. Existing methods usually require downloading and processing massive volumes of data in order to produce the resulting maps. In this work we propose a method to make this procedure fast and yet accurate by leveraging the use of a coarse resolution burned area product, the computation capabilities of Google Earth Engine to pre-process and download Sentinel-2 10-m resolution data, and a deep learning model trained to map the multispectral satellite data into the burned area maps. For a 1500 ha fire our method can generate a 10-m resolution map in about 5 min, using a computer with an 8-core processor and 8 GB of RAM. An analysis of six important case studies located in Portugal, southern France and Greece shows the detailed computation time for each process and how the resulting maps compare to the input satellite data as well as to independent reference maps produced by Copernicus Emergency Management System. We also analyze the feature importance of each input band to the final burned area map, giving further insight about the differences among these events.<\/jats:p>","DOI":"10.3390\/rs13091608","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T21:25:10Z","timestamp":1619040310000},"page":"1608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6291-9790","authenticated-orcid":false,"given":"Miguel M.","family":"Pinto","sequence":"first","affiliation":[{"name":"Instituto Dom Luiz (IDL), Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4183-9852","authenticated-orcid":false,"given":"Ricardo M.","family":"Trigo","sequence":"additional","affiliation":[{"name":"Instituto Dom Luiz (IDL), Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisbon, Portugal"},{"name":"Departmento de Meteorologia, Instituto de Geoci\u00eancias, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-916, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8640-9170","authenticated-orcid":false,"given":"Isabel F.","family":"Trigo","sequence":"additional","affiliation":[{"name":"Departamento de Meteorologia e Geof\u00edsica, Instituto Portugu\u00eas do Mar e da Atmosfera (IPMA), 1749-077 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1699-9886","authenticated-orcid":false,"given":"Carlos C.","family":"DaCamara","sequence":"additional","affiliation":[{"name":"Instituto Dom Luiz (IDL), Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1071\/WF07151","article-title":"Are wildfires a disaster in the Mediterranean basin?\u2014A review","volume":"17","author":"Pausas","year":"2008","journal-title":"Int. J. Wildland Fire"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bowman, D.M.J.S., Williamson, G.J., Abatzoglou, J.T., Kolden, C.A., Cochrane, M.A., and Smith, A.M.S. (2017). Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol., 1.","DOI":"10.1038\/s41559-016-0058"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Turco, M., Rosa-C\u00e1novas, J.J., Bedia, J., Jerez, S., Mont\u00e1vez, J.P., Llasat, M.C., and Provenzale, A. (2018). Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models. Nat. Commun., 9.","DOI":"10.1038\/s41467-018-06358-z"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ruffault, J., Curt, T., Moron, V., Trigo, R.M., Mouillot, F., Koutsias, N., Pimont, F., Martin-StPaul, N., Barbero, R., and Dupuy, J.L. (2020). Increased likelihood of heat-induced large wildfires in the Mediterranean Basin. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-70069-z"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.rse.2016.06.015","article-title":"Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems","volume":"184","author":"Quintano","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hislop, S., Jones, S., Soto-Berelov, M., Skidmore, A., Haywood, A., and Nguyen, T. (2018). Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery. Remote Sens., 10.","DOI":"10.3390\/rs10030460"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.5194\/nhess-19-169-2019","article-title":"Using cellular automata to simulate wildfire propagation and to assist in fire management","volume":"19","author":"Freire","year":"2019","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3653","DOI":"10.1080\/01431161.2011.631950","article-title":"Comparison of burnt area estimates derived from satellite products and national statistics in Europe","volume":"33","author":"Loepfe","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","first-page":"76","article-title":"Satellite versus ground-based estimates of burned area: A comparison between MODIS based burned area and fire agency reports over North America in 2007","volume":"3","author":"Mangeon","year":"2015","journal-title":"Anthr. Rev."},{"key":"ref_10","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_11","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 250m reflectance bands and thermal anomalies","volume":"10","author":"Chuvieco","year":"2018","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2019.12.014","article-title":"A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images","volume":"160","author":"Pinto","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1080\/17538947.2018.1433727","article-title":"Spatial and temporal intercomparison of four global burned area products","volume":"12","author":"Humber","year":"2018","journal-title":"Int. J. Digit. Earth"},{"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":"1003","DOI":"10.1016\/j.rse.2010.12.005","article-title":"Mapping burned areas from Landsat TM\/ETM + data with a two-phase algorithm: Balancing omission and commission errors","volume":"115","author":"Bastarrika","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Ban, Y., Zhang, P., Nascetti, A., Bevington, A.R., and Wulder, M.A. (2020). Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning. Sci. Rep., 10.","DOI":"10.1038\/s41598-019-56967-x"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Knopp, L., Wieland, M., R\u00e4ttich, M., and Martinis, S. (2020). A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data. Remote Sens., 12.","DOI":"10.3390\/rs12152422"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_24","unstructured":"San-Miguel-Ayanz, J., Durrant, T., Boca, R., Libert\u00e0, G., Branco, A., de Rigo, D., Ferrari, D., Maianti, P., Vivancos, T.A., and Costa, H. (2017). Forest fires in Europe. Middle East N. Afr., 2018."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Turco, M., Jerez, S., Augusto, S., Tar\u00edn-Carrasco, P., Ratola, N., Jim\u00e9nez-Guerrero, P., and Trigo, R.M. (2019). Climate drivers of the 2017 devastating fires in Portugal. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-50281-2"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Archibald, S., and Roy, D.P. (2009, January 12\u201317). Identifying individual fires from satellite-derived burned area data. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417974"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nogueira, J., Ruffault, J., Chuvieco, E., and Mouillot, F. (2016). Can We Go Beyond Burned Area in the Assessment of Global Remote Sensing Products with Fire Patch Metrics?. Remote Sens., 9.","DOI":"10.3390\/rs9010007"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Laurent, P., Mouillot, F., Yue, C., Ciais, P., Moreno, M.V., and Nogueira, J.M.P. (2018). FRY, a global database of fire patch functional traits derived from space-borne burned area products. Sci. Data, 5.","DOI":"10.1038\/sdata.2018.132"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1071\/WF04010","article-title":"Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data","volume":"14","author":"Cocke","year":"2005","journal-title":"Int. J. Wildland Fire"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2006.12.006","article-title":"Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)","volume":"109","author":"Miller","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_33","first-page":"221","article-title":"Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity","volume":"64","author":"Quintano","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","unstructured":"Rolnick, D., Veit, A., Belongie, S., and Shavit, N. (2017). Deep learning is robust to massive label noise. arXiv."},{"key":"ref_35","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_36","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_37","unstructured":"Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., and Venkatesh, G. (2017). Mixed precision training. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wu, Y., and He, K. (2018). Group Normalization. Computer Vision\u2014ECCV 2018, Springer.","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref_39","unstructured":"Smith, L.N. (2018). A disciplined approach to neural network hyper-parameters: Part 1\u2013learning rate, batch size, momentum, and weight decay. arXiv."},{"key":"ref_40","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shorten, C., and Khoshgoftaar, T.M. (2019). A survey on Image Data Augmentation for Deep Learning. J. Big Data, 6.","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Howard, J., and Gugger, S. (2020). Fastai: A Layered API for Deep Learning. Information, 11.","DOI":"10.3390\/info11020108"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"515","DOI":"10.5194\/nhess-18-515-2018","article-title":"Fire danger rating over Mediterranean Europe based on fire radiative power derived from Meteosat","volume":"18","author":"Pinto","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"DaCamara, C.C., Libonati, R., Pinto, M.M., and Hurduc, A. (2019). Near- and Middle-Infrared Monitoring of Burned Areas from Space. Satellite Information Classification and Interpretation, IntechOpen.","DOI":"10.5772\/intechopen.82444"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2137","DOI":"10.1175\/BAMS-D-18-0231.1","article-title":"Meteorological Conditions Conducive to the Rapid Spread of the Deadly Wildfire in Eastern Attica, Greece","volume":"100","author":"Lagouvardos","year":"2019","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_47","unstructured":"Boschetti, L., Roy, D., and Justice, C. (2009). International Global Burned Area Satellite Product Validation Protocol Part I\u2013Production and Standardization of Validation Reference Data, Committee on Earth Observation Satellites."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Pulvirenti, L., Squicciarino, G., Fiori, E., Fiorucci, P., Ferraris, L., Negro, D., Gollini, A., Severino, M., and Puca, S. (2020). An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data. Remote Sens., 12.","DOI":"10.3390\/rs12040674"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1080\/01431160701281072","article-title":"Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM\/ETM images","volume":"29","author":"Escuin","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1016\/j.rse.2011.02.006","article-title":"On a new coordinate system for improved discrimination of vegetation and burned areas using MIR\/NIR information","volume":"115","author":"Libonati","year":"2011","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1608\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:50:32Z","timestamp":1760161832000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1608"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,21]]},"references-count":50,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13091608"],"URL":"https:\/\/doi.org\/10.3390\/rs13091608","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,21]]}}}