  Biogenic Volatile Organic Compounds (BVOCs) emitted from the terrestrial
ecosystem into the Earth's atmosphere are an important component of atmospheric
chemistry. Due to the scarcity of measurement, a reliable enhancement of BVOCs
emission maps can aid in providing denser data for atmospheric chemical,
climate, and air quality models. In this work, we propose a strategy to
super-resolve coarse BVOC emission maps by simultaneously exploiting the
contributions of different compounds. To this purpose, we first accurately
investigate the spatial inter-connections between several BVOC species. Then,
we exploit the found similarities to build a Multi-Image Super-Resolution
(MISR) system, in which a number of emission maps associated with diverse
compounds are aggregated to boost Super-Resolution (SR) performance. We compare
different configurations regarding the species and the number of joined BVOCs.
Our experimental results show that incorporating BVOCs' relationship into the
process can substantially improve the accuracy of the super-resolved maps.
Interestingly, the best results are achieved when we aggregate the emission
maps of strongly uncorrelated compounds. This peculiarity seems to confirm what
was already guessed for other data-domains, i.e., joined uncorrelated
information are more helpful than correlated ones to boost MISR performance.
Nonetheless, the proposed work represents the first attempt in SR of BVOC
emissions through the fusion of multiple different compounds.
