Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation
This work addresses methane emission monitoring for environmental and climate applications, though it is incremental as it adapts existing methods to new sensor data.
The authors tackled methane plume detection from space using PRISMA hyperspectral images by developing a deep learning model with data augmentation, achieving large-scale detection capabilities without computationally expensive simulations.
The new generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m). We present here a complete framework to identify CH4 plumes using images from the PRISMA satellite mission and a deep learning model able to detect plumes over large areas. To compensate for the relative scarcity of PRISMA images, we trained our model by transposing high resolution plumes from Sentinel-2 to PRISMA. Our methodology thus avoids computationally expensive synthetic plume generation from Large Eddy Simulations by generating a broad and realistic training database, and paves the way for large-scale detection of methane plumes using future hyperspectral sensors (EnMAP, EMIT, CarbonMapper).