Satellite-Based Seasonal Fingerprinting of Methane Emissions from Canadian Dairy Farms Using Sentinel-5P

Abstract

Methane (CH₄) emissions from dairy farming represent a substantial yet under-quantified share of agricultural greenhouse gas emissions. This study provides an in-depth, satellite-based fingerprinting analysis of methane emissions from Canada’s dairy sector, using Sentinel-5P/TROPOMI data. We utilized a robust quasi-experimental design, pairing 14 dairy-intensive zones with eight non-dairy reference regions, to analyze methane emissions from 2019 to 2024. A dynamic, region-specific baseline approach was implemented to remove temporal non-stationarity and isolate dairy-specific methane signals. Dairy regions exhibited consistently higher methane concentrations than reference areas, with an average methane anomaly of 17.4 ppb. However, this concentration gap between dairy and non-dairy regions notably narrowed by 57.23% (from 24.42 ppb in 2019 to 10.44 ppb in 2024), driven primarily by accelerated methane increases in non-dairy landscapes and a pronounced one-year contraction during 2022–2023 (−39.29%). Nationally, atmospheric methane levels rose by 3.83%, revealing significant spatial heterogeneity across provinces. Notably, an inverse relationship between the initial methane concentrations in 2019 and subsequent growth rates emerged, indicating spatial convergence. The seasonal analysis uncovered consistent spring minima and fall–winter maxima across regions, reflecting the combined effects of seasonal livestock management practices, atmospheric transport dynamics, and biogeochemical processes. The diminishing dairy methane anomaly suggests complex interplay of intensifying background methane emissions from climate-driven wetland fluxes, increasing fossil fuel extraction activities, and diffuse agricultural emissions. These findings underscore the emerging challenges in attributing sector-specific methane emissions accurately from satellite observations, highlighting both the capabilities and limitations of current satellite monitoring approaches.

1. Introduction

Atmospheric methane (CH₄) has emerged as a critical focus of climate change mitigation efforts, due to its potent warming potential, which is 28–34 times that of carbon dioxide over a 100-year timeframe [1]. Global atmospheric methane concentrations have risen from approximately 722 parts per billion (ppb) in pre-industrial times to over 1900 ppb recently, with accelerated growth since 2007 [2]. This trajectory significantly impacts the Paris Agreement climate targets, as methane contributes approximately 0.5 °C to observed global warming [3]. Agricultural activities represent a substantial and potentially manageable emission source, among various contributors [4]. The dairy sector contributes significantly to agricultural methane emissions via enteric fermentation and manure management [5].

In Canada, dairy production accounts for approximately 8% of agricultural greenhouse gas emissions, primarily methane [5]. With approximately 977,000 dairy cows across 10,095 farms, the Canadian dairy industry presents both challenges and opportunities for targeted emission reduction [6]. However, effective mitigation depends on the robust spatiotemporal characterization of methane emissions at regional scales. Such data have historically been limited by ground-based measurement constraints [7]. Traditional quantification approaches have relied on bottom-up inventory methods, applying emission factors to activity data, such as livestock populations and management practices [8,9]. While providing valuable baseline estimates, these methods often fail to capture complex spatial and temporal dynamics that are influenced by regional climate variations, management practices, and ecosystem interactions [10]. Significant discrepancies between inventory estimates and atmospheric observations exist, as documented by [11], who found that western Canadian energy operation methane emissions were nearly twice those reported in official inventories. Similar discrepancies may exist regarding agricultural emission estimates, highlighting the need for independent verification methods.

Previous studies have attempted to quantify agricultural methane emissions using satellite observations through inverse modeling [12,13], enhancement detection over known source regions [14], and their correlation with activity data [8]. However, these approaches have largely focused on continental or global scales, with limited application to regional agricultural systems like Canada’s dairy sector. Few studies have characterized the seasonal dynamics of agricultural methane emissions using satellite observations, with a critical gap related to emission processes’ sensitivity to seasonal environmental factors and management practices [15]. The Canadian dairy sector presents an interesting case study for satellite-based methane monitoring, due to several factors: (1) its geographical distribution, which spans diverse climatic zones, creating natural experimental conditions for examining the environmental modulation of emission patterns; (2) the adoption of relatively standardized management practices that are governed by supply management systems, potentially reducing the number of confounding influences observed in other countries [16]; and (3) its northern latitude, which provides unique conditions for examining seasonal emission patterns, due to its pronounced temperature variations, frozen soil periods, and seasonal changes in cattle housing and feeding regimes.

We have developed a multi-method approach to characterize the spatiotemporal distribution of methane concentrations in Canadian dairy regions and identify seasonal emission patterns. Our approach combines satellite remote sensing data with spatial clustering techniques, quasi-experimental design elements, and advanced time-series analysis to address attribution challenges. By comparing dairy regions with carefully selected non-dairy reference areas, we aim to isolate the dairy-specific methane signals, while controlling for regional and latitudinal background variations. This study addresses four primary research questions that are focused on: (1) the spatiotemporal dynamics of atmospheric methane concentrations across Canadian dairy regions (2019–2024); (2) a comparison of methane concentrations between dairy-intensive and non-dairy reference areas; (3) seasonal patterns characterizing methane emissions from Canadian dairy regions; and (4) the identification of consistent seasonal patterns with subtle regional variations that could support source attribution efforts when combined with other methane source types and inform targeted mitigation strategies. These insights will contribute to research utilizing advanced remote sensing technologies to improve the understanding of agricultural greenhouse gas emissions and support evidence-based climate policy development.

