Leveraging Land Cover Priors for Isoprene Emission Super-Resolution
This work addresses a domain-specific problem for atmospheric modeling and climate research by providing a cost-effective, data-driven approach to refine emission maps, though it appears incremental as it builds on existing super-resolution methods with land cover integration.
The paper tackles the problem of limited spatial resolution in satellite-derived biogenic volatile organic compound emissions by proposing a deep learning-based super-resolution framework that leverages land cover priors, resulting in significantly improved accuracy, particularly in heterogeneous landscapes.
Remote sensing plays a crucial role in monitoring Earth's ecosystems, yet satellite-derived data often suffer from limited spatial resolution, restricting their applicability in atmospheric modeling and climate research. In this work, we propose a deep learning-based Super-Resolution (SR) framework that leverages land cover information to enhance the spatial accuracy of Biogenic Volatile Organic Compounds (BVOCs) emissions, with a particular focus on isoprene. Our approach integrates land cover priors as emission drivers, capturing spatial patterns more effectively than traditional methods. We evaluate the model's performance across various climate conditions and analyze statistical correlations between isoprene emissions and key environmental information such as cropland and tree cover data. Additionally, we assess the generalization capabilities of our SR model by applying it to unseen climate zones and geographical regions. Experimental results demonstrate that incorporating land cover data significantly improves emission SR accuracy, particularly in heterogeneous landscapes. This study contributes to atmospheric chemistry and climate modeling by providing a cost-effective, data-driven approach to refining BVOC emission maps. The proposed method enhances the usability of satellite-based emissions data, supporting applications in air quality forecasting, climate impact assessments, and environmental studies.