Enhancing Biogenic Emission Maps Using Deep Learning

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 of the 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 propose to investigate the possibility of enhancing BVOC acquisitions, taking a step forward in explaining the relationships between plants and these compounds. We do so by comparing the performances of several state-of-the-art neural networks proposed for Single-Image Super-Resolution (SISR), showing how to adapt them to correctly handle emission data through preprocessing. Moreover, we also consider realistic scenarios, considering both temporal and geographical constraints. Finally, we present possible future developments in terms of Super-Resolution (SR) generalization, considering the scale-invariance property and super-resolving emissions from unseen compounds.