2. Materials and Methods

This study uses a multi-method approach to assess methane emissions from dairy farming in Canada, leveraging satellite remote sensing data, with a dynamic temporal baseline methodology (Figure 1).

Rather than employing a fixed baseline year, our approach recognizes the non-stationary nature of atmospheric methane concentrations and uses concurrent regional differentials and year-specific references to isolate dairy-specific signals. This dynamic baseline methodology was selected over traditional fixed baseline approaches because atmospheric methane concentrations exhibit significant inter-annual variability due to changing climatic conditions, evolving emission sources, and atmospheric transport patterns that would confound static reference periods [12]. The concurrent regional differential approach ensures that background atmospheric trends are consistently accounted across the entire study period, enabling robust attribution of dairy-specific signals, even under changing baseline conditions [14]. A quasi-experimental design was applied, with dairy regions as treatment areas and non-dairy regions as controls, facilitating a comparative analysis, while controlling for regional and latitudinal differences. This approach allows for a detailed understanding of methane emissions from dairy operations under varying environmental conditions.

2.1. Data Acquisition and Processing

2.1.1. Satellite Platform and Sensor Specification

The Sentinel-5 Precursor (Sentinel-5P) satellite, launched in October 2017, has revolutionized atmospheric methane monitoring through its TROPOspheric Monitoring Instrument (TROPOMI), providing unprecedented spatial and temporal resolution [14]. TROPOMI delivers daily global coverage at a spatial resolution of 5.5 × 7 km², enabling the detection of regional methane enhancements and their temporal evolution, which supports top-down verification of emission estimates and the identification of mitigation targets [14]. Operating in a near-polar, sun-synchronous orbit, with a 13:30 local solar time equatorial crossing, Sentinel-5P employs a nadir-viewing spectrometer, with a 2600 km swath width, to measure reflected solar radiation across ultraviolet (267–332 nm), ultraviolet–visible (305–499 nm), near-infrared (661–786 nm), and shortwave infrared (SWIR; 2300–2389 nm) spectral bands. The TROPOMI column averaging kernel demonstrates that more than 60% of the retrieved signal originates from the lowest 1 km of the atmosphere under clear-sky conditions, providing strong sensitivity to surface-level methane emissions. This sensitivity is further enhanced by the use of shortwave infrared (SWIR) measurements, which are essential for retrieving column-averaged dry air mole fractions of methane (XCH₄). The combination of kernel sensitivity and SWIR data makes TROPOMI particularly well-suited for detecting and characterizing localized methane emission sources [12].

2.1.2. Data Retrieval and Temporal Aggregation

Sentinel-5P Level 3 products were accessed via the Google Earth Engine (GEE) API. The dataset, comprising the XCH₄ column-averaged dry air mixing ratio, expressed in parts per billion (ppb), was retrieved for the period from 2019 to 2024, to investigate seasonal variabilities. The Sentinel-5P XCH₄ Level 3 products are pre-processed datasets that incorporate geolocation corrections, radiometric calibration, cloud masking, and bias corrections, which address albedo effects, aerosol interference, and systematic instrumental biases to ensure high data fidelity [17]. To balance the temporal resolution and data quality, the data were processed into weekly composites, minimizing the gaps due to cloud interference and instrumental errors. Spatial filtering was applied to isolate methane concentrations in dairy-intensive agricultural zones for comparative analysis with non-dairy regions. Quality control procedures excluded data points with cloud fractions greater than 20% and data affected by instrumental anomalies. This workflow ensured that we processed the methane data at both high temporal and spatial resolution, enabling comprehensive analyses of seasonal and regional methane emissions.

2.2. Study Area Definition and Sampling Strategy

2.2.1. Spatial Domain Selection for Methane Emission Analysis

Our analysis focused primarily on southern Canada due to a confluence of demographic, agricultural, and technical factors specific to this area that align with the study’s objective of characterizing dairy-specific methane emissions. This region encompasses the majority of Canada’s population and agricultural infrastructure, with approximately 66% of Canadians living within 100 km of the U.S. border [18], and extending this range to 200 km captures an estimated 85% to 90% of the national population, thereby reflecting the concentration of urban centers and economic activity in this region [19]. Whereas northern Canada, despite covering nearly 90% of the country’s land area, hosts less than 15% of the population and contributes marginally to national agricultural production. Dairy farming, the primary focus of our methane emission assessment, is overwhelmingly concentrated in the southern portions of provinces [20] such as Ontario, Quebec, and British Columbia. These regions offer favorable climatic conditions, robust infrastructure, and proximity to markets, which is a key enabler of intensive dairy operations. Our geospatial analysis of the geotagged dairy farms and processing facilities (Table S1) across ten provinces also confirms a pronounced southern clustering.

