Explainable Comparison of Feature-Based and Deep Learning Models for TROPOMI Methane Plume Screening

2026-05-26

AI Summary

This study addresses the challenge of distinguishing genuine methane emission plumes from artifacts induced by topography or albedo variations in TROPOMI satellite observations. For the first time, it systematically compares the performance of handcrafted-feature-based classifiers—such as support vector classifiers, random forests, and XGBoost—against end-to-end deep learning models, including ResNet-18 and ResNet-34, under both balanced and imbalanced data regimes. Employing a unified SHAP interpretability framework, the work elucidates fundamental differences in decision-making mechanisms between the two model families, clarifying the trade-offs between accuracy and robustness. These findings provide actionable guidance for operational systems such as the CAMS Methane Hotspot Detector in selecting appropriate models for real-world deployment.

Abstract

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 Classifier (SVC), trained on an extensive set of observation-based scalar features designed by domain experts. However, such an approach limits the information scope received by the algorithm to what is deemed to be important by the experts, breaks the spatial relationship between pixels, and loses information during the process of statistical aggregation. In this study, we compare feature-based (SVC, Random Forest, XGBoost) and image-based (ResNet-18, ResNet-34) models for methane plume-artifact classification under balanced and imbalanced evaluation settings. To interpret the results, we apply SHAP-based explainability to both model families. Our findings provide practical guidance for model selection in operational methane-screening workflows such as the CAMS Methane Hotspot Explorer.

Problem

• methane plume
• TROPOMI
• retrieval artifacts
• plume screening
• satellite observation

Innovation

• methane plume detection
• deep learning vs. feature-based models
• satellite remote sensing
• model interpretability
• TROPOMI