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重要因素。获取大规模高分辨率的BVOC排放地图成本高昂且耗时，因此现有大部分BVOC数据均基于稀疏采样网格或小区域获得。然而，在空气质量监测、大气化学分析和气候监控等诸多应用中，高分辨率BVOC数据具有重要价值。本研究通过比较多种最新图像超分辨率（SR）神经网络模型的性能，探索增强BVOC数据采集的可能性，进一步阐明环境与这些化合物之间的关联。我们通过适配这些模型来克服BVOC排放动态范围广泛带来的挑战，并减少异常值对预测结果的影响。此外，本研究还考虑包含时间和地理约束的真实场景。最后，我们基于尺度不变性特性与未知化合物排放的超分辨率重建，探讨超分辨率泛化能力的未来发展方向。