  As global warming intensifies, increased attention is being paid to
monitoring fugitive methane emissions and detecting gas plumes from landfills.
We have divided methane emission monitoring into three subtasks: methane
concentration inversion, plume segmentation, and emission rate estimation.
Traditional algorithms face certain limitations: methane concentration
inversion typically employs the matched filter, which is sensitive to the
global spectrum distribution and prone to significant noise. There is scant
research on plume segmentation, with many studies depending on manual
segmentation, which can be subjective. The estimation of methane emission rate
frequently uses the IME algorithm, which necessitates meteorological
measurement data. Utilizing the WENT landfill site in Hong Kong along with
PRISMA hyperspectral satellite imagery, we introduce a novel deep
learning-based framework for quantitative methane emission monitoring from
remote sensing images that is grounded in physical simulation. We create
simulated methane plumes using large eddy simulation (LES) and various
concentration maps of fugitive emissions using the radiative transfer equation
(RTE), while applying augmentation techniques to construct 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 yield higher
validation accuracy compared to traditional algorithms. Furthermore, we combine
the first two subtasks and the last two subtasks to design multi-task learning
models, MTL-01 and MTL-02, both of which outperform single-task models in terms
of accuracy. Our research exemplifies the application of multi-task deep
learning to quantitative methane monitoring and can be generalized to a wide
array of methane monitoring tasks.
