Satellite-borne identification and quantification of wildfire smoke emissions in North America via a novel UV-based index

Abstract

Estimation of the smoke emissions released by wildfires has become a significant concern in the context of atmospheric environment and global climate change, in which satellite-borne optical remote sensing plays an important role. Previous studies have utilized the satellite-based aerosol optical depth (AOD) algorithm to estimate the wildfire-related total particulate matter emissions (TPM). However, this “top-down” approach is often impeded by false or missed detection due to extensive cloud cover and smokes with high concentrations, resulting in an underestimation of TPM. To address these limitations, this study proposes a new ultraviolet (UV)-based methodology for the identification and quantification of wildfire smokes from satellite platforms. The spectral characteristics of smoke plumes and different cloud types, including cirrus and cumulus clouds, are characterized by China's Haiyang-1C/D (HY-1C/D) satellites within both UV and visible wavelengths. Specifically, the 355 nm UV band displays a strong absorption pattern associated with smoke plumes. Based on this diagnostic spectral feature, an ultraviolet smoke index (UVSI) is developed, which can mitigate the impact of high background heterogeneity. Moreover, in comparison with the MODIS AOD algorithm, the UVSI index can not only improve the precision of smoke detection, especially for smokes with high concentrations, but also demonstrate a strong correlation with AOD at the numerical level. Accordingly, this study proposes the use of UVSI in place of AOD for TPM estimation. Combined with the fire radiative energy (FRE) measured by GOES-R satellite, the smoke emission coefficient (C_e) can be determined, which is 34.37 g MJ⁻¹ for the September 2020 burning season in North America. The above results would provide a new reference for operational monitoring and assessment of wildfire smokes.