A universal method combining spectral index and machine learning for monitoring onshore gas flaring from Sentinel-2 MSI images

• This universal method extracts GFs with precision higher than 93%. • The factor of spatial variability plays an important role in detecting GFs. • The proposed method could well detect weak GFs. Gas flaring (GF) releases large amounts of greenhouse gases and pollutants, severely affecting global climate change and regional environmental quality. Quantifying global GF activities is essential to implement emission mitigation polices. Existing methods struggle with detecting weak GF activities under complex daytime background conditions. This study proposes a method combining the spectral index and machine learning methods, to detect onshore GF sites (GFs) using the images from the Multispectral Instrument (MSI) onboard Sentinel-2 satellites. First, the Thermal Anomaly Index (TAI, calculated in the near-infrared and short-wave infrared bands) and TAI increment (ΔTAI) are applied to detect thermal anomalies from MSI images, in which ΔTAI enhances the spatial-contextual background contrast. Then, nine flare-specific features are selected to further identify the GFs from the thermal anomalies using a random forest model. Finally, the detected GFs were validated worldwide, achieving an overall user accuracy of 93.34% and a producer accuracy of 95.31%. This approach enhances the sensitivity to weak flares and effectively addresses the interferences from other heat sources and highly reflective buildings, providing a universal solution for GFs detection in complex onshore scenes.