# Leveraging Land Cover Priors for Isoprene Emission Super-Resolution

arXiv: https://arxiv.org/abs/2503.18658 · HTML: https://mlsanity.com/paper/2503.18658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 96 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 17 of 17)

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- [DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution](https://mlsanity.com/paper/1703.03126/) (2017)
- [Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward](https://mlsanity.com/paper/2305.08413/) (2023)
- [Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks](https://mlsanity.com/paper/2007.03107/) (2020)
- [Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation](https://mlsanity.com/paper/2111.03231/) (2021)
- [Fast and accurate learned multiresolution dynamical downscaling for precipitation](https://mlsanity.com/paper/2101.06813/) (2021)
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- [On the modern deep learning approaches for precipitation downscaling](https://mlsanity.com/paper/2207.00808/) (2022)
- [Deep generative model super-resolves spatially correlated multiregional climate data](https://mlsanity.com/paper/2209.12433/) (2022)
- [Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications](https://mlsanity.com/paper/2311.03378/) (2023)
- [Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution](https://mlsanity.com/paper/2202.10753/) (2022)
- [Super-Resolution of BVOC Maps by Adapting Deep Learning Methods](https://mlsanity.com/paper/2302.07570/) (2023)
- [Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection](https://mlsanity.com/paper/2305.14180/) (2023)
- [Super-Resolution of BVOC Emission Maps Via Domain Adaptation](https://mlsanity.com/paper/2306.12796/) (2023)
- [Climate Downscaling: A Deep-Learning Based Super-resolution Model of Precipitation Data with Attention Block and Skip Connections](https://mlsanity.com/paper/2403.17847/) (2024)

## Abstract (arXiv)

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.