Evaluating Methane Emission Estimates from Intergovernmental Panel on Climate Change Compared to Sentinel-Derived Air–Methane Data

Abstract

This study compares the methane emission estimates from the Intergovernmental Panel on Climate Change with satellite-based measurements from Sentinel-5P to assess trends in global methane emissions and concentrations. Focusing on the countries listed in Annex I of the United Nations Framework Convention on Climate Change and the key sectors of Agriculture, Energy, industrial processes and product use, land use, land use change, forestry, and Waste, this analysis uses data from 1990 to 2021, evaluated through the Mann–Kendall trend test. The findings reveal a decline in methane emissions reported by the IPCC, particularly in the Energy and Waste sectors, driven by stricter environmental regulations and technological advancements in these regions. However, the satellite data from the TROPOspheric Monitoring Instrument for 2019–2024 indicate an increasing trend in atmospheric methane concentrations, suggesting that the reductions reported in the inventories may be insufficient to offset ongoing or previously accumulated emissions. The discrepancies between the IPCC inventories and the satellite observations highlight the challenges in methane source attribution and the limitations of relying solely on inventory-based methods. This study demonstrates the potential of integrating high-resolution satellite data with the traditional methodologies to improve the accuracy of methane emission estimates. Such an approach provides a more comprehensive understanding of methane dynamics, particularly in regions where natural and anthropogenic sources overlap. The findings of this study contribute to a better understanding of global methane emission trends and their implications for climate change. Integrating satellite observations into national inventories has practical applications for enhancing methane monitoring, improving emission reporting, and supporting global climate goals through the more effective and sustainable management of methane emissions.

1. Introduction

Methane (CH₄) is a potent greenhouse gas (GHG) with significantly more global warming potential (GWP) than that of carbon dioxide (CO₂) [1], making its accurate measurement and reporting critical for effective climate change mitigation. With a relatively short atmospheric lifetime of approximately 12.4 years [2], CH₄ nonetheless plays a significant role in accelerating climate change due to its high efficiency in trapping radiation. For comparison, CH₄ radiative forcing is often expressed in terms of CO₂ equivalents over a 100-year time horizon (GWP100), with the latest estimate from the Intergovernmental Panel on Climate Change—the IPCC 5th Assessment Report—putting CH₄ GWP at 34 [3]. Globally, in 2017, CH₄ emissions were estimated to be at 600 Tg y⁻¹, partially offset by a CH₄ sink estimated at 570 Tg y⁻¹. Anthropogenic sources are a major contributor to total emissions, with the bottom-up estimates indicating 380 Tg y⁻¹, and the top-down estimates suggesting 360 Tg y⁻¹ for human activities [4]. Methane can form through various processes involving biological (biogenic) and non-biological (abiogenic) pathways [5,6]. Biogenic CH₄ formation occurs through the metabolic activity of methanogenic archaea in anaerobic environments. Organic matter is decomposed in waterlogged soils, producing methane through microbial activity. Rumen microbes (bacteria, protozoa, and fungi) break down cellulose, hemicellulose, and starch into simple sugars. Methanogens utilize two main pathways: hydrogenotrophic methanogenesis CO₂ + 4H₂ → CH₄ + 2H₂O and acetoclastic methanogenesis CH₃COO⁻ + H⁺ → CH₄ + CO₂ [7]. Abiogenic methane is formed through chemical or geological processes, not requiring living organisms. When water reacts with olivine-rich rocks, hydrogen is produced, which can react with CO₂ to form methane. Organic matter buried deep within the earth undergoes heat and pressure, breaking down to form hydrocarbons, including CH₄ [8,9].

CH₄ anthropogenic emissions arise from various sectors, and the Intergovernmental Panel on Climate Change (IPCC) methodology provides a standardized approach for estimating these emissions [10]. In the Energy sector, CH₄ emissions primarily originate from fossil fuel activities, such as the production, processing, transmission, and use of natural gas, oil, and coal. This includes leaks during extraction and refining, gas venting, and incomplete combustion [11]. The industrial processes and product use (IPPU) sector is another important source, contributing emissions from chemical production and high-temperature industrial processes, such as ammonia, methanol, and steel production. Although the total is smaller in scale than those of Energy or Agriculture, IPPU emissions still represent a critical component of national GHG inventories [12]. The agricultural sector is the largest anthropogenic source of CH₄ emissions, primarily through enteric fermentation in livestock, manure management, rice cultivation, and agricultural residue burning. These activities collectively contribute significant quantities of CH₄, necessitating targeted mitigation efforts, especially in countries with large agricultural sectors [13]. Land use, land use change, and forestry (LULUCF) activities also contribute to CH₄ emissions, mainly through wetland conversion, peatland degradation, and biomass burning. These emissions are highly variable, depending on the land management practices, the soil conditions, and climate factors [14]. Finally, the CH₄ emissions from the Waste sector are primarily generated by the anaerobic decomposition of organic matter in landfills and wastewater treatment facilities, with significant emissions also arising from biological waste treatment methods [15].

