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.

翻译：生物源挥发性有机物（BVOCs）在生物圈-大气相互作用中扮演关键角色，是影响大气物理化学性质及气候的关键因素。获取大规模、高分辨率的BVOC排放地图成本高昂且耗时，因此现有BVOC数据大多基于稀疏采样网格或小区域获取。然而，在空气质量监测、大气化学及气候监测等众多应用中，高分辨率BVOC数据具有重要价值。本研究探索了提升BVOC采集分辨率的可能性，进一步阐释环境与这些化合物之间的关系。我们通过比较多种为图像超分辨率（SR）提出的先进神经网络性能，并对其进行适配以克服BVOC排放量程大带来的挑战，同时减少异常值对预测的影响。此外，我们还考虑了现实场景中的时间与地理约束。最后，我们基于尺度不变性特性及对未见化合物排放的超分辨率处理，提出了超分辨率泛化方面的未来发展方向。