Explainable Comparison of Feature-Based and Deep Learning Models for TROPOMI Methane Plume Screening
Continuous and global detection of large methane emissions is a crucial step for global warming mitigation. Satellite observations, such as from S5P/TROPOMI, combined with plume detection algorithms, can play a key role in this effort. However, not all TROPOMI plume detections that look like methane emission plumes are the result of actual emissions. A significant part of the plume-like features in the data are retrieval artifacts. Such artifacts could be the result of variations in elevation or albedo gradients, high concentrations of aerosols, coastal lines, water bodies, etc. Previous work approached the problem of plume-artifact classification by means of a Support Vector Machine Clas...
| Authors | Solomiia Kurchaba, Joannes D. Maasakkers, Berend J. Schuit et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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