Enhancing the resolution of Biogenic Volatile Organic Compound (BVOC) emission maps is a critical task in remote sensing. Recently, some Super-Resolution (SR) methods based on Deep Learning (DL) have been proposed, leveraging data from numerical simulations for their training process. However, when dealing with data derived from satellite observations, the reconstruction is particularly challenging due to the scarcity of measurements to train SR algorithms with. In our work, we aim at super-resolving low resolution emission maps derived from satellite observations by leveraging the information of emission maps obtained through numerical simulations. To do this, we combine a SR method based on DL with Domain Adaptation (DA) techniques, harmonizing the different aggregation strategies and spatial information used in simulated and observed domains to ensure compatibility. We investigate the effectiveness of DA strategies at different stages by systematically varying the number of simulated and observed emissions used, exploring the implications of data scarcity on the adaptation strategies. To the best of our knowledge, there are no prior investigations of DA in satellite-derived BVOC maps enhancement. Our work represents a first step toward the development of robust strategies for the reconstruction of observed BVOC emissions.

翻译：提升生物源挥发性有机化合物（BVOC）排放图的分辨率是遥感领域的关键任务。近年来，一些基于深度学习（DL）的超分辨率（SR）方法通过利用数值模拟数据训练模型而得到提出。然而，在处理源自卫星观测的数据时，由于可用于训练SR算法的测量数据稀缺，重建过程极具挑战性。本研究旨在通过利用数值模拟获得的排放图信息，对卫星观测所得的低分辨率排放图进行超分辨率重建。为此，我们将基于深度学习的SR方法与域自适应（DA）技术相结合，调和模拟域与观测域中使用的不同聚合策略和空间信息，以确保兼容性。通过系统性地改变使用的模拟与观测排放数据量，我们研究了DA策略在不同阶段的有效性，并探究了数据稀缺性对自适应策略的影响。据我们所知，此前尚无将域自适应技术应用于卫星衍生BVOC图增强的研究。本研究标志着迈向开发用于重建观测BVOC排放的稳健策略的第一步。