Super-Resolution of BVOC Emission Maps Via Domain Adaptation
Antonio Giganti Sara Mandelli Paolo Bestagini Marco Marcon Stefano Tubaro
Enhancing the resolution of Biogenic Volatile Organic Compound (BVOC)
emission maps is a critical task in remote sensing. Recently, some
Super-Resolution (SR) methods based on Deep Learning (DL) have been proposed,
leveraging data from numerical simulations for their training process. However,
when dealing with data derived from satellite observations, the reconstruction
is particularly challenging due to the scarcity of measurements to train SR
algorithms with. In our work, we aim at super-resolving low resolution emission
maps derived from satellite observations by leveraging the information of
emission maps obtained through numerical simulations. To do this, we combine a
SR method based on DL with Domain Adaptation (DA) techniques, harmonizing the
different aggregation strategies and spatial information used in simulated and
observed domains to ensure compatibility. We investigate the effectiveness of
DA strategies at different stages by systematically varying the number of
simulated and observed emissions used, exploring the implications of data
scarcity on the adaptation strategies. To the best of our knowledge, there are
no prior investigations of DA in satellite-derived BVOC maps enhancement. Our
work represents a first step toward the development of robust strategies for
the reconstruction of observed BVOC emissions.