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：陆地生态系统向地球大气排放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。由于观测数据稀缺，可靠提升BVOC排放地图的分辨率有助于为大气化学、气候和空气质量模型提供更密集的数据。本文提出一种策略，通过同时利用不同化合物的贡献，实现粗分辨率BVOC排放地图的超分辨率重建。为此，我们首先精确研究了多种BVOC物种之间的空间关联性，继而利用发现的相似性构建了多图像超分辨率（MISR）系统，该系统聚合与不同化合物相关的多个排放地图以提升超分辨率（SR）性能。我们对比了不同物种组合及BVOC数量配置的实验结果，表明将BVOC间的关联性纳入处理过程可显著提升超分辨率地图的精度。有趣的是，聚合强不相关化合物的排放地图时取得了最佳效果。这一特性似乎印证了其他数据领域的已有猜想——即联合不相关信息比相关信息更有利于提升MISR性能。尽管如此，本研究首次尝试通过融合多种不同化合物来实现BVOC排放的超分辨率重建。