Impact of Scene-Specific Enhancement Spectra on Matched Filter Greenhouse Gas Retrievals from Imaging Spectroscopy
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Abstract (arXiv)
Matched filter (MF) techniques have been widely used for retrieval of greenhouse gas enhancements (enh.) from imaging spectroscopy datasets. While multiple algorithmic techniques and refinements have been proposed, the greenhouse gas target spectrum used for concentration enh. estimation has remained largely unaltered since the introduction of quantitative MF retrievals. The magnitude of retrieved methane and carbon dioxide enh., and thereby integrated mass enh. (IME) and estimated flux of point-source emitters, is heavily dependent on this target spectrum. Current standard use of molecular absorption coefficients to create unit enh. target spectra does not account for absorption by background concentrations of greenhouse gases, solar and sensor geometry, or atmospheric water vapor absorption. We introduce geometric and atmospheric parameters into the generation of scene-specific (SS) unit enh. spectra to provide target spectra that are compatible with all greenhouse gas retrieval MF techniques. For methane plumes, IME resulting from use of standard, generic enh. spectra varied from -22 to +28.7% compared to SS enh. spectra. Due to differences in spectral shape between the generic and SS enh. spectra, differences in methane plume IME were linked to surface spectral characteristics in addition to geometric and atmospheric parameters. IME differences for carbon dioxide plumes, with generic enh. spectra producing integrated mass enh. -76.1 to -48.1% compared to SS enh. spectra. Fluxes calculated from these integrated enh. would vary by the same %s, assuming equivalent wind conditions. Methane and carbon dioxide IME were most sensitive to changes in solar zenith angle and ground elevation. SS target spectra can improve confidence in greenhouse gas retrievals and flux estimates across collections of scenes with diverse geometric and atmospheric conditions.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Jun 2021.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 2.
5 papers.
- 2024HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensingcited by 11
- 2025Towards Operational Automated Greenhouse Gas Plume Detection and Delineationcited by 6
- 2024Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumescited by 3
- 2026PlumeQuant: Uncertainty-aware consistency assessment of methane plume masks and emission-rate estimatescited by 0
- 2026Efficient Neural-Network-Based High-Resolution Radiative Transfer for CO___ Retrieval, and Application to Interferometric Sensingcited by 0
- 2026Efficient Neural-Network-Based High-Resolution Radiative Transfer for CO___ Retrieval, and Application to Interferometric Sensing29 Jul 2026
- 2026PlumeQuant: Uncertainty-aware consistency assessment of methane plume masks and emission-rate estimates15 Jul 2026
- 2025Towards Operational Automated Greenhouse Gas Plume Detection and Delineation27 May 2025
- 2024HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensing22 Oct 2024
- 2024Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes23 Jan 2024