Improving quantification of methane point source emissions from imaging spectroscopy

Pei, Zhipeng; Han, Ge; Mao, Huiqin; Chen, Cuihong; Shi, Tianqi; Yang, Kuo-Hsin; Ma, Xin; Gong, Wei

doi:10.1016/j.rse.2023.113652

Cited by 33 publications

(17 citation statements)

References 41 publications

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“…Δ c is the methane column enhancements and k is the unit methane absorption coefficient, which is extracted from simulated radiance by the MODTRAN model, following Pei et al. (2023). Then Δ c can be retrieved by fitting the observed radiance spectrum ( x ) and modeled radiance spectrum ( x m ) in the SWIR spectral region:

{\Delta }c=\frac{{(x-\mu )}^{T}{{\Sigma }}^{-1}\mu k}{{k}^{T}{{\Sigma }}^{-1}\mu k}.

…”

Section: Methodsmentioning

confidence: 99%

“…Here x r is the background spectrum, which is not affected by the methane concentration enhancement. Δc is the methane column enhancements and k is the unit methane absorption coefficient, which is extracted from simulated radiance by the MODTRAN model, following Pei et al (2023). Then Δc can be retrieved by fitting the observed radiance spectrum (x) and modeled radiance spectrum (x m ) in the SWIR spectral region:…”

Section: Methane Retrievalsmentioning

confidence: 99%

See 1 more Smart Citation

Unveiling Unprecedented Methane Hotspots in China's Leading Coal Production Hub: A Satellite Mapping Revelation

Han,

Pei,

Shi

et al. 2024

Geophysical Research Letters

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China is likely the world's largest anthropogenic source of methane emissions, with coal mine methane (CMM) being the predominant contributor. Here, we deploy 2 years of satellite observations to survey facility‐level CMM emitters in Shanxi, the most prolific coal mining province in China. A total of 138 detected episodic events at 82 facilities are estimated to emit 1.20 (+0.24/−0.20, 95% CI) million tons of methane per year (Mt CH4/yr) during 2021–2023, roughly equivalent to 4.2 times the integrated flux from the Permian plumes and four times of the integrated flux from the Four Corners plumes, two of the world's largest hotspots for oil and gas methane emissions. This work reveals the heavy‐tailed distribution characteristic of CMM emission sources for the first time, with 20% of emitters contributing approximately 50% of total emissions. Comparison with the Global Energy Monitor (GEM) inventory reveals that the GEM estimate is about 4.1 times our estimate.

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{\Delta }c=\frac{{(x-\mu )}^{T}{{\Sigma }}^{-1}\mu k}{{k}^{T}{{\Sigma }}^{-1}\mu k}.

…”

Section: Methodsmentioning

confidence: 99%

Section: Methane Retrievalsmentioning

confidence: 99%

Unveiling Unprecedented Methane Hotspots in China's Leading Coal Production Hub: A Satellite Mapping Revelation

Han,

Pei,

Shi

et al. 2024

Geophysical Research Letters

Self Cite

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“…In addition, Pei et al [70] point out that the traditional matched filter algorithm is only applicable to point sources with small emissions, and they propose a logarithmic matched filter algorithm, i.e., using the logarithmic normal distribution as a substitute for the spectral background model. Based on this, the iterative computation of anomaly rejection can improve the inversion accuracy.…”

Section: Matched Filtermentioning

confidence: 99%

Methane Retrieval Algorithms Based on Satellite: A Review

Jiang,

Zhang,

Zhang

et al. 2024

Atmosphere

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As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research.

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“…In the context of global warming, the climate problems caused by greenhouse gas (GHG) emissions have received widespread attention in recent years [1][2][3][4][5][6]. In December 2020, at the 75th Session of the United Nations General Assembly, China formally and explicitly proposed reducing the intensity of carbon emissions and formulating an action program for carbon emissions peaking by 2030, followed by a steady decline after peaking [7].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT

Cai,

Xiang

2024

Atmosphere

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Remote sensing satellite monitoring involving the use of shortwave infrared (SWIR) solar backscatter radiation to measure atmospheric CH4 column concentrations provides wide−ranging and accurate data for quantitatively determining atmospheric CH4 emissions and is highly important for human studies of atmospheric composition and environmental protection. The ESA−launched Sentinel−2 satellite equipped with a tropospheric monitoring instrument (TROPOMI) can provide the concentration of CH4 columns in every piece of the global atmosphere every day. However, these data may be affected by surface albedo, SWIR, aerosols, cirrus cloud scattering, and other factors. The greenhouse gas observing satellite (GOSAT) launched by Japan has fairly accurate data that are minimally affected by the aforementioned factors; however, its data density is much less than that of the TROPOMI. In this study, we propose a CH4 model that combines the TROPOMI and GOSAT data. We construct the model by analyzing the data from the TROPOMI and GOSAT at the same location at the same time. Then, we apply the proposed model to a certain location at a certain time with TROPOMI data but without GOSAT data to obtain a large range of high−precision CH4 data. The most developed urban agglomeration in the Yangtze River Delta in China was selected for model construction and the correlations between the TROPOMI and GOSAT data and their spatial and temporal trends were analyzed. First, we analyzed the CH4 concentrations in the same area measured by both models. The results revealed a high degree of temporal and spatial correlation in the YRD region. The correlation coefficient reached 0.71 in the metropolitan area of the YRD. At the small−city scale, the correlation is much more significant, with the correlation reaching 0.80, 0.79, and 0.71 for Nanjing, Shanghai, and Ningbo, respectively. The most accurate model was screened through comparative construction to calibrate the TROPOMI data and high−precision and high−coverage CH4 concentration information was obtained for the study area. Five models (linear model, quadratic term model, cubic term model, lognormal model, and logistic model) were used to select the best−fitting model. The magnitudes of the differences in the CH4 concentrations calculated by each model were compared. The final results showed that the linear model, as the prediction model, had the highest accuracy, with a coefficient of determination (R22) of 0.542. To avoid the specificity of the constructed model, we used the same method in several simulations to validate. The coefficient of determination of the model constructed with different stochastic data was greater than 0.5. Subsequently, we used Nanjing as the study area and applied the same method to construct the model. The coefficient of determination of the model (R22) was approximately 0.601. The model constructed in this research can be used not only for data conversion between the same products from different sensors to obtain high−precision data products but also for calibrating newly developed satellite data products that utilize mature data products.

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Improving quantification of methane point source emissions from imaging spectroscopy

Cited by 33 publications

References 41 publications

Unveiling Unprecedented Methane Hotspots in China's Leading Coal Production Hub: A Satellite Mapping Revelation

Unveiling Unprecedented Methane Hotspots in China's Leading Coal Production Hub: A Satellite Mapping Revelation

Methane Retrieval Algorithms Based on Satellite: A Review

Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT

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