# Fast and Accurate Retrieval of Methane Concentration from Imaging Spectrometer Data Using Sparsity Prior

arXiv: https://arxiv.org/abs/2003.02978 · HTML: https://mlsanity.com/paper/2003.02978/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 12 of 12)

- [HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensing](https://mlsanity.com/paper/2410.17248/) (2024)
- [Towards Operational Automated Greenhouse Gas Plume Detection and Delineation](https://mlsanity.com/paper/2505.21806/) (2025)
- [Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation](https://mlsanity.com/paper/2211.15429/) (2022)
- [Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware](https://mlsanity.com/paper/2507.01472/) (2025)
- [Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes](https://mlsanity.com/paper/2401.12870/) (2024)
- [Operational machine learning for remote spectroscopic detection of CH4 point sources](https://mlsanity.com/paper/2511.07719/) (2025)
- [Towards Methane Detection Onboard Satellites](https://mlsanity.com/paper/2509.00626/) (2025)
- [Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy](https://mlsanity.com/paper/2509.19665/) (2025)
- [FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery](https://mlsanity.com/paper/2606.01577/) (2026)
- [Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing](https://mlsanity.com/paper/2605.24273/) (2026)
- [A Fast Methane Detection Pipeline on Board Satellites Based on Mag1c-SAS and LinkNet](https://mlsanity.com/paper/2606.03675/) (2026)
- [Methane Detection On Board Satellites from Unorthorectified Imagery](https://mlsanity.com/paper/2609.04906/) (2026)

## Abstract (arXiv)

The strong radiative forcing by atmospheric methane has stimulated interest in identifying natural and anthropogenic sources of this potent greenhouse gas. Point sources are important targets for quantification, and anthropogenic targets have potential for emissions reduction. Methane point source plume detection and concentration retrieval have been previously demonstrated using data from the Airborne Visible InfraRed Imaging Spectrometer Next Generation (AVIRIS-NG). Current quantitative methods have tradeoffs between computational requirements and retrieval accuracy, creating obstacles for processing real-time data or large datasets from flight campaigns. We present a new computationally efficient algorithm that applies sparsity and an albedo correction to matched filter retrieval of trace gas concentration-pathlength. The new algorithm was tested using AVIRIS-NG data acquired over several point source plumes in Ahmedabad, India. The algorithm was validated using simulated AVIRIS-NG data including synthetic plumes of known methane concentration. Sparsity and albedo correction together reduced the root mean squared error of retrieved methane concentration-pathlength enhancement by 60.7% compared with a previous robust matched filter method. Background noise was reduced by a factor of 2.64. The new algorithm was able to process the entire 300 flightline 2016 AVIRIS-NG India campaign in just over 8 hours on a desktop computer with GPU acceleration.