Assessing the Detection of Methane Plumes in Offshore Areas Using High-Resolution Imaging Spectrometers

Abstract. The offshore oil and gas industry is an important contributor to global anthropogenic methane emissions. Satellite-based, high-resolution imaging spectrometers are showing a great potential for the detection of methane emissions over land. However, the use of the same methods over offshore oil and gas extraction basins is challenged by the low reflectance of water in the near- and shortwave infrared spectral windows used for methane retrievals. This limitation can be partly alleviated by data acquisitions under the so-called sun glint configuration, which enhances the at-sensor radiance. In this work, we assess the performance of two space-based imaging spectrometers, EnMAP and EMIT, for the detection of offshore methane plumes applying the matched filter method. We use simulated plumes to generate parametric probability of detection (POD) models for a range of emission flux rates (Q), at-sensor radiances and wind speeds. The POD models were confronted with real plume detections for the two instruments. Our analysis shows that the spatial resolution of the instrument and the at-sensor radiance (which drives the retrieval precision) are the two factors with the greatest impact on plume POD. We also evaluate the dependence of the at-sensor radiance on the illumination-observation geometry and the surface roughness. Our POD models properly represent the different trade-offs between spatial resolution and retrieval precision in EnMAP and EMIT. As an example, for most combinations of Q and wind speed values at POD = 50 %, EMIT demonstrates better detection performance at Q>7 t h−1, whereas EnMAP performs better at Q<7 t h−1. This study demonstrates the ability of these two satellite instruments to detect high-emitting offshore point sources under a range of different conditions. By filtering data based on these conditions, methane emission detection and monitoring efforts can be optimized, reducing unnecessary searches and ultimately increasing the action taken on these emissions.

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HyperCoast: A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments

Liu et al., (2024). HyperCoast: A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments. Journal of Open Source Software, 9(100), 7025, https://doi.org/10.21105/joss.07025

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