Conference Agenda

Methane (CH4) is a major greenhouse gas; however, large-scale monitoring remains challenging due to the high costs and spatial limitations of ground-based and airborne observations. In contrast, Sentinel-2 shortwave infrared (SWIR)–based plume detection is hindered by its coarse spectral resolution, surface artifacts, and limited real-world annotations. This study proposes a self-supervised learning (SSL) framework based on the Simple Framework for Contrastive Learning of Visual Representations (SimCLR) to learn transferable CH4 plume representations from unlabeled Sentinel-2 data. A real-world dataset of 456 Sentinel-2 image tiles was manually annotated using the multi-band–multi-pass (MBMP) approach and utilized to evaluate six encoder backbones. Across five labeled-data portions ranging from 20% to 100%, SimCLR pretraining improved plume segmentation compared to ImageNet-only initialization. In the full-data scenario, MobileNet achieved an F1-score of 0.90 with an Intersection over Union (IoU) of 0.80, while Shifted Window Transformer (SwinT) reached an F1-score of 0.85 with an IoU of 0.75. The benefit of self-supervised pretraining was most evident with limited labeled data, where ImageNet-only models degraded substantially, while SimCLR-pretrained encoders achieved higher accuracy. Moreover, the Integrated Mass Enhancement (IME) method was employed for quantifying the emission flux rate. MobileNet provided the strongest agreement with reference emission estimates, achieving an RMSE of 1690 kg/h. Finally, the results demonstrate that SimCLR-based SSL substantially enhances CH4 plume detection from Sentinel-2 imagery and supports more reliable emission quantification for large-scale CH4 monitoring.

Column-averaged methane (XCH4) data spanning 2018-2023 from the European Space Agency (ESA) Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5 Precursor satellite are assessed for evidence of methane (CH4) emissions from permafrost. We generated bi-monthly anomaly maps of XCH4 from TROPOMI and soil temperature (Tsoil) from reanalysis data for all land north of 50°N. Considering the XCH4 anomalies in the contexts of soil carbon content and wind variability led to a focus on Canada’s Hudson Bay Lowlands (HBL), Earth’s second largest peatland complex (~325,000 km2), which is underlain by continuous to isolated permafrost. This sub-Arctic region is vulnerable to rapid climatic warming and exhibits wind conditions favorable for emission detection from space. HBL XCH4 anomalies strongly correlate with soil temperature anomalies (R = 0.626 to 0.866), consistent with wetlands as the primary CH4 emission source; however, the strong increase in CH4 emissions over 2018-2023 may also suggest a contribution from permafrost thaw and expansion of thermokarst fens.

This study leverages TROPOMI satellite observations and atmospheric inversion modelling to quantify methane emissions from urban wetlands across six major North American cities, including Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City. By coupling high-resolution column-averaged methane measurements with the GEOS-Chem chemical transport model via the Integrated Methane Inversion (IMI) platform, the research distinguishes emissions from both natural wetland and anthropogenic urban sectors. Results indicate that prior inventories substantially underestimate urban wetland methane emissions in most cities. Posterior wetland emissions are resolved alongside dominant anthropogenic sources such as landfills, energy systems, and wastewater, revealing spatially distinct patterns and highlighting seasonal wetland flux variability. The findings demonstrate that urban wetlands, although representing a relatively smaller source compared to anthropogenic emissions, display considerable underrepresented contributions to local methane budgets, underscoring the need for robust, integrated monitoring in urban environments. This methodology provides a scalable framework for routine urban wetland methane flux quantification and supports evidence-based climate mitigation and land management strategies.

Methane plume detection from medium-resolution multispectral satellites such as Sentinel-2 remains challenging due to weak methane signals and strong background variability across land cover, illumination conditions, and atmospheric states. To advance automated detection capabilities, we develop a large-scale benchmark dataset that combines simulated methane plume enhancements with real Sentinel-2 imagery, covering a wide range of emission magnitudes and diverse environmental scenarios. The dataset includes over 64,000 samples and incorporates methane-sensitive inputs derived from the MBMP retrieval workflow, providing a comprehensive foundation for robust model training and evaluation. Building on this dataset, a hybrid transformer–U-Net architecture is proposed, integrating global self-attention with Grouped Attention Gates to enhance feature fusion and improve segmentation of methane structures. The model achieves high accuracy on the benchmark dataset and demonstrates strong generalization to real emission events in complex environments. The combined contributions of the benchmark dataset and hybrid model offer a promising path toward reliable, scalable methane plume monitoring using widely available multispectral satellite observations.

Methane (CH₄) is a potent short-lived climate pollutant, making the detection of major point sources (“super-emitters”) crucial for mitigation. The Sentinel-5 Precursor (S5P) mission, with the TROPOMI instrument, captures global methane concentrations at ~7 × 5.5 km resolution with near-daily coverage. While this resolution is too coarse to identify emissions from individual facilities, its revisit frequency allows effective regional monitoring. Conversely, high-resolution (HR) imaging spectrometers like Carbon Mapper’s Tanager (~30 m) and NASA’s EMIT (~60 m) provide detailed plume mapping but have limited spatial and temporal coverage.

Carbon Mapper releases open-access, high-resolution plume products including georeferenced rasters and metadata. In this study, these HR detections serve as reference events to assess their visibility in coarser Sentinel-5P observations. The workflow includes curating HR events, summarizing their emission context, and inspecting nearby Sentinel-5P data for consistent methane enhancements. The method is exploratory and avoids presupposing Sentinel-5P’s success or failure in detecting plumes at this scale.

This analysis bridges the gap between frequent global monitoring and targeted HR observations. It establishes a path for future cross-sensor integration, combining HR spatial precision with Sentinel-5P’s temporal continuity. With additional labeled data, this approach could inform machine-learning tools for methane anomaly detection and plume segmentation, improving operational methane monitoring across scales.