  Biogenic Volatile Organic Compounds (BVOCs) play a critical role in
biosphere-atmosphere interactions, being a key factor in the physical and
chemical properties of the atmosphere and climate. Acquiring large and
fine-grained BVOC emission maps is expensive and time-consuming, so most
available BVOC data are obtained on a loose and sparse sampling grid or on
small regions. However, high-resolution BVOC data are desirable in many
applications, such as air quality, atmospheric chemistry, and climate
monitoring. In this work, we investigate the possibility of enhancing BVOC
acquisitions, further explaining the relationships between the environment and
these compounds. We do so by comparing the performances of several
state-of-the-art neural networks proposed for image Super-Resolution (SR),
adapting them to overcome the challenges posed by the large dynamic range of
the emission and reduce the impact of outliers in the prediction. Moreover, we
also consider realistic scenarios, considering both temporal and geographical
constraints. Finally, we present possible future developments regarding SR
generalization, considering the scale-invariance property and super-resolving
emissions from unseen compounds.
