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dc.contributor.authorÁlvarez Martín, Rodrigo
dc.contributor.authorSantos Arranz, Javier
dc.contributor.authorGil González, Ana Belén 
dc.contributor.authorLuis Reboredo, Ana de 
dc.contributor.authorPérez Lancho, María Belén 
dc.coverage.spatialEspaña, lat=40.463667; long= -3.74922es_ES
dc.coverage.temporalstart 2024-07-16 and end 2024-08-31es_ES
dc.date.accessioned2026-09-10T07:26:22Z
dc.date.available2026-09-10T07:26:22Z
dc.date.issued2026
dc.identifier.citationÁlvarez Martín, R., Santos Arranz, J., Gil González, A. B., de Luis, A., & Pérez Lancho, B. (2026). Sentinel-2 Wildfire Risk Classification Dataset for the Iberian Peninsula [Dataset]. Universidad de Salamanca. https://doi.org/10.71636/Y14A-WK88es_ES
dc.identifier.urihttp://hdl.handle.net/10366/172714
dc.description.abstract[EN]This dataset was developed to support the development, training and evaluation of artificial intelligence and machine learning models for wildfire risk prediction in the Iberian Peninsula. The dataset integrates satellite observations from Sentinel-2 with meteorological information from ERA5 and fire-weather indicators derived from the Fire Weather Index (FWI) system. Data were collected for the period from 16 July 2024 to 31 August 2024, with observations obtained approximately at midday following the temporal criteria established by the Spanish State Meteorological Agency (AEMET). Sentinel-2 data include ten spectral bands (B2, B3, B4, B5, B6, B7, B8, B8A, B11 and B12). ERA5 meteorological information includes 2-metre temperature, 2-metre dewpoint temperature, the U and V components of 10-metre wind, and total precipitation. These variables were used to calculate the components of the Fire Weather Index using the FWIfunctions library (version 0.1.10), including Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Buildup Index (BUI), and the resulting Fire Weather Index (FWI). The continuous FWI values were subsequently classified into five wildfire-risk categories: low, medium, high, very high and extreme. The class thresholds were established using the 40th, 65th, 85th and 95th percentiles of the FWI distribution, respectively. A common spatial grid with a resolution of 0.1° × 0.1° was used to harmonize Sentinel-2 and ERA5 information. The study area was defined using the bounding box [44, -10, 36, 4]. Each record represents a spatiotemporal observation linking satellite-derived information with meteorological and FWI-derived wildfire-risk information for a given location and date.es_ES
dc.description.sponsorshipThis work was supported by the International Chair Project on Trustworthy Artificial Intelligence and the Demographic Challenge within the framework of the National Artificial Intelligence Strategy (ENIA). Reference: TSI-100933-2023-0001. Funded by the Secretary of State for Digitalization and Artificial Intelligence and by the European Union (NextGenerationEU).es_ES
dc.language.isoenges_ES
dc.publisherUniversidad de Salamancaes_ES
dc.rightsAttribution 4.0 Internationales_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/es_ES
dc.subjectWildfire riskes_ES
dc.subjectFire Weather Indexes_ES
dc.subjectFWIes_ES
dc.subjectSentinel-2es_ES
dc.subjectERA5es_ES
dc.subjectGoogle Earth Enginees_ES
dc.subjectRemote sensinges_ES
dc.subjectSatellite imageryes_ES
dc.subjectMeteorological dataes_ES
dc.subjectWildfire predictiones_ES
dc.subjectFire dangeres_ES
dc.subjectMachine learninges_ES
dc.subjectSpaines_ES
dc.subjectIberian Peninsulaes_ES
dc.titleSentinel-2 Wildfire Risk Classification Dataset for the Iberian Peninsula [Dataset]es_ES
dc.typeinfo:eu-repo/semantics/datasetes_ES
dc.subject.unesco2509 Meteorologíaes_ES
dc.subject.unesco2502 Climatologíaes_ES
dc.subject.unesco1203.04 Inteligencia Artificiales_ES
dc.identifier.doi10.71636/y14a-wk88
dc.relation.projectIDTSI-100933-2023-0001es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES
dc.publication.year2026


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