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：生物源挥发性有机化合物（BVOCs）在大气-生物圈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。获取大范围高精度的BVOC排放地图成本高昂且耗时，因此现有BVOC数据多基于稀疏采样网格或小区域获取。然而，在空气质量监测、大气化学分析与气候监测等诸多应用中，高分辨率BVOC数据具有重要价值。本研究通过对比多种前沿图像超分辨率（Super-Resolution, SR）神经网络模型的性能，探索增强BVOC采集数据并阐释环境与这些化合物关联的可能性。研究中针对BVOC排放数据动态范围大、异常值影响预测准确度的特点，对现有SR模型进行适应性改进。此外，我们同时考虑时间与地理约束的真实应用场景。最后，本文基于尺度不变性特性与未知化合物超分辨率重建能力，探讨了SR泛化方法的未来发展方向。