# Super-Resolution of BVOC Maps by Adapting Deep Learning Methods

arXiv: https://arxiv.org/abs/2302.07570 · HTML: https://mlsanity.com/paper/2302.07570/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks](https://mlsanity.com/paper/2007.03107/) (2020)
- [AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results](https://mlsanity.com/paper/1911.01249/) (2019)
- [Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution](https://mlsanity.com/paper/2202.10753/) (2022)

## Built on by (most cited first, 3 of 3)

- [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)
- [Leveraging Land Cover Priors for Isoprene Emission Super-Resolution](https://mlsanity.com/paper/2503.18658/) (2025)

## Abstract (arXiv)

Biogenic Volatile Organic Compounds (BVOCs) play a critical role in biosphere-atmosphere interactions, being a key factor in the physical and chemical properties of the atmosphere and climate. Acquiring large and fine-grained BVOC emission maps is expensive and time-consuming, so most available BVOC data are obtained on a loose and sparse sampling grid or on small regions. However, high-resolution BVOC data are desirable in many applications, such as air quality, atmospheric chemistry, and climate monitoring. In this work, we investigate the possibility of enhancing BVOC acquisitions, further explaining the relationships between the environment and these compounds. We do so by comparing the performances of several state-of-the-art neural networks proposed for image Super-Resolution (SR), adapting them to overcome the challenges posed by the large dynamic range of the emission and reduce the impact of outliers in the prediction. Moreover, we also consider realistic scenarios, considering both temporal and geographical constraints. Finally, we present possible future developments regarding SR generalization, considering the scale-invariance property and super-resolving emissions from unseen compounds.