Apart from the geographic distribution of agriculture and the population, technological constraints associated with satellite-based methane detection further support the southern focus. The Sentinel-5P TROPOMI, with its coarse spatial resolution, is designed to detect concentrated emission sources but lacks the resolution needed to distinguish smaller or diffuse sources [21], typical of northern Canada’s low-density agricultural regions. During winter, extended periods of darkness in arctic regions [22], low solar elevation angles that increase the atmospheric path length and reduce measurement precision [23], and persistent cloud cover in northern latitudes [24], severely degrade the data quality and create multi-month gaps that block the generation of consistent weekly composites required for our time series framework. Additionally, methane emissions in northern Canada are largely shaped by arctic air masses and low-density anthropogenic activities, complicating source attribution [25] using the current satellite platforms. While such dynamics warrant dedicated investigation using future high-resolution instruments and localized ground-based measurements, our study prioritizes areas where agricultural emissions are both significant and policy relevant.

2.2.2. Dairy Region Delineation and Reference Region Selection

To identify concentrated dairy farming areas, we applied the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) spatial clustering method [26,27] to a geospatial database, comprising 1180 geolocated dairy farms and associated processing facilities across ten Canadian provinces (Table S1). These locations were retrieved through systematic geocoding using the Google Maps Application Programming Interface (API), enabling spatially explicit analysis at a national scale. To delineate regions of concentrated dairy activity, we employed bounding box geometries that were approximately 2° × 2° in size, following the use of spatial clustering to maintain methodological consistency. Fourteen rectangular bounding boxes were defined to encompass high-density dairy farming zones (Table S2). These regions recognized by Dairy Farmers of Canada (2023), represent around 82% of Canada’s dairy output [28], making them ideal for analyzing dairy-related methane fluxes. To support the comparative analysis, we identified eight reference regions (Table S2) situated at similar longitudes but at higher latitudes relative to the dairy zones, with minimal livestock activity. To minimize the potential confounding factors, reference regions were selected to avoid major anthropogenic and natural methane sources, including urban areas, wetlands, and industrial zones. This selection was guided by cross-referencing the ESRI Sentinel-2 Land Use/Land Cover (S2 LULC) dataset, available at https://livingatlas.arcgis.com/landcover/ (accessed on 9 April 2025), enabling the identification of areas predominantly characterized by natural vegetation or barren land. This ensured a clean atmospheric background for the robust attribution of observed methane enhancements to dairy-related activities. This spatial pairing strategy adheres to Intergovernmental Panel on Climate Change [29] guidelines for atmospheric methane monitoring, ensuring the maintenance of comparable climatic regimes, while minimizing the influence of emissions. The overall methodological framework draws on established approaches to satellite-based greenhouse gas monitoring and addresses spatial heterogeneity issues [30].

2.2.3. Spatial Clustering Using DBSCAN Algorithm

To refine the initially defined bounding boxes based on actual farm distributions, we applied the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, a widely used unsupervised machine learning method for spatial pattern recognition [31]. This technique enabled the identification of natural clusters of dairy farms based on their geographic proximity, thereby improving the representativeness of the defined dairy regions. The epsilon (eps) value was set to 0.1, which corresponds approximately to a 10 km radius at mid-latitudes in Canada. This value defines the maximum distance between two farms for them to be considered part of the same cluster. The minimum sample parameter was set to 2, indicating the minimum number of dairy farms required to form a dense cluster. This clustering approach enabled the exclusion of isolated farms and improved the spatial precision of methane emission analyses, which has been successfully used for identifying spatial patterns in geocoded datasets [32,33,34].

2.2.4. Regional Boundary Refinement Using Adaptive Bounding Boxes

Following the identification of dairy farm clusters, we implemented an adaptive bounding box refinement technique to define the spatial extent more precisely around each cluster. This method calculated the geographic boundaries by identifying the minimum and maximum latitudinal and longitudinal coordinates for each cluster, then extended these boundaries with a fixed spatial buffer. Following cluster identification, the adaptive bounding boxes were defined around each cluster using minimum/maximum coordinates with a standardized buffer to ensure complete coverage, while maintaining regional comparability [24,30].

2.3. Spatial Analytical Implementation

2.3.1. Zonal Statistics Computation

We computed the zonal statistics for each defined region by aggregating pixel-level methane concentrations using Google Earth Engine’s reducer functions, where each pixel value represents the column-averaged methane concentration that serves as a proxy for the surface emission activity within that spatial domain [12,14]. The mean and standard deviation of the methane values within each region were calculated. To balance computational efficiency and spatial resolution, we selected a scale parameter of 1000 m, which aligns with best practices on large-scale satellite-based atmospheric trace gas retrieval [35]. This approach enables the region-specific aggregation of satellite observations, while maintaining the spatial fidelity needed for subnational emissions assessments. Spatially averaged concentrations were computed for each defined region, supporting robust statistical comparisons between dairy and non-dairy zones.

2.3.2. Spectral Reflectance Modelling and Correction of Atmospheric Methane

We generated continuous spectral surfaces of the atmospheric methane concentrations by compositing the satellite observations over time, using the arithmetic means of individual image acquisitions. To ensure consistency across the spatial analyses, we exported the resulting mean image with a standardized spatial resolution of 5 km and a coordinate reference system (CRS) of EPSG:4326. The export was performed using the Google Earth Engine export function. A two-dimensional Gaussian filter (σ = 1.0) was applied to suppress high-frequency noise, while preserving the spatial gradients [36].

