Impact of scene-specific enhancement spectra on matched filter greenhouse gas retrievals from imaging spectroscopy

Foote, Markus D.; Dennison, Philip E.; Sullivan, Patrick; O'Neill, Kelly; Thorpe, A. K.; Thompson, David R.; Cusworth, Daniel H.; Duren, Riley; Joshi, Sarang

doi:10.1016/j.rse.2021.112574

Cited by 23 publications

(12 citation statements)

References 29 publications

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“…The other spectral region spans from 2100 to 2500 nm, exhibiting a stronger CH4 absorption signal. The 𝒌 spectrum can be generated by MODerate resolution atmospheric TRANsmission (MODTRAN; Berk et al, 2014) radiative transfer model (Foote et al, 2021). Moreover, spectral features vary from satellites due to different spectral response functions (SRF).…”

Section: Mf Methodsmentioning

confidence: 99%

An optimized methane retrieval approach based on Kalman filter for mapping methane point emissions from spaceborne imaging spectrometer

Feng,

Li,

Roger

et al. 2024

Preprint

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Methane (CH4) emissions from point sources in the energy sector plays a crucial role in global CH4 budget. Spaceborne imaging spectrometer has shown superior ability for monitoring these emission events over large spatial coverages and extended timeframes. Presently, the Matched Filter (MF), which is a data-driven method, is widely employed for satellite-based retrieval of CH4 emission flux rates. However, traditional MF method faces challenges such as omission of CH4 plumes and underestimation of CH4 emission flux rate, which may lead to significant uncertainties in the CH4 inventories for the energy industry at global scales. In this study, we propose a new Kalman-Filtering Matched Filter (KMF) algorithm as the improvement of the MF method, incorporating a linear combination of MF results in different CH4 absorption channels to reduce the retrieval bias of point-source CH4 enhancement values. We validate this algorithm in two stages. Retrieval accuracy for the enhancement of column-averaged dry-air mole fraction of CH4 (XCH4) relative to the background (ΔXCH4) is tested using end-to-end simulation and emission-free scenario analysis. Additionally, data from hyperspectral satellites including Chinese Gaofen 5B, Ziyuan 1F, Italian PRISMA, and German EnMAP are used to retrieve CH4 emissions from a ground-based controlled-releasing experiment using our proposed KMF and traditional MF algorithm. We then compare the retrieval results with ground metered-measurements to evaluate the performance on estimating CH4 emission flux rate of our method. The results from end-to-end simulations show that the KMF method exhibits a 34.3% higher fitted-line slope and 25.5% lower root mean square error (RMSE) on ΔXCH4 retrieval compared with MF method. Further analysis in an emission-free scenario indicates that the retrieval precision with the KMF method can improve by up to 42.2% compared to the conventional MF method. Comparison from controlled-releasing experiment data reveals the capability of the KMF method to detect minor CH4 emissions that traditional MF methods fail to identify. Meanwhile, the KMF-based emission rate quantifications have an R2 of 0.99 and a small RMSE of 0.18 ton of CH4 per hour, which shows an approximately 62% reduction on RMSE. We further apply the KMF algorithm to Gaofen 5B and Gaofen 5 data in various regions, including the Delaware Basin (USA), Libya, Oman, and Shanxi (China) from 2021 to 2023, and focus on 16 plumes identified through case studies, highlighting the KMF algorithm's robust detection capabilities for CH4 point source emissions in the energy industry.

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Section: Mf Methodsmentioning

confidence: 99%

An optimized methane retrieval approach based on Kalman filter for mapping methane point emissions from spaceborne imaging spectrometer

Feng,

Li,

Roger

et al. 2024

Preprint

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“…First, it uses wavelength bands within the range 1,928–2,200 nm where CO 2 has strong absorption effects on the radiance. Second, it simulates CO 2 concentration enhancements above background, starting at 0 ppm‐m and incrementally doubling up from 20,000 to 1,280,000 ppm‐m (Foote et al., 2021). Finally, it applies an independent matched filter to a group of 50 adjacent columns of the image to improve the covariance estimate by suppressing artifacts from non‐uniformity among detector elements (Ayasse et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

