Biogenic Volatile Organic Compounds (BVOCs) emitted from the terrestrial ecosystem into the Earth's atmosphere are an important component of atmospheric chemistry. Due to the scarcity of measurement, a reliable enhancement of BVOCs emission maps can aid in providing denser data for atmospheric chemical, climate, and air quality models. In this work, we propose a strategy to super-resolve coarse BVOC emission maps by simultaneously exploiting the contributions of different compounds. To this purpose, we first accurately investigate the spatial inter-connections between several BVOC species. Then, we exploit the found similarities to build a Multi-Image Super-Resolution (MISR) system, in which a number of emission maps associated with diverse compounds are aggregated to boost Super-Resolution (SR) performance. We compare different configurations regarding the species and the number of joined BVOCs. Our experimental results show that incorporating BVOCs' relationship into the process can substantially improve the accuracy of the super-resolved maps. Interestingly, the best results are achieved when we aggregate the emission maps of strongly uncorrelated compounds. This peculiarity seems to confirm what was already guessed for other data-domains, i.e., joined uncorrelated information are more helpful than correlated ones to boost MISR performance. Nonetheless, the proposed work represents the first attempt in SR of BVOC emissions through the fusion of multiple different compounds.

翻译：从陆地生态系统排放到地球大气中的生物源挥发性有机化合物（BVOCs）是大气化学的重要组成部分。由于测量数据的稀缺性，可靠增强BVOC排放图有助于为大气化学、气候和空气质量模型提供更密集的数据。在本工作中，我们提出了一种策略，通过同时利用不同化合物的贡献，对粗分辨率BVOC排放图进行超分辨率重建。为此，我们首先精确研究了多种BVOC物种之间的空间关联性。随后，利用发现的相似性构建了一个多图像超分辨率（MISR）系统，该系统聚合了与不同化合物相关的多个排放图，以提升超分辨率（SR）性能。我们比较了不同物种组合及联合BVOC数量的配置方案。实验结果表明，将BVOC之间的关联纳入处理过程能显著提高超分辨率图的准确性。有趣的是，当聚合强不相关化合物的排放图时，获得了最佳结果。这一特性似乎印证了其他数据领域的已有推测，即联合不相关信息比相关信息更有助于提升MISR性能。尽管如此，本工作首次尝试了通过融合多种不同化合物实现BVOC排放的超分辨率。