2.3.3. National and Provincial Scale Data Aggregation of Methane Metrics

To assess methane dynamics at broader administrative levels, the regional estimates were aggregated to provincial and national scales using area-weighted averaging. This approach accounts for spatial heterogeneity in region sizes, thereby ensuring accurate representation of methane concentrations across varying geographical areas [12]. At the provincial level, the average methane concentration for each province was computed. Subsequently, the national average methane concentration was calculated by aggregating the provincial estimates using a similar area-weighted approach. This hierarchical aggregation framework facilitates scalable methane monitoring, while preserving spatial fidelity at multiple administrative levels.

2.4. Temporal Dynamics and Time-Series Analysis

2.4.1. Kalman Filtering for Time-Series Smoothing

Time-series smoothing was implemented using a Kalman filter to reduce noise, while preserving temporal trends [37]. The filter parameters were optimized through a grid search (Q = 0.01, R = 0.1) to minimize the root mean square error (RMSE) between the raw and filtered values. This approach ensures the optimal selection of parameters for filtering atmospheric methane data with minimal errors [38]. The Kalman filter is particularly effective for linear systems with Gaussian noise, as it reduces high-frequency fluctuations, while preserving the underlying trends and seasonal patterns, which are essential for accurate time-series environmental monitoring [39].

2.4.2. Non-Parametric and Parametric Trend Estimation

We estimated the linear trends in atmospheric methane concentrations to quantify the annual rate of change across the study regions. The linear trends were estimated as annualized rates of change between the initial and final concentration. To assess the statistical significance, we applied the non-parametric Mann–Kendall trend test, which is robust when dealing with missing data and non-normal distributions typical of satellite-derived time-series data [40].

2.4.3. Statistical Analysis of Methane Anomaly Time Series

To quantify the convergence between dairy and non-dairy regions, we analyzed the temporal trend in the weekly methane concentration differentials using linear regression and Mann–Kendall trend tests. The differential was calculated as the difference between the mean dairy and reference region methane concentrations at each time step. Confidence intervals (95%) for the slope were computed, and the total change over the study period was calculated in both absolute (ppb) and percentage terms, providing robust statistical evidence of the observed convergence between dairy and non-dairy methane concentrations.

2.4.4. Inter-Regional Convergence Analysis

To quantify the spatial convergence in methane concentrations over time, a modified sigma convergence framework was employed, following the approach by [41]. Specifically, the coefficient of variation (CV) was used as a normalized measure of dispersion across the regions at each time point. The temporal trends in the regional convergence were evaluated by computing the negative time derivative of the CV. A positive convergence rate indicates decreasing spatial disparities in the methane concentrations, signifying a trend toward inter-regional homogenization. This dynamic metric enables systematic assessment of spatial equity in emission patterns over time.

2.4.5. Methane Anomaly Detection and Quantification

We quantified the dairy-specific methane anomalies by calculating the difference between the average concentrations in dairy regions and their corresponding non-dairy reference regions for a weekly time step. In this study, the dairy specific methane anomaly ($∆{CH}_4$) is defined as the excess column-averaged methane concentration in the dairy region relative to the reference non-dairy region. The anomaly was calculated at time $t$ (Equation (1)):

$$∆{CH}_4(t)={\displaystyle \frac{1}{n_d}} \sum _{i=1}^{n_d}{CH}_{4_{d,i,t}}- {\displaystyle \frac{1}{n_r}} \sum _{j=1}^{n_r}{CH}_{4_{r,j,t}}$$

(1)

where

• ${CH}_{4_{d,i,t}}$ represents the methane concentration in dairy region $i$ at time $t$;
• ${CH}_{4_{r,j,t}}$ denotes the methane concentration in reference region $j$ at the same time; and
• $n_d$ and $n_r$ are the total number of dairy and reference regions, respectively.

To identify periods of anomalously elevated emissions, we defined a significant methane anomaly as any weekly value of $∆{CH}_4$ exceeding two standard deviations above the mean of the full anomaly time series. This threshold was determined using a statistically grounded approach, based on the distribution of weekly anomaly values over the entire study period (2019–2024), comprising 314 weekly observations. Based on this threshold, the significant anomaly frequency (SAF) was computed annually, thus representing the percentage of weeks with statistically significant anomalies (Equation (2)):

$$SAF \left(\%\right)=\left({\displaystyle \frac{N_{∆{CH}_4}>Mean Anomaly Threshold }{N_{total}}}\right)\times 100$$

(2)

where

• $N_{∆{CH}_4}>Mean Anomaly Threshold$ is the number of weeks according to which the methane anomaly exceeds the threshold; and
• $N_{total}$ is the total number of weeks in the year.

This approach controls shared background variability by referencing non-dairy regions with similar environmental conditions, thus isolating potential methane enhancements linked to dairy production. By leveraging spatiotemporal differentials, this method builds upon established practices on methane source attribution [11,16], offering a robust framework for detecting localized agricultural emissions within broader atmospheric signals.

