MPSUNet: A Deep Learning-Based Segmentation Framework for Methane Plume Detection With Space-Based Hyperspectral and Multispectral Imagery

Methane is a potent greenhouse gas, and its accurate detection is critical for addressing global climate change. Although remote sensing has been a crucial technique for understanding the spatial distribution and temporal dynamics of methane emissions, it is still urgently needed to automate the identification of methane emission plumes and effectively deconvolve the signal from background noise. In this study, we propose the methane plume segmentation UNet (MPSUNet) to achieve precise segmentation of methane plumes from remote sensing imagery. The MPSUNet incorporates the pyramid squeeze attention (PSA) module to enhance feature representation and employs a joint loss function combining Dice Loss and Focal Loss to address challenges such as class imbalance and noisy data. A novel dataset, MPDataset, was constructed using Earth surface mineral dust source investigation (EMIT) methane enhancement and RGB radiance data, providing 4172 high-quality samples for model training and evaluation. Our results show that MPSUNet achieves a mean intersection over union (MIoU) of 78.20%, mean precision of 80.78%, recall of 71.11%, and mean pixel accuracy (MPA) of 85.41% on the complete four-channel MPDataset. Compared with seven classical segmentation models, the most improvements of MPSUNet in MIoU, MPrecision, recall, and MPA reach up to 5.33%, 12.28%, 18.04%, and 8.94%, respectively. Notably, the integration of RGB channels enhances the segmentation of small and intricate plume structures. Cross-dataset evaluation using Sentinel-2 data further validates the model’s robustness, achieving an MIoU of 77.65% and an MPA of 83.61%. Generally, the proposed MPSUNet model marks a substantial performance in methane detection, which provides a robust technical framework for global-scale methane emission monitoring as emphasized by global climate agreements.