Fuel biophysical variables retrieved from satellite optical data combined with Prometheus fuel types allow for accurate prediction of potential fire severity

Abstract Background Accurate prediction of fire severity is increasingly important in Mediterranean ecosystems, where climate change and rural land abandonment are intensifying fire regimes. This study assesses, for the first time, the potential of pre-fire Prometheus fuel types (FTs) and fuel biophysical variables to predict fire severity and understand the underlying fuel-related drivers with implications for fuel management and risk mitigation strategies. We evaluated four large wildfires (> 257,000 ha) that occurred in Spain during the extreme 2022 fire season. Fire severity was estimated using the differenced Normalized Burn Ratio-Enhanced Vegetation Index (dNBR-EVI) from Sentinel-2 Level-2A optical data. We leveraged previously developed estimates of pre-fire Prometheus FTs at 20-m grid size for each study site, derived from physically-based remote sensing techniques. The inversion of PROSAIL and INFORM radiative transfer models (RTMs) were used to retrieve pre-fire fuel biophysical variables (canopy water content -CWC-, canopy chlorophyll content -CCC-, and fractional vegetation cover -FCOVER-) from Sentinel-2 data in grasslands/shrublands and forests, respectively. Results Random forest (RF) regression models showed that fire severity was most accurately predicted when Prometheus FTs and fuel biophysical variables were fitted together (R 2 = 0.71 and RMSE = 9.18% of the observed dNBR-EVI range), outperforming models using either set of predictors alone. The most important predictor was CWC, followed by Prometheus fuel type, FCOVER and CCC, but fire severity variability was largely driven by predictor interactions. High fire severity was only associated with dense and continuous fuel loads when water amount in live fuels was low, as depicted by the strong interaction between CWC and Prometheus FTs. Conversely, fire severity was constrained under high fuel moisture availability, regardless of fuel arrangement. Finally, in areas with moderate to high FCOVER, reduced CCC was associated with markedly higher fire severity, suggesting that dense and physiologically stressed canopies may be more prone to intense fire damage. Conclusions Our findings support that Prometheus FTs combined with fuel biophysical variables, mainly CWC, may strengthen decision-making frameworks for adaptive fire management under current and future global change scenarios.

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Publication Details

Journal
Fire Ecology
Published
2026-06-16
DOI
https://doi.org/10.1186/s42408-026-00530-3
Primary Topic
Fire effects on ecosystems
Type
article
Field-Weighted Citation Impact
0.00

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article

Fuel biophysical variables retrieved from satellite optical data combined with Prometheus fuel types allow for accurate prediction of potential fire severity

Carmen Quintano, Alfonso Fernández–Manso, L. Calvo, José Manuel Fernández‐Guisuraga
Fire Ecology
Fire effects on ecosystems
article

Fuel biophysical variables retrieved from satellite optical data combined with Prometheus fuel types allow for accurate prediction of potential fire severity

Carmen Quintano, Alfonso Fernández–Manso, L. Calvo, José Manuel Fernández‐Guisuraga
article en

Abstract

Abstract Background Accurate prediction of fire severity is increasingly important in Mediterranean ecosystems, where climate change and rural land abandonment are intensifying fire regimes. This study assesses, for the first time, the potential of pre-fire Prometheus fuel types (FTs) and fuel biophysical variables to predict fire severity and understand the underlying fuel-related drivers with implications for fuel management and risk mitigation strategies. We evaluated four large wildfires (> 257,000 ha) that occurred in Spain during the extreme 2022 fire season. Fire severity was estimated using the differenced Normalized Burn Ratio-Enhanced Vegetation Index (dNBR-EVI) from Sentinel-2 Level-2A optical data. We leveraged previously developed estimates of pre-fire Prometheus FTs at 20-m grid size for each study site, derived from physically-based remote sensing techniques. The inversion of PROSAIL and INFORM radiative transfer models (RTMs) were used to retrieve pre-fire fuel biophysical variables (canopy water content -CWC-, canopy chlorophyll content -CCC-, and fractional vegetation cover -FCOVER-) from Sentinel-2 data in grasslands/shrublands and forests, respectively. Results Random forest (RF) regression models showed that fire severity was most accurately predicted when Prometheus FTs and fuel biophysical variables were fitted together (R 2 = 0.71 and RMSE = 9.18% of the observed dNBR-EVI range), outperforming models using either set of predictors alone. The most important predictor was CWC, followed by Prometheus fuel type, FCOVER and CCC, but fire severity variability was largely driven by predictor interactions. High fire severity was only associated with dense and continuous fuel loads when water amount in live fuels was low, as depicted by the strong interaction between CWC and Prometheus FTs. Conversely, fire severity was constrained under high fuel moisture availability, regardless of fuel arrangement. Finally, in areas with moderate to high FCOVER, reduced CCC was associated with markedly higher fire severity, suggesting that dense and physiologically stressed canopies may be more prone to intense fire damage. Conclusions Our findings support that Prometheus FTs combined with fuel biophysical variables, mainly CWC, may strengthen decision-making frameworks for adaptive fire management under current and future global change scenarios.

Fire Ecology
Universidad de Valladolid (ES), VSB - Technical University of Ostrava (CZ), University of Trás-os-Montes and Alto Douro (PT), Management Research Institute (US), Universidad de León (ES)
Ministerio de Ciencia e Innovación, Junta de Castilla y León
Climate action
Openalex Percentile: Top 7%
Fire effects on ecosystems
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