2.5. Seasonal Dynamics and Pattern Recognition

2.5.1. Seasonal Decomposition from Time-Series Data

We segmented the annual cycle into four meteorological seasons [42] consistent with Northern Hemisphere conventions: winter (December–February), spring (March–May), summer (June–August), and fall (September–November). For each region and season, we computed the following summary statistics for atmospheric methane concentrations: mean concentration $\left({\mu }_{\mathrm{s}}\right)$, standard deviation ($SD) {\sigma }_{\mathrm{s}}$, minimum and maximum values, and range (seasonal amplitude). This seasonal decomposition enabled the identification of temporal emission patterns across regions and provided a basis for the comparative analysis. To assess seasonal variability between the dairy and non-dairy regions, we conducted paired t-tests for each season. We applied a Bonferroni correction to control Type I errors in multiple comparisons, setting the adjusted significance level to $\alpha =0.0125(0.05/4)$. This approach aligns with established methodologies for analyzing seasonal methane emission patterns [43].

2.5.2. Seasonal Peak Identification

For each region, the weekly methane concentration time series was analyzed using a peak-finding algorithm based on local maxima detection, with a minimum prominence threshold. Each identified peak was assigned to its corresponding meteorological season. To characterize the seasonal dynamics of methane emissions, the dominant peak season for each region and year was determined by analyzing the frequency of significant intra-annual peaks. Significant peaks were identified using a prominence-based threshold, defined as the mean plus one standard deviation of all the peak prominences within the annual time series. The dominant season was assigned based on the meteorological season (winter, spring, summer, or fall) exhibiting the highest count of significant peak occurrences, indicating periods of maximal methane enhancement, thereby enhancing the resistance to noise and short-term anomalies in the time series. As a result, this methodology facilitated a robust and spatially explicit characterization of seasonal peak timing in methane concentrations, enabling inter-regional comparisons and the identification of spatiotemporal patterns in emission dynamics.

2.5.3. Seasonal Amplitude Characterization

To quantify intra-annual variation, we calculated the seasonal amplitude $\left(A\right)$ for each region as the difference between the maximum and minimum seasonal mean concentrations (Equation (3)):

$$A=\left(\begin{array}{c}\mathit{max}{\mu }_S \\ s\in S\end{array}-\begin{array}{c}\mathit{min}{\mu }_S \\ s\in S\end{array}\right)$$

(3)

where $S$ represents the set of four meteorological seasons (winter, spring, summer, fall), and ${\mu }_S$ denotes the mean methane concentration for season $s$. To evaluate relative seasonal dominance, we computed the pairwise seasonal concentration ratios (Equation (4)) for all the pairs of seasons:

$$R_{s_1{s}_2}={\displaystyle \frac{{\mu }_{s_1}}{{\mu }_{s_2}}}, {\forall }_{S_1{S}_2 }\in S, S_1\ne S_2$$

(4)

where $s_1{s}_2$ are distinct seasons. These ratios provided normalized, dimensionless indicators of seasonal contrasts, facilitating the identification of peak emission periods and potential drivers of seasonal methane variability. These comparative metrics are instrumental for emission source attribution and characterization of region-specific seasonal emission behaviors [44].

2.6. Source Attribution and Emission Signature Profiling

2.6.1. Seasonal Fingerprinting of Methane Emission Profiles

The extraction and normalization of seasonal methane patterns employed a systematic approach to identify region-specific emission signatures. This fingerprinting approach is essential for advancing beyond descriptive seasonal analysis toward the quantitative source attribution capabilities required for effective climate policy implementation. The regional methane concentration data were temporally discretized into four seasonal means (winter, spring, summer, fall) for each region–year combination from 2019 to 2024. To enable inter-regional comparability, while minimizing the influence of absolute concentration levels, normalized seasonal indices (${NSI}_{r,s})$ were calculated as the ratio of each seasonal mean (${\mu }_{r,s}$) to the corresponding annual mean (${\mu }_{r,annual}$), with values above 1.0 indicating above-average seasonal concentrations and values below 1.0 representing below-average concentrations (Equation (5)):

$${NSI}_{r_1,s}={\displaystyle \frac{{\mu }_{r,s}}{{\mu }_{r,annual}}}$$

(5)

where

• ${NSI}_{r_1,s}={\displaystyle \frac{{\mu }_{r,s}}{{\mu }_{r,annual}}}$ is the normalized seasonal index for region r in season s; and
• ${\mu }_{annual}={\displaystyle \frac{1}{\left|S\right|}}\sum {s\in S}^{{\mu }_S}$.

These indices were then averaged across the years to produce stable, four-dimensional temporal emission signatures $({NSI}_{r,winter},{NSI}_{r,spring},{NSI}_{r,summer},{NSI}_{r,fall})$ for each region. This approach standardized the temporal patterns across regions with varying methane baselines, enabling the derivation of region-specific emission fingerprints using Euclidean distance-based analysis. To evaluate the distinctiveness of seasonal methane emission patterns between dairy-intensive and non-dairy reference regions, two quantitative approaches were employed: a multivariate hypothesis testing framework and a geometric dissimilarity metric. Hotelling’s $T^2$ multivariate test was used to determine whether the multivariate seasonal profiles of the dairy regions significantly differed from those of the reference regions. To assess the statistical significance, we transformed the T² statistic into an F-distribution and the resulting F-statistic was evaluated at a 0.05 significance level to assess whether the differences in the four seasonal patterns were statistically meaningful. This technique offers robust evidence for identifying consistent seasonal patterns with subtle regional variations across climate and land use types [45,46]. In parallel, the pairwise dissimilarity between the seasonal patterns was quantified using the Euclidean distance in a four-dimensional space in terms of normalized seasonal indices (Equation (6)):

$$d(r_1,r_2)=\sqrt{{\sum _{s\in S}({NSI}_{r_1,s}-{NSI}_{r_2,s})}^2}$$

(6)

where

• $S=\{Winter,Spring,Summer,Fall\}$; and
• $d\left(r_1,r_2\right)$ denotes the dissimilarity between regions $r_1\mathrm{and}{r}_2$.

