The integration of vision transformers and SAM for automated methane super-emitter detection using TROPOMI data

Methane (CH₄) significantly contributes to global warming, with a global warming potential approximately 84 times greater than carbon dioxide (CO₂) over 20 years. Numerous studies have shown that a small number of high-emitting point sources, known as super-emitters, account for a disproportionately large share of total anthropogenic CH₄ emissions, underscoring the urgency of targeted detection strategies. Recently, a growing focus has been on using remote sensing technology for CH₄ monitoring across various emission sources. As such, this study introduces an automated solution for identifying CH₄ super-emitters using Sentinel-5P (S5P) satellite data. Specifically, a deep learning (DL) framework that integrates a Vision Transformer (ViT) and the Segment Anything Model (SAM) for CH₄ plume detection is proposed. The ViT model is trained using CH₄ plume locations reported by the Netherlands Institute for Space Research (SRON) to classify the presence or absence of CH₄ plumes within image patches, achieving an overall accuracy (OA) of 0.92. Subsequently, SAM extracts plume boundaries from patches identified as plumes by the ViT. Integrated Mass Enhancement (IME) is then used to quantify emission rates based on the SAM-generated masks. This approach is applied to various known emission regions, including Turkmenistan, Spain, Algeria, Argentina, China, Iran, the United States, India, and Morocco, identifying significant CH₄ plumes with emission rates up to 92 t/h. The reported rates align closely with SRON values for the same dates and locations. While the ViT model requires supervised training, the SAM component operates without mask-specific training data, enabling automated and generalizable mask extraction. This study demonstrates the potential of combining advanced AI models with satellite data for effective CH₄ monitoring and environmental assessment.