The significance of CH₄ emissions has drawn increased attention from the scientific community due to its critical role in climate change, but controversies remain. For instance, while the global CH₄ budget is well documented, uncertainties persist in distinguishing between natural and anthropogenic sources, and there is an ongoing debate over the most effective approaches to reducing CH₄ emissions across the sectors. In particular, disagreements exist about whether efforts should focus more on Energy sector improvements (such as preventing CH₄ leaks) or agricultural practices (like reducing livestock emissions). Thus, global efforts are required to track and reduce emissions.

Annex I countries are listed in Annex I of the United Nations Framework Convention on Climate Change (UNFCCC) [16]. The Annex I countries have specific commitments under the UNFCCC to reduce their greenhouse gas emissions and to report their progress. They have more stringent reporting requirements compared to those of non-Annex I countries and are expected to lead in efforts to combat climate change. Under the Kyoto Protocol, which further operationalizes the UNFCCC, the Annex I countries agreed to setting targets for reducing their greenhouse gas emissions. These targets varied by country, reflecting each nation’s capabilities and circumstances. National Inventory Reports (NIRs) are comprehensive documents that countries submit to detail their greenhouse gas (GHG) emissions and removals [16]. NIRs must include evidence of rigorous QA/QC procedures to ensure the accuracy and reliability of the data reported. The countries that are party to UNFCCC, particularly those listed under Annex I, must compile and submit these reports. Among others, NIRs include the emission of CH₄.

Another advanced and increasingly vital method for monitoring CH₄ emissions, offering a complementary approach to the traditional ground-based techniques, is satellite measurement. Satellites enable the large-scale, continuous monitoring of CH₄ concentrations across diverse regions, including remote and inaccessible areas. This global coverage is crucial for capturing emissions from natural sources such as wetlands and detecting anthropogenic emissions from Energy production and Agriculture. By providing near-real-time data, satellite technologies have become essential for understanding the CH₄ emission patterns, enhancing climate models, and informing mitigation strategies on a broader scale. However, detecting CH₄ emissions via satellite poses challenges, mainly because many sources, such as wetlands, sabkhas, and human activities, are often spatially confined, making it difficult to detect small-scale emissions. Despite these challenges, the Sentinel-5 Precursor (Sentinel-5P) satellite launched in 2017 has significantly enhanced the CH₄ monitoring capabilities. Equipped with the TROPOMI (TROPOspheric Monitoring Instrument), Sentinel-5P provides CH₄ data at a spatial resolution of 7 km × 7 km and offers daily global coverage [17]. This improved resolution and frequency marked a significant advancement in satellite-based monitoring.

While the previous studies have documented methane emissions using ground-based estimates [18,19,20] or satellite observations [21,22,23], a gap exists in systematically comparing the IPCC’s bottom-up data with the high-resolution, satellite-derived measurements from Sentinel-5P. This study bridges this gap by providing the integrated analysis of methane emission trends in the Annex I countries, leveraging the strengths of both the approaches. Unlike earlier research, which often focused on sector-specific emissions [24,25] or regional scales [26,27], this paper provides a comprehensive comparison across multiple countries and sectors, offering insights into the alignment and discrepancies between the reported and observed emissions (Figure 1). Such an approach is crucial for refining the national inventories and improving the accuracy of global methane budgets. By integrating the IPCC estimates with satellite measurements, this research offers insights into methane emission trends and identifies areas for targeted mitigation. This study emphasizes the importance of accurate and comprehensive emission inventories in developing effective policies to reduce greenhouse gas emissions. Ultimately, this work contributes to sustainability by enhancing methane monitoring, supporting the efforts to meet global climate goals, and guiding policies to create a more sustainable future.

2. Materials and Methods

The countries taken into account in analysis are Annex I countries and include Australia (AUS), Austria (AUT), Belarus (BLR), Belgium (BEL), Bulgaria (BGR), Canada (CAN), Croatia (HRV), Cyprus (CYP), Czechia (CZE), Denmark (DNK), Estonia (EST), Finland (FIN), France (FRA), Germany (DEU), Greece (GRC), Hungary (HUN), Iceland (ISL), Ireland (IRL), Italy (ITA), Japan (JPN), Kazakhstan (KAZ), Latvia (LVA), Lithuania (LTU), Luxembourg (LUX), Netherlands (NLD), New Zealand (NZL), Norway (NOR), Poland (POL), Portugal (PRT), Romania (ROU), the Russian Federation (RUS), Slovakia (SVK), Slovenia (SVN), Spain (ESP), Sweden (SWE), Switzerland (CHE), Turkey (TUR), Ukraine (UKR), the United Kingdom (GBR), and the United States of America (USA). The data on estimated CH₄ emissions for these counties were obtained from NIRs, covering the period from 1990 to 2021.

2.1. IPCC Methodology for Calculating CH₄ Emissions at the Country Level

The IPCC’s approach to estimating CH₄ emissions is structured to accommodate various levels of data availability and technical capability across countries. The IPCC methodology offers three tiers of increasing complexity for estimating CH₄ emissions: Tier 1—basic default method; Tier 2—intermediate method with country-specific data; and Tier 3—advanced method with detailed models and measurements (

Table 1. Key differences between IPCC tier methodologies (Tier 1, Tier 2, and Tier 3) used in Annex I countries.