This distance-based approach captures relative differences in the intra-annual emission structure, allowing for a cross-region comparison regardless of the absolute methane levels. To assess the statistical significance of the observed Euclidean distances, a permutation test was conducted with 10,000 random resampling’s of the region group labels [47]. This generated a null distribution of the inter-group distances under the hypothesis of no difference. The empirical $\rho -\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}\mathrm{s}$ were derived by comparing the observed distances to this null distribution, providing robust inferences, without relying on parametric assumptions.

2.6.2. Hierarchical Clustering of Regional Emission Signatures

To uncover spatial patterns in the methane emission characteristics across the regions, a hierarchical clustering analysis was conducted, based on multiple standardized emission attributes [48,49]. Prior to clustering, feature normalization was applied to ensure comparability across the attributes. Agglomerative hierarchical clustering was then performed using Ward’s linkage method, which minimizes the total within-cluster variance during the merging process. To determine the optimal number of clusters, the silhouette score was computed for each observation. This facilitated the identification of spatially distinct clusters exhibiting similar methane emission dynamics, thereby enabling the classification of regions based on underlying dairy production practices and associated environmental drivers.

2.7. Cross-Validation with TCCON Ground-Based Observations

2.7.1. TCCON Data Acquisition and Processing

To validate our Sentinel-5P TROPOMI methane observations, we incorporated ground-based measurements from the Total Carbon Column Observing Network (TCCON) East Trout Lake station (54.354° N, −104.987° W, Saskatchewan, Canada). This station was selected as the only available TCCON site within our region of interest for methane monitoring. TCCON provides high-precision, ground-based Fourier Transform Spectrometer (FTS) measurements of column-averaged dry air mole fractions of atmospheric greenhouse gases, including methane (XCH₄), with the typical precision being better than 0.25% [50]. The use of TCCON as a validation reference for satellite methane retrievals is well-established, with previous studies demonstrating its effectiveness for TROPOMI validation across multiple trace gases [51]. The TCCON Level 2 data products were obtained in NetCDF format from the TCCON Data Archive (https://tccondata.org/) for the period 2019–2024 [52]. The dataset includes quality controlled XCH₄ measurements, with associated uncertainty estimates, measurement timestamps, and quality flags. The data was filtered using standard TCCON quality control procedures to exclude measurements affected by clouds, instrumental anomalies, or poor atmospheric conditions. The methane concentrations were converted from ppm to ppb units where necessary, and the values were filtered to retain only the measurements within the reasonable atmospheric range during the study period.

2.7.2. TROPOMI Data Selection and Regional Validation

For the validation analysis, we established a dynamic buffer zone around the East Trout Lake TCCON station to extract the corresponding Sentinel-5P observations. This buffer size was selected to balance spatial representativeness, while accounting for: (1) the difference in measurement footprints between the satellite (5.5 × 7 km² individual pixels averaged over the buffer region) and the ground-based (∼2–5 km diameter) observations, (2) atmospheric transport variability that may introduce spatial gradients into methane concentrations, and (3) established practices in satellite–ground validation studies.

2.7.3. Temporal Matching and Data Pairing

Temporal matching between the TROPOMI and TCCON observations was implemented using an adaptive window approach to maximize the number of matched pairs, while maintaining temporal proximity. We tested multiple time windows (12 h, 24 h, 72 h, and 168 h) and selected the optimal window that provided sufficient matched pairs (≥20) for statistical validation. For each TCCON measurement, corresponding TROPOMI observations within the selected time window were identified and aggregated using an inverse distance weighting (IDW) approach. To account for spatial proximity, each XCH₄ value from the surrounding TROPOMI grid cells was weighted by the inverse of its distance to the TCCON site, offset by one unit to avoid division by zero in cases of spatial coincidence [53]. This method emphasizes observations closer to the ground-based site, thereby enhancing its spatial representativeness [54]. The weighted mean was then computed as the ratio of the sum of the weighted XCH₄ values to the sum of the weights, following geostatistical interpolation practices [51].

3. Results

3.1. Spatiotemporal Dynamics of Methane Concentrations at the National Level

3.1.1. Temporal Trends in National Methane Concentrations

The national average methane concentration demonstrated a consistent upward trend over the study period (Figure 2A), with an overall increase of 3.83% from 1819.79 ppb in 2019 to 1889.60 ppb in 2024 (

Table 1. National methane concentration trends and inter-provincial statistical distribution (2019–2024).

