  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).
