Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes

With the intensification of global warming, the monitoring of methane emission and detection of gas plumes from landfills have increasingly received attention. We decompose methane emission monitoring into three sub-tasks: methane concentration inversion, plume segmentation, and emission rate estimation. Conventional algorithms have limitations: methane concentration inversion usually uses the matched filter, which is sensitive to global spectrum distribution and contains a large amount of noises. There is limited research on plume segmentation, with many studies resorting to manual segmentation that is likely to be subjective. The estimation of methane emission rate often utilizes IME algorithm, which relies on obtaining meteorological measurement data. Using the WENT landfill site in Hong Kong and PRISMA hyperspectral satellite imagery, we propose a new deep learning-based framework for quantitative monitoring of methane emissions from remote sensing images based on physical simulation. We generate simulated methane plumes using large eddy simulation (LES) and different concentration maps of fugitive emission using the radiative transfer equation (RTE), while combining augmentation techniques to create a simulated PRISMA dataset. We train a U-Net network for methane concentration inversion, a Mask R-CNN network for methane plume segmentation, and a ResNet-50 network for methane emission rate estimation. All three deep networks achieve higher validation accuracy compared to conventional algorithms. We further respectively combine the first two sub-tasks and the last two sub-tasks to design the multi-task learning models - MTL-01 and MTL-02, both of which achieve higher accuracy than single-task models. Our research serves as a demonstration of applying multi-task deep learning to quantitative methane monitoring and can be extended to a broad range of methane monitoring tasks.