Year	National Average (ppb)	Annual Change (%)	Avg. Minimum (ppb)	Avg. Maximum (ppb)	Avg. Range (ppb)	Avg. SD (ppb)
2019	1819.79	-	1745.84	1901.43	155.59	18.03
2020	1841.02	+1.17	1748.72	1911.1	162.37	19.48
2021	1863.45	+1.22	1768.38	1928.02	159.64	15.42
2022	1882.24	+1.01	1768.9	1945.1	176.2	13.76
2023	1875.3	−0.37	1723.39	1933.08	209.69	14.67
2024	1889.6	+0.76	1692.91	1945.69	252.78	15.67
2019–2024	+69.81	+3.83	-	-	-	-
% Change	-	-	−3.03%	+2.33%	+62.47%	−13.09%

), interrupted only by a notable 0.37% decrease between 2022 and 2023.

This temporal pattern coincided with significant changes in the statistical distribution of the methane values across the provinces (Table 1), with distinct spatial heterogeneity evident across the country (Figure 2B). While the mean values increased steadily, we observed divergent trends in the minimum and maximum values: the average provincial minimum values ultimately decreased by 3.03% from 2019 to 2024 (from 1745.84 to 1692.91 ppb), while the maximum values increased by 2.33% (from 1901.43 to 1945.69 ppb). Most strikingly, the average range, which represents the difference between the maximum and minimum values, expanded drastically by 62.47% (from 155.59 to 252.78 ppb), indicating substantially increasing spatial heterogeneity within the provinces, a trend that accelerated during the later years in the study period. The anomalous 2022–2023 decrease in the national means, combined with the widening range values, suggests complex underlying dynamics, potentially involving both anthropogenic and natural factors that require further investigation.

3.1.2. Provincial Variations and Growth Rate Patterns

Analysis of the provincial data revealed an inverse relationship between the baseline methane concentrations and growth rates (

Table 2. Provincial methane concentrations, with absolute and percentage changes. Provinces are arranged in descending order of percentage change.

Province	Mean 2019 (ppb)	Mean 2024 (ppb)	Absolute Change (ppb)	Percentage Change (%)
British Columbia	1789.43	1868.7	+79.27	+4.43
New Brunswick	1818.23	1896.87	+78.64	+4.32
Alberta	1805.91	1883.14	+77.22	+4.28
Ontario	1813.64	1888.81	+75.17	+4.14
Nova Scotia	1830.6	1899.19	+68.58	+3.75
Manitoba	1816.88	1885.09	+68.21	+3.75
Quebec	1820.48	1887.02	+66.54	+3.66
Saskatchewan	1823.98	1889.43	+65.46	+3.59
Newfoundland and Labrador	1821.64	1884.4	+62.76	+3.45
Prince Edward Island	1857.07	1913.33	+56.26	+3.03
National Average	1819.79	1889.6	+69.81	+3.83

). Throughout the study period, Prince Edward Island consistently maintained the highest mean methane concentrations (1857.07 ppb in 2019, rising to 1913.33 ppb in 2024), while British Columbia exhibited the lowest concentrations (1789.43 ppb in 2019, increasing to 1868.70 ppb in 2024). However, British Columbia, despite having the lowest baseline concentrations, had the highest percentage increase (4.43%) over the 6-year period. Conversely, Prince Edward Island, with the highest baseline levels, demonstrated the lowest percentage increase (3.03%).

This pattern was consistent across all the provinces, with those having lower initial concentrations, generally exhibiting higher growth rates. The gap between the highest and lowest provincial means narrowed over the study period, from 67.64 units in 2019 to 44.63 units in 2024, suggesting a potential convergence in provincial methane levels if current trends continue. This convergence pattern, combined with the expanding range values within provinces, indicates complex spatial dynamics governing methane distribution across Canada.

3.2. Regional Methane Concentration Comparison and Differential Frequency

3.2.1. Temporal Dynamics of Regional Methane Concentrations

We performed comprehensive statistical analyses of the methane concentrations from dairy-intensive and non-dairy (reference) regions across Canada, spanning 2019–2024, at weekly intervals.

Table 3. Summary statistics of atmospheric methane concentrations in diary vs. non-dairy regions of Canada.

Metric	Dairy Region (ppb)	Non-Dairy Region (ppb)	Anomaly (ppb)
Mean	1861.03	1843.64	17.39
SD	21.45	26.63	8.24
Minimum	1823.04	1794.87	−4.46
Maximum	1904.53	1901.36	36.39

presents the aggregate statistical parameters, characterizing the complete dataset of 314 weekly observations.

The time-domain analysis reveals a persistent elevation of methane concentrations in the dairy regions compared to the reference regions across the entire study period, with a mean difference of 17.39 ppb. However, this aggregate view obscures the significant temporal heterogeneity in both absolute concentrations and regional differentials. To elucidate these temporal dynamics, we calculated the absolute and percentage changes between consecutive years to quantify these increases precisely, as shown in

Table 4. Evolution of methane concentrations in dairy-intensive vs. non-dairy reference regions demonstrating progressive convergence. Gap reduction percentages show annual decreases in concentration differences between region types.

Year	Dairy Regions Average (ppb)	Non-Dairy Regions Average (ppb)	Gap Reduction (ppb)	Convergence Rate (%)
Baseline–2019	1831.22	1806.80	-	-
2019–2020	1844.37	1823.06	3.11	12.74
2020–2021	1856.32	1836.82	1.81	8.49
2021–2022	1873.20	1855.39	1.69	8.67
2022–2023	1878.38	1867.57	7.00	39.30
2023–2024	1882.76	1872.32	0.37	3.42
Total Change	51.54	65.52	13.98	57.25

.