Methodology	Complexity	Data Source	Accuracy	Example
Tier 1	Basic	Default IPCC factors	Low	General livestock population
Tier 2	Intermediate	Country-specific factors	Medium	Breed-specific livestock data
Tier 3	Advanced	Direct measurements/models	High	Real-time methane monitoring at facilities

).

Tier 1 uses global default emission factors provided by the IPCC and basic national activity data, such as the amount of livestock, the area of rice paddies, or the quantity of waste managed. These emission factors are generic and do not account for specific national conditions. Tier 2 uses country-specific emission factors that better reflect local conditions, such as fuel types, particular breeds of livestock, the types of rice cultivation, or waste management practices. This tier provides more accurate estimates than Tier 1 by tailoring factors to national circumstances. Tier 3 involves using sophisticated models, direct measurements, or continuous emissions monitoring at specific sites or facilities. This approach accounts for real-time variability and provides the most accurate and site-specific estimates. Developing country-specific emission factors is therefore considered the best practice [10]. Consequently, all the Annex I countries in their inventories primarily used the Tier 2 methodology, combining country-specific data with the default IPCC factors for emission sources that contribute less significantly to total emissions in kilotons (kt) of CH₄. The emissions across all sectors are calculated using the general equation CH₄ Emissions = Activity Data (AD) × Emission Factor (EF). In the Energy sector, AD quantities of oil, gas, or coal are produced, processed, or consumed, and EF emissions are from venting, flaring, leaks, and other fugitive processes. In enteric fermentation, AD is livestock population, and EF depends on animal type and feed quality. AD includes the volatile solids excreted in manure management, and EF is adjusted for storage types and climate. CH₄ in landfill is calculated using AD as the amount of municipal solid waste, and EF accounts for organic content and methane recovery. AD is organic wastewater generation, and EF varies with treatment system type. In IPPU, AD is the production volume of chemicals or metals, and EF is the process-specific emission for each industrial activity. LULUCF uses AD as an area of wetlands, peatlands, or other affected land, and EF as the land-type-specific CH₄ release rates. This generalized equation allows for the systematic estimation of CH₄ emissions using accurate, sector-specific data combined with detailed emission factors. After calculating CH₄ emissions for each source, the emissions from all the sources within a sector were summed.

2.2. Analysis of CH₄ Levels Using Sentinel-5P Data

The data from Sentinel-5P were used for analyses. TROPOMI provides global coverage with a 7 km × 7 km spatial resolution, enabling detailed observations of CH₄ concentrations. The instrument operates in the shortwave infrared (SWIR) spectral range, specifically between 2305 and 2385 nm, which is optimal for detecting methane. TROPOMI achieves a signal-to-noise ratio (SNR) of approximately 1000 over bright surfaces in this spectral range, ensuring high-quality data for atmospheric monitoring [28]. The column-averaged dry air mixing ratio of CH₄ (in ppb—parts per billion) data from 8 February 2019 to 31 August 2024 was used. Sentinel-5P imagery from the TROPOMI sensor, processed as a Level 3 (L3) product, was utilized [17]. Datasets were downloaded from the image collection “COPERNICUS/S5P/OFFL/L3_CH4” on Google Earth Engine as georeferenced raster files (in Geographic Tagged Image File Format, in short GeoTIFF), with a spatial resolution of 7 × 7 km². The mean values of the column-averaged dry air mixing ratio of CH₄ for the years 2019, 2020, and 2021 and 3-month periods from 8 February 2019 to 31 August 2024 were analyzed. GeoTIFF raster files containing averaged values of the air–methane mixing coefficient were used for subsequent processing. The mean CH₄ content for each study period was calculated for specific countries using the zonal statistics tool available in QGIS 3.34 software [29]. These mean values were utilized to assess inter-annual changes. Furthermore, spatial-temporal variability within the countries was evaluated.

2.3. Statistical Analysis

The pyMannKendall Python package was utilized to test trends in CH₄ emissions for each county during the studied period [30] (

Table 2. Methods used in analysis and their purposes, inputs, and outputs.

Method	Purpose	Input	Output
Mann–Kendall Trend Test	Detect monotonic trends in time series data	Time series of CH4 emissions (per country)	Increasing, decreasing, or stable trend
Sen’s Slope Estimation	Quantify the rate of change in time series data	Time series of CH4 emissions	Rate of change (e.g., ppb/year)
PCA	Reduce the dimensionality of complex datasets	CH4 emissions by sector and country	Principal components explaining variance in data

). The Mann–Kendall trend test is a non-parametric statistical test designed to detect monotonic trends in time series data, i.e., consistently increasing or decreasing trends [31,32]. Being non-parametric, it does not require the data to adhere to a specific distribution, such as normality, making it versatile for various data types. It includes Sen’s slope estimation [33], making it a versatile resource for analyzing trends in time series data across multiple disciplines. The principal component analysis (PCA) technique visualizes data, focusing on sectors and specific countries [34]. This type of analysis was used to reduce the dimensionality of complex datasets, like the CH₄ emission trends for each studied country, across various sectors, enabling a more straightforward interpretation and a better insight into underlying patterns in the data. Bartlett’s test confirmed that the correlation matrix was significantly different from an identity matrix, supporting the presence of relationships among variables. According to Kaiser’s criterion, only components with eigenvalues > 1 are considered significant, and those were considered.

