# Matched-filter trace-gas plume detection
*analysis . methodologies*

Detects and quantifies localised trace-gas enhancements by applying a matched filter tuned to the target gas absorption signature across an imaging-spectrometer scene, isolating plumes against a variable surface background. Used for methane and carbon dioxide point-source mapping.

## Specifications
- **family**: Spectral analysis
- **entity type**: methodology
- **last verified date**: 2026-06-04
- **verified by**: agency-doc
- **claim status**: unclaimed
- **subtype**: analysis
- **attributes**: {"family":"Spectral analysis","kind":"analysis","summary":"Detects and quantifies localised trace-gas enhancements by applying a matched filter tuned to the target gas absorption signature across an imaging-spectrometer scene, isolating plumes against a variable surface background. Used for methane and carbon dioxide point-source mapping."}

## Editorial
Matched-filter trace-gas plume detection answers the question: is there a localised enhancement of a target gas at this location, and how large is it? The method applies a statistical filter tuned to the spectral absorption signature of a specific gas, extracting the concentration signal from the variable surface background without requiring an explicit atmospheric retrieval model for each pixel.

The column-wise matched filter operates on imaging spectrometer radiance cubes. A target spectrum is derived from the lab-measured gas transmittance (for methane, the SWIR window around 2200-2430 nm; for CO2, around 2020-2070 nm). The background is modelled as a column-wise multivariate Gaussian distribution, fitted per image column to capture the spatial structure of the scene. The filter projects each pixel's spectrum onto the target direction in spectral space, producing a concentration-pathlength enhancement in ppm-m. Integrated emission rates are estimated from the concentration-pathlength map using a cross-sectional flux method.[^avirisng-benchmark]

The mag1c algorithm (Foote et al. 2020) extends the basic matched filter with L1 sparse regularisation and albedo correction, yielding 60.7% lower RMSE and 2.64x reduction in background noise relative to earlier robust matched filter implementations.[^foote-2020-mag1c] AVIRIS-NG JPL greenhouse gas survey flights have applied this pipeline operationally for methane and CO2 point-source mapping.[^avirisng-ghg-mapping]

The method requires a VSWIR imaging spectrometer with SWIR coverage to at least 2300 nm for methane, spectral resolution under 10 nm, and SNR above 200:1 in the SWIR for reliable sub-1000 ppm-m retrievals. Detection thresholds are typically 200-500 ppm-m for methane at 3-8 m resolution. Failure modes include low-albedo surfaces (water, dark soils, shadow), surface materials with spectral structure that mimics the target gas absorption, and high aerosol loading.

## Sources
- [foote-2020-mag1c] | Fast and Accurate Retrieval of Methane Concentration from Imaging Spectrometer Data Using Sparsity Prior | https://arxiv.org/pdf/2003.02978 | tier=peer-reviewed | accessed=2026-06-04 | author=Foote M.D. et al.
  (2020) mag1c algorithm: sparse L1-regularised albedo-corrected matched filter; 60.7% RMSE reduction vs prior robust MF; 2.64x background noise reduction; AVIRIS-NG application
- [avirisng-benchmark] | Benchmark Dataset for Methane and Carbon Dioxide Plumes | https://avirisng.jpl.nasa.gov/benchmark_methane_carbon_dioxide.html | tier=agency-doc | accessed=2026-06-04 | author=NASA JPL AVIRIS-NG team
  (2022) Benchmark dataset description; mag1c L1 sparse albedo corrected MF used for retrievals; confirms column-wise multivariate Gaussian background
- [avirisng-ghg-mapping] | Greenhouse Gas Mapping - AVIRIS-Next Generation | https://avirisng.jpl.nasa.gov/greenhouse_gas_mapping.html | tier=agency-doc | accessed=2026-06-04 | author=NASA JPL
  (2023) Operational GHG mapping using matched filter on AVIRIS-NG; point source emission rate estimation via cross-sectional flux

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Source: https://eo-atlas.org/methodologies/matched-filter-plume-detection
Maintainer: SpectraWorks B.V. (CC-BY 4.0)