Intensity-resolved classification of forest fire smoke via fused satellite and ground-based optical climatology

Accurate quantitative remote sensing of forest fire smoke aerosol is essential for assessing its impacts on the environment and climate. Traditional aerosol retrieval algorithms rely on aerosol models derived from global site statistics, in which a class of aerosols representing biomass-burning particles is often used for fire smoke scenarios. However, such models fail to capture the local characteristics of forest fire smoke. To address this limitation, this study matches and fuses Global Fire Emission Data (GFED) with ground-based the Aerosol Robotic Network (AERONET) observations to develop a dedicated aerosol optical dataset for forest fire smoke scenes. Fire emission intensity is used to represent burn strength, allowing the relationship between smoke optical properties and fire intensity to be established. Subsequently, cluster analysis reveals statistically distinct optical signatures of smoke under different combustion intensities, which also exhibit typical regional distribution patterns. Results show that smoke presents categorically different optical characteristics depending on burn strength. This work provides explicit constraints for improving next-generation satellite retrieval algorithms and enhances the accuracy of evaluating the environmental and climatic effects of forest fire smoke.