3. Results

3.1. Results from IPCC Methodology and Sentinel Measurements

Figure 2 visualizes the trends in anthropogenic CH₄ emissions across all the Annex I countries and sectors where CH₄ is emitted and their total emissions over a time span from 1990 to 2021. The data were analyzed using the Mann–Kendall trend test, and Sen’s slope values are provided to show the rate of change in emissions.

The dotted line representing total CH₄ emissions shows a downward trend from approximately 107 Mt in 1990 to 78 Mt in 2021 (Figure 2, blue). The CH₄ emissions from Energy begin at 45 Mt and decrease to 27 Mt by 2021 (Figure 2, orange), reflecting substantial reductions likely due to advancements in energy efficiency and CH₄ management. The CH₄ total calculated from Agriculture decreases more gradually from 36 Mt to 30 Mt (Figure 2, grey), indicating ongoing emission, but at a reduced rate. The emission total from Waste starts at 22 Mt, declining to 16 Mt (Figure 2, yellow), which may be attributed to improved waste management practices and technologies that reduce CH₄ emissions. The CH₄ total from LULUCF slightly fluctuates, but generally increases from 4 Mt to 5 Mt (Figure 2, navy), suggesting minimal impact or varied effects of land use practices on CH₄ emissions. The emissions from IPPU show a minimal rise (Figure 2, green), possibly reflecting increases in specific industrial activities that emit CH₄. The general decrease in total CH₄ emissions over 31 years highlights significant progress in emission reduction, particularly in the Energy and Waste sectors. The Energy sector exhibits the most substantial decline, likely driven by tighter regulations, improved leak detection and repair practices, and the shift towards less methane-intensive energy sources. The Agriculture sector displays a slower rate of decline, which might be challenging due to the nature of agricultural practices that produce CH₄, such as enteric fermentation in livestock. The CH₄ emission reduction in the Waste sector suggests effective improvements in organic waste treatment and landfill gas capture. The IPPU sector total slightly increases, perhaps due to industry growth using methane-intensive processes. The LULUCF sector total remains relatively stable with a slight increase, which could indicate complex interactions between land management practices and CH₄ emissions.

Table 3. Numerical values indicating Sen slopes, which represent the rate of change in methane emissions (kt of CH₄) per year (or 3 months for the last column) for each country. The arrows next to each value indicate the trend direction. ↑ (up arrow) indicates a statistically significant increasing trend in CH₄ emissions; ↓ (down arrow) indicates a statistically significant decreasing trend in CH₄ emissions; and → (right arrow) indicates no significant trend, or a stable situation.