“…They estimated emissions from 17 coal and gas fired power plants in the U.S. with robust correlation with simultaneous in situ measured data. Foote et al (2021) also quantified seven CO 2 emissions examples, concluding that generating the scene-specific unit enhancement spectra could achieve quantification improvement. However, these studies have primarily focused on power plants, as they are often high-volume point sources of CO 2 emissions equipped with continuous emissions monitoring systems (CEMS) that provide in situ measured data for calibration purposes (U.S. EPA, 2023).…”

mentioning

confidence: 99%

Measuring Carbon Dioxide Emissions From Liquefied Natural Gas (LNG) Terminals With Imaging Spectroscopy

Zhang,

Cusworth,

Ayasse

et al. 2023

Geophysical Research Letters

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The rapid growth of liquefied natural gas (LNG) exports underscores the importance of CO2 monitoring for LNG export terminals. We present a method for measuring LNG terminal CO2 emissions using remote sensing imaging spectroscopy. The method is first validated using 47 power plant emission events with in situ measured data, then applied to 22 emission events in Sabine Pass and Cameron. The power plant data set shows a robust correlation between our estimates and in situ data, with R2 0.9146 and the average error −2%. At Sabine Pass, eight point sources are identified with emission rates from 219.69 ± 54.95 to 1,083.22 ± 308.06 t/hr. At Cameron, three point sources are identified with emission rates from 91.64 ± 25.81 to 265.61 ± 67.80 t/hr. The liquefaction carbon intensity estimates also align with past study ranges. This illustrates that remote sensing can validate environmental reporting and CO2 inventories for industrial facilities.

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“…where i ∈ [0,n] are the pixels selected through manual plume segmentation, S is the area of a pixel, and αi is the concentration path length enhancement within each pixel. The constant k represents the conversion factor from concentration-path length (in ppm-m) to mass of CH4 (typically in kilograms); it is derived by dividing the molecular mass of the gas and the volume of one mole of an ideal gas at standard pressure and temperature with appropriate unit conversions (Duren et al 2019;Foote et al 2021). To minimize the variability of IME with respect to rc, IME is calculated for the entire range of radii (rc) then its standard deviation (σIME/rc), representing the uncertainty in IME/rc estimates, is calculated.…”

Section: Plume Detection and Emission Flux Estimationmentioning

confidence: 99%

Detecting Methane Emissions from Space in India: analysis using EMIT and Sentinel-5P TROPOMI datasets

Siddiqui,

Halder,

Kannemadugu

et al. 2024

Preprint

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Methane (CH4) is a potent greenhouse gas and the second highest anthropogenic emissions are recorded from CH4 on Earth. Considering its high global warming potential, the monitoring of source locations is inadvertent. The paper presented here is the first attempt (to the best of our knowledge) to comprehensively analyse the methane emissions over multiple Indian locations using satellite data. It outlays a brief background of methane emission sensors and studies carried out worldwide for estimation of the GHG. It further enumerates the potential of Jet Propulsion Laboratory’s Earth Surface Mineral Dust Source Investigation (EMIT) and TROPOMI in highlighting the potential point sources of methane emissions and its concentration/emission flux in India. 17 unique plumes were identified using EMIT in states of Maharashtra (06), Rajasthan (04), Punjab (02), Gujarat (03) and Assam (02). Gujarat, Surat, Assam Uttar Pradesh and Haryana using TROPOMI were also studied. The hotspots showcase emission sources from solid waste landfill sites (SW), sewage treatment plant (STP), wetlands/marshy agriculture (WT), city sewage outlet (CS), oil and gas field (O&G), oil refinery (OR) and textile industry (TI). It was observed that EMIT can effectively be used for point source identification, monitoring and enhancement while TROPOMI is best suited for regional level methane monitoring. A sewage outlet (SO) plume in Maharashtra produced the maximum emission of 6202.9 ± 691.94 kg/hr followed by solid waste (SW) sites located in Pirana Landfill, Ahmedabad and Khajod Landfill, Surat in Gujarat. Methane monitoring is an important step towards mitigating enormous methane emissions and anomalous methane sources.

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Impact of scene-specific enhancement spectra on matched filter greenhouse gas retrievals from imaging spectroscopy

Cited by 23 publications

References 29 publications

An optimized methane retrieval approach based on Kalman filter for mapping methane point emissions from spaceborne imaging spectrometer

An optimized methane retrieval approach based on Kalman filter for mapping methane point emissions from spaceborne imaging spectrometer

Measuring Carbon Dioxide Emissions From Liquefied Natural Gas (LNG) Terminals With Imaging Spectroscopy

Detecting Methane Emissions from Space in India: analysis using EMIT and Sentinel-5P TROPOMI datasets

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