	Agriculture		Energy		IPPU		LULUCF		Waste		Total		Sentinel
	1990–2021												2019–2024
AUS	−19.103	↓	2.183	→	−0.027	↓	−2.331	→	−13.432	↓	−33.953	↓	2.450	↑
AUT	−0.947	↓	−0.475	↓	0.017	↑	0.000	→	−4.133	↓	−5.478	↓	2.441	↑
BEL	−1.188	↓	−0.271	↓	0.011	→	0.000	↓	−4.824	↓	−6.276	↓	2.487	↑
BGR	−1.568	↓	−1.113	↓	−0.067	↓	0.004	→	−2.029	↓	−4.819	↓	2.725	↑
BLR	−0.029	→	−0.083	→	0.073	↑	0.001	→	2.746	↑	2.916	↑	2.923	↑
CAN	0.203	→	13.940	↑	0.024	↑	−0.408	↓	1.807	↑	9.945	→	2.477	↑
CHE	−0.372	↓	−0.585	↓	0.003	↑	−0.006	↓	−0.503	↓	−1.443	↓	2.261	↑
CYP	−0.032	→	0.005	↑	0.000	→	0.000	→	0.242	↑	0.219	↑	1.808	↑
CZE	−2.623	↓	−11.718	↓	0.022	↑	−0.038	↓	2.740	↑	−11.269	↓	2.688	↑
DEU	−6.909	↓	−44.614	↓	0.140	↑	0.354	↑	−51.445	↓	−102.638	↓	2.458	↑
DNM	−0.200	↓	−0.790	↓	0.000	→	−0.012	↓	−0.875	↓	−1.675	↓	2.202	↑
ESP	−0.274	→	−1.190	↓	0.021	↑	−0.171	↓	1.846	↑	−1.797	→	2.215	↑
EST	0.011	→	−0.056	↓	0.000	→	0.002	↑	−0.249	↓	−0.408	↓	2.809	↑
FIN	−0.120	↓	−0.139	↓	−0.005	↓	−1.144	↓	−4.491	↓	−5.972	↓	2.770	↑
FRA	−5.360	↓	−14.095	↓	−0.241	↓	−0.588	↓	−1.092	→	−26.382	↓	2.461	↑
GBR	−6.455	↓	−37.574	↓	−0.282	↓	0.190	↑	−84.054	↓	−133.026	↓	2.293	↑
GRC	−0.651	↓	−0.508	→	−0.001	↓	−0.033	→	−0.551	↓	−2.223	↓	1.963	↑
HRV	−0.667	↓	−0.283	↓	−0.011	↓	−0.004	→	1.200	↑	0.283	→	2.725	↑
HUN	−1.125	↓	−2.435	↓	0.036	↑	−0.008	↓	−0.552	↓	−4.132	↓	2.688	↑
IRL	1.046	→	−0.302	↓	0.000	→	0.188	↑	−1.279	↓	−0.241	→	2.266	↑
ISL	−0.057	↓	−0.003	↓	0.002	↑	−0.137	↓	−0.034	→	−0.219	↓	1.521	↑
ITA	−4.459	↓	−5.841	↓	−0.099	↓	−0.493	↓	−3.308	→	−13.856	↓	2.286	↑
JPN	−5.394	↓	−3.824	↓	−0.022	↓	−0.045	↓	−14.462	↓	−24.445	↓	3.093	↑
KAZ	14.086	↑	−38.788	↓	−0.007	↓	−0.017	→	3.101	↑	−25.887	↓	2.138	↑
LTU	−1.300	↓	0.115	↑	−0.003	↓	−0.005	↓	−1.023	↓	−2.431	↓	2.398	↑
LUX	0.010	→	0.006	→	0.000	→	0.000	→	−0.034	↓	−0.014	↓	2.590	↑
LVA	−0.121	→	−0.369	↓	0.000	↓	0.240	↑	−0.241	↓	−0.492	↓	2.928	↑
MCO	0.000	→	−0.001	↓	0.000	→	0.000	→	0.001	↑	−0.001	↓	2.363	↑
MLT	−0.039	↓	−0.004	↓	0.000	→	0.000	→	0.116	↑	0.080	↑	2.128	↑
NLD	−3.469	↓	−0.774	↓	0.036	↑	−0.077	↓	−17.539	↓	−20.963	↓	2.501	↑
NOR	−0.233	↓	−0.703	↓	−0.009	↓	0.027	↑	−1.557	↓	−2.480	↓	2.575	↑
NZL	2.417	↑	−0.825	↓	0.054	→	0.012	→	−1.719	↓	−0.207	→	2.866	↑
POL	−4.284	↓	−1.499	→	0.023	↑	−0.049	↓	−19.456	↓	−25.830	↓	2.636	↑
PRT	−0.305	↓	−0.408	↓	0.004	→	−0.027	→	−0.975	→	−1.431	↓	2.579	↑
ROU	−6.983	↓	−23.567	↓	−0.062	↓	0.003	↑	1.231	↑	−30.613	↓	2.677	↑
RUS	−57.804	↓	−70.571	↓	0.507	↑	7.208	↑	64.034	↑	−55.263	↓	2.725	↑
SVK	−1.518	↓	−3.071	↓	0.002	↑	0.019	↑	0.703	↑	−3.873	↓	2.433	↑
SVN	−0.025	→	−0.264	↓	−0.007	↓	−0.001	↓	−0.469	↓	−0.800	↓	1.935	↑
SWE	−0.688	↓	−0.317	↓	−0.002	→	−0.101	↓	−4.298	↓	−5.489	↓	2.334	↑
TUR	15.124	↑	6.175	↑	0.012	↑	−0.030	→	7.991	↑	29.251	↑	2.450	↑
UKR	−35.902	↓	−105.724	↓	1.752	↑	−0.025	→	1.328	↑	−140.553	↓	2.450	↑
USA	31.886	↑	−84.113	↓	−0.093	→	9.546	↑	−91.254	↓	−136.161	↓	2.441	↑

provides data on the CH₄ emission trends across various sectors for the analyzed countries. The sectors analyzed are Agriculture, Energy, IPPU and LULUCF, Waste, and there is an overall category representing total CH₄ emissions calculated over 1990–2021 using the IPCC methodology. The values are Sen slopes derived from Mann–Kendall analysis, commonly used to detect trends in environmental data. The Sen slopes represent the rate of change in CH₄ emissions (kt of CH₄) per year. Turkey (TUR) shows significant increases across the Agriculture, Energy, and Waste sectors, leading to a substantial overall increase (+29 kt). Kazakhstan (KAZ) shows a notable increase in the Agriculture sector (+14 kt) despite overall decreases in the other sectors. In the United States (USA), while some sectors like Energy show a reduction, Agriculture and LULUCF indicate increasing trends, affecting the overall emissions. Russia (RUS), despite increases in the IPPU and Waste sectors, experiences a substantial overall decrease in total CH₄ emissions (−55 kt) due to significant reductions in Agriculture and Energy. The United Kingdom (GBR) shows a major decline in Waste (−84 kt) and overall CH₄ emissions (−133 kt). In Germany (DEU), the CH₄ emissions decreased in both the Energy (−45 kt) and Waste (−51 kt) sectors, and thus contribute to an overall negative trend. The Energy sector of Canada (CAN) shows a significant increase (+14 kt), but the overall emissions are relatively stable (close to zero change). In Australia (AUS), a notable decrease in Agriculture (−19 kt) is balanced by increases in the other sectors, leading to a moderate overall decline. This table highlights the variability in CH₄ emission trends across the different sectors and countries. Countries like Turkey and Belarus show increasing trends in specific sectors, leading to overall rises in CH₄ emissions. In contrast, most countries exhibit significant reductions, primarily due to decreased Energy, Agriculture, and Waste sector emissions. The last column of Table 3 presents the results of the M-K test, i.e., Sen slopes for the countries, analyzing the CH₄ emissions trends from Sentinel observations over multiple 3-month periods from 2019 to 2024. Generally, all the countries display positive slopes, indicating statistically significant upward trends in CH₄ emissions.

The biplot presented in Figure 3 depicts the analysis of trends in CH₄ emissions. The data used in PCA analysis are Sen’s slope coefficients derived from the Mann–Kendall test (Table 3), with CH₄ emissions calculated according to the IPCC methodology. The p-value of Bartlett’s test of sphericity was below 0.001. This indicates that the correlation matrix is significantly different from the identity matrix. This suggests the dataset is suitable for PCA, as the variables exhibit sufficient inter-correlation. Only two first principal components, PC1 and PC2, meet the Kaiser’s criterion. The horizontal axis (PC1) accounts for 45.0% of the variance in the dataset. The vertical axis (PC2) displays a smaller percentage of variance (32.9%) and can also be inferred to be significant for separation along the y-axis. Together, PC1 and PC2 account for 77.9% of the total variance, which is sufficient to describe most of the structure in the data. The biplot shows vectors corresponding to the Agriculture, Energy, Waste, LULUCF, and IPPU sectors. Each vector’s direction and length indicate how much each sector influences the positioning of the countries along the principal components. Each point’s position is determined based on their respective CH₄ emission trends in the sectors represented by the vectors. The clustering or spreading of countries around specific vectors suggests commonalities or differences in emission trends within these sectors. A country’s proximity to a particular vector (sector) indicates a more substantial influence or a more significant trend in CH₄ emissions in that sector for the country. For instance, if a country is close to the LULUCF vector, like DEU (Germany), it implies a significant positive trend in CH₄ emissions related to that country’s LULUCF sector. Conversely, a country near the center or equidistant from several vectors might have mixed influences from multiple sectors. Countries like TUR (Turkey) and KAZ (Kazakhstan) are closer to the center, suggesting a balanced influence from several sectors. RUS and USA appear farther along the PC1 axis, indicating distinct trends that may be driven by multiple sectors, with a strong component aligned with their positions relative to sector vectors. The USA has mixed trends with increases in Agriculture and LULUCF and decreases in Energy and Waste, aligning with their positioning near respective vectors in the PCA plot. Contrastingly, countries like Germany (DEU) and the UK (GBR) show predominantly negative slopes, consistent with their positions away from the Energy vector, indicating major reductions in CH₄ emissions in sectors such as Energy and Waste. UKR (Ukraine) and RUS (Russia) show similar trends in CH₄ emissions. They are both characterized by significant positive trends in the IPPU sector. The biplot provides a visual summary of how CH₄ emissions trends vary across the countries and the sectors. For the IPCC estimates, uncertainties arise from two primary sources: the activity data (with an uncertainty of approximately 5% of the value) and the emission factors (ranging from 5% to 20% of the value). These uncertainties are typically quantified using error propagation formulas or Monte Carlo simulations [35]. In contrast, the satellite-derived data’s standard deviation (SD) is approximately 15 ppm, corresponding to 1% of the measured value. Using Monte Carlo simulation, we performed sensitivity analysis to evaluate the impact of ±20% uncertainty on the data. The results revealed that the observed trends remained consistent, indicating that the PCA structure is robust to such uncertainties. This is because the PCA primarily depends on the directionality of trends (whether increasing or decreasing) rather than the absolute magnitude of variability.

The map presented in Figure 4 displays the mean CH₄ concentrations for the Annex I countries, as measured in 2021 based on Sentinel satellite data color-coded by concentration ranges. The concentrations are given in parts per billion (ppb) and divided into seven categories, each represented by a different color on the map. The color coding corresponds to the CH₄ concentration ranges. Each was assigned a specific color from dark gray to white to dark red. The map shows a gradient in CH₄ concentrations, with the darker red colors typically shown in the more industrially active or populated regions or regions with significant agricultural or natural CH₄ sources, like wetlands and oil fields. This visualization is instrumental in comprehending the global CH₄ emission patterns and is critical for climate change research and mitigation efforts.

3.2. Comparison of IPCC Results with Sentinel Observations

For all the studied countries, in 2019, the column-averaged dry air mixing ratio of CH₄ in ppb (parts per billion) was 1831 (from 1774 to 1888), and then 1839 (from 1783 to 1894) ppb in 2020 and 1848 (from 1794 to 1902) ppb in 2021. The total area of all the Annex I countries is 37,916,967 km². We converted the column-averaged dry air mixing ratio of CH₄ from parts per billion (ppb) to grams of CH₄ per square meter (g CH₄ m⁻²), assuming an atmospheric pressure of 1000 hPa and an average molar mass of dry air of 28.96 g mol⁻¹ and taking into consideration the column-averaged dry air mixing ratio of CH₄ (in ppb—parts per billion). We approximated the mass of CH₄ over the studied surface. For 1868 ppb, we obtained around 10 g CH₄ m⁻¹. Thus, for the total area of the Annex I countries, the CH₄ total in the atmosphere is almost 400 Mt, which is nearly four times higher than the annual emission of CH₄ in these countries (Figure 2).

Figure 5 shows the 3-month growth rate of mean CH₄ concentrations for the studied countries, as measured from 2019 to 2024 based on the Sentinel satellite data. The positive values represent the periods of increased CH₄ concentrations, while the negative values indicate a decline. There are notable fluctuations in the growth rate over the years. For example, a significant increase is observed in late 2020, 2021, 2023, and early 2024, while a major drop occurs in the first half of 2022. Overall, the data highlight the variability in CH₄ growth rate over the analyzed time frame. There is no significant trend in the growth rate.

4. Discussion

This study demonstrates a comprehensive comparison of CH₄ emission estimates derived from the IPCC methodology and the Sentinel-5P satellite data. The results indicate that the observed trends in methane emissions across the Annex I countries are inconsistent between the two approaches. For most countries, the IPCC-derived data indicate a decreasing trend in methane emissions across the study period (1990–2021). This decline is primarily attributed to reductions in emissions from sectors like Agriculture, Energy, and Waste, as seen in countries such as Germany, the United Kingdom, and Australia. Conversely, the Sentinel-5P data (2019–2024) consistently show an increasing trend in CH₄ concentrations across all the analyzed countries. In contrast, the Sentinel-5P data suggest these reductions are insufficient to offset the other CH₄ sources or earlier emissions, as the total CH₄ concentrations show an increasing trend.

CH₄ emitted into the atmosphere primarily originates from natural sources (e.g., wetlands, termites, and oceans) and anthropogenic activities (e.g., agriculture, energy production, and waste). The removal of CH₄ predominantly occurs through oxidation by hydroxyl radicals (OH) in the atmosphere, breaking down CH₄ into CO₂ and water [36]. The annual CH₄ emissions from the Annex I countries total 100 Mt, with the stock of CH₄ in the atmosphere over the area of these countries nearing 400 Mt. This difference underscores methane’s relatively long atmospheric lifetime (~12 years) and the balance between emissions and the removal processes. The increasing trend observed in the Sentinel-5P data may reflect the cumulative effect of ongoing emissions and methane’s persistence in the atmosphere, highlighting the urgency of integrating both these methodologies to quantify and manage methane emissions better.

Based on the satellite measurements, the observation is that the global trend in CH₄ concentration in the atmosphere shows an increase. As calculated using the IPCC methodology, CH₄ emissions from the Annex I countries show a decreasing trend, but CH₄ is still constantly added to the atmosphere. This is attributed to several factors. The Annex I countries typically have more robust data collection and reporting mechanisms, which could lead to more accurate and possibly lower reported emissions due to identifying and mitigating significant CH₄ sources. Annex I countries, including the most developed nations, often have stricter environmental regulations and more advanced technologies to reduce CH₄ emissions [37]. Measures such as improved waste management practices, advanced agricultural techniques, and more stringent regulations in the oil and gas sectors contribute to a decreasing trend in these regions. Many developed countries have comprehensive climate policies that include specific targets for CH₄ emission reduction, supported by legislation and financial incentives for emission control [38]. However, the increasing trends observed in the satellite data suggest that these reductions may be insufficient to counteract emissions from other sources or earlier cumulative emissions. Developing countries may experience increased CH₄ emissions due to rapid industrialization, agricultural expansion, and less stringent environmental controls [39]. As these economies grow, their energy consumption, waste generation, and farming activities increase, potentially leading to higher CH₄ emissions [40]. The policies in developing countries may not be as stringent or well enforced, which can result in the less effective control of CH₄ emissions. The limitation of this analysis is that it did not include large developing countries, notable for their rising CH₄ emissions. Countries such as China, India, and Brazil, among others, play a critical role in global CH₄ emissions due to their vast agricultural sectors, rapid industrialization, and extensive fossil fuel exploitation [41]. As the world’s largest coal producer and consumer, China emits significant quantities of CH₄ from its coal mining sectors. This country has also seen increased emissions from expanding livestock industries and burgeoning urban waste management challenges. India’s CH₄ emissions are primarily driven by agriculture, mainly from rice paddies and livestock, which are integral to its agricultural economy. With increasing food demand, these emissions will rise unless significant mitigation strategies are adopted [4]. In Brazil, CH₄ emissions are extensively linked to the livestock sector, particularly cattle ranching in the Amazon region. Deforestation for agricultural expansion further contributes to CH₄ emissions through biomass burning and decay [42].

Including these key players in analysis can lead to a complete understanding of global CH₄ dynamics. While the Annex I countries have shown decreasing trends due to stringent regulations and technological advances, the increasing trends in large developing countries can offset these reductions. Therefore, the global impact of CH₄ emissions requires a comprehensive approach that includes all the major emitters to effectively address and mitigate the contributions to global warming. This contrast between the reported reductions in the Annex I countries and the increasing global CH₄ concentrations highlights the need to integrate satellite observations into inventory-based approaches to improve the accuracy and comprehensiveness of global methane emission estimates.

A potential limitation of this study lies in the contribution of natural CH₄ sources [43]. Wetlands are the largest natural source of CH₄ due to the anaerobic decomposition of organic materials in their water-saturated soils, particularly in tropical and boreal regions [44]. Accelerated by rising global temperatures, permafrost-thawing releases CH₄ stored as organic carbon, especially in the Arctic [45]. Methane hydrates in sediments, when disturbed, can also release CH₄, as do natural oceanic seeps. Termites, especially in tropical regions, emit CH₄ during digestion due to their gut fauna [46]. Some other sources include volcanic and geothermal systems, which release CH₄ locally [47], and wildfires, which emit methane through organic material combustion [48]. These natural sources vary across regions and time, influenced by climatic, ecological, and geological factors. Regions with significant natural sources may show differing total CH₄ emission trends independent of human mitigation efforts. The discrepancies observed between the IPCC estimates and the Sentinel-5P data may partly stem from the influence of natural CH₄ emissions. Future studies should aim to improve the attribution of methane sources, potentially integrating additional datasets or advanced modeling techniques to better account for the variability and impact of natural emissions.

The movement of CH₄ masses across different regions adds another layer of complexity to understanding and managing global CH₄ emissions. CH₄ released into the atmosphere can be transported over long distances by prevailing wind patterns [49]. This means that CH₄ emitted in one area can contribute to the atmospheric CH₄ levels in faraway regions. The movement of atmospheric CH₄ can vary seasonally, influenced by meteorological conditions and atmospheric circulation changes. CH₄ can be absorbed into the soil, and later released, influenced by soil moisture, temperature, and microbial activity [50]. In bodies of water, CH₄ produced from the bottom (e.g., from decaying organic matter) can diffuse through the water column to the atmosphere. The rate of this diffusion can be affected by water temperature, pressure, and turbulence. The movement of CH₄ makes it challenging to pinpoint the exact sources of emission, especially when measuring atmospheric concentrations. This can complicate efforts to attribute emissions to specific activities or regions. Since CH₄ mixes globally in the atmosphere, regional reductions in CH₄ emissions can have widespread benefits, reducing the atmospheric concentrations locally and globally. Understanding the movement of CH₄ is crucial for improving the accuracy of climate models that predict the future impacts of GHG emissions. These models must account for transport mechanisms to accurately simulate methane’s influence on global warming. The movement of CH₄ can also become a part of feedback loops. For example, CH₄ released from melting permafrost due to warming can travel and contribute to further warming, thereby accelerating the melting process. Since CH₄ can cross national boundaries, international cooperation is needed to monitor and reduce emissions. Policies and measures need to consider the transboundary nature of CH₄ to address its global impact effectively. Developing global strategies and sharing knowledge, technologies, and data across countries can help manage the movement of CH₄ more effectively. This includes improving monitoring networks and sharing the best practices for reducing emissions from critical sources.

5. Conclusions

This study comprehensively compares the CH₄ emission estimates derived from the IPCC methodology and the Sentinel-5P satellite data, focusing on the Annex I countries. The results reveal significant discrepancies between the two approaches. This contrast underscores the challenges in accurately quantifying methane emissions and highlights the complementary roles of inventory-based and satellite-derived approaches. Analysis demonstrates that methane continues accumulating in the atmosphere despite robust reductions in anthropogenic emissions from the Annex I countries. This study highlights the importance of integrating multiple methodologies for a more accurate and comprehensive understanding of methane emissions. The Sentinel-5P satellite data complement the IPCC estimates by offering high-resolution insights into atmospheric methane concentrations, particularly in regions with sparse or uncertain inventory data. Combining these approaches can enhance the accuracy of methane inventories, inform targeted mitigation strategies, and support more effective climate policies. Future work should aim to expand analysis to include developing countries with significant methane contributions, improve methane source attribution by integrating the satellite data with advanced models and additional datasets, address natural methane sources and feedback mechanisms in global methane dynamics, and strengthen international collaboration to monitor and mitigate methane emissions globally. By adopting a comprehensive approach that combines satellite observations and inventory-based estimates, this study contributes to a deeper understanding of methane emissions and their impact on global warming. Such integration is crucial for advancing climate mitigation efforts and achieving long-term sustainability goals.