The methane levels in both the dairy and non-dairy regions show a consistent upward trend from 2019 to 2024. From 2019 to 2024, the dairy regions exhibited an absolute increase of 51.54 ppb in the methane concentrations. The non-dairy regions demonstrated a more pronounced growth trajectory, with an absolute increase of 65.52 ppb. This differential growth pattern contributed to a progressive narrowing of the gap between the two region types, with the concentration difference decreasing from 24.42 ppb in 2019 to 10.44 ppb in 2024, representing a 57.25% reduction over the study period. The convergence between the dairy and non-dairy regions was particularly pronounced during 2022–2023, when the gap reduction reached 39.30% in a single year. Prior to this period, convergence occurred at a steady rate of approximately 8–13% annually. The dramatic convergence in 2022–2023 was followed by a slower gap reduction of 3.41% in 2023–2024, suggesting that the regional differences may be stabilizing. This convergence pattern indicates that non-dairy regions consistently demonstrated higher growth rates that progressively reduced the concentration differential, suggesting that regional methane concentration patterns may be influenced by changing emission factors or atmospheric transport patterns that affect non-dairy regions more substantially than dairy-intensive areas.

3.2.2. Inter-Annual Methane Variability and Regional Methane Anomaly Patterns

We examined the inter-annual variability, at weekly intervals, in the absolute methane concentrations and the differential between the dairy and non-dairy regions to identify critical transition points and pattern shifts. Figure 3 illustrates the weekly year-on-year emissions and anomaly patterns across the study period.

The methane anomaly frequency between the dairy and non-dairy regions demonstrated a consistent decreasing trend throughout the study period. However, the rate of convergence was not uniform. The 2022–2023 transition represents a critical inflection point, with the anomaly decreasing by 7.00 ppb (−39.30%), a substantial single year reduction observed in the dataset (Table 4). This abrupt shift coincided with a reduction in the frequency of significant anomalies (

Table 5. Significant dairy-reference region methane anomaly frequency by year.

Year	Total Weeks	Significant Anomalies	Percentage
2019	52	47	90.38
2020	53	39	73.58
2021	52	32	61.54
2022	52	37	71.15
2023	52	9	17.31
2024	53	12	22.64

) and is also reflected in the national average methane concentration, which dropped by 1882.24 ppb in 2022 to 1875.3 ppb during the same period.

To quantitatively verify the convergence between the dairy and non-dairy regions, we performed statistical tests on the time series of the weekly methane concentration differences. The results (

Table 6. Statistical analysis of dairy-reference methane concentration differential from 2019–2024.

Statistical Test	Statistical Value	Significance
Linear Regression Slope (ppb/week)	−0.04962773	p < 0.001
Linear Regression Slope (ppb/year)	−2.58950421	p < 0.001
Linear Regression R2 coefficient	0.312	-
Mann–Kendall Z-statistic	−10.53	p < 0.001
Estimated Total Change (Absolute in ppb)	−15.12	Decreased
Estimated Total Change (Relative in %)	−61.45
95% Confidence Interval (Annual Slope, ppb/year)	[−3.03, −2.14]	-

) provide strong statistical evidence of a significant narrowing trend in the dairy-reference methane anomaly. The parametric (linear regression) and non-parametric (Mann–Kendall) tests confirmed a statistically significant negative trend in the methane anomaly. The coefficient of determination (R² = 0.312) relates to 31.2% of the variability in the dairy-reference region convergence pattern. The model estimates a total reduction of 15.12 ppb in the difference between the dairy and reference regions, representing a 61.45% decrease over the study period. This statistically significant convergence aligns with our observation on the reduced anomaly frequency and suggests a shift in the relative methane dynamics between dairy and non-dairy landscapes, although the moderate R² value indicates substantial unexplained variability in the convergence pattern.

3.3. Seasonal Methane Fingerprints for Dairy Regions

3.3.1. Annual Methane Concentration Patterns

The atmospheric methane concentrations exhibited distinct spatial and temporal patterns across the fourteen dairy regions during 2019–2024 (

Table 7. Annual mean methane concentrations (ppb) by dairy region and year.

Dairy Region	2019	2020	2021	2022	2023	2024	Average
Ontario: Southern	1857.31	1864.08	1876.56	1886.67	1891.64	1899.79	1879.34
Saskatchewan: Central	1842.84	1863.80	1872.31	1877.26	1882.06	1885.81	1870.68
Alberta: Southern	1844.85	1857.14	1870.29	1877.17	1882.51	1885.12	1869.51
BC: Fraser Valley	1842.36	1856.04	1864.36	1879.81	1886.15	1883.21	1868.66
Prince Edward Island: Central	1846.41	1848.94	1860.99	1882.81	1883.73	1892.35	1869.21
Alberta: Central	1828.88	1852.76	1864.42	1871.40	1874.53	1881.99	1862.33
Manitoba: Eastern	1833.88	1843.30	1860.67	1869.16	1879.50	1887.51	1862.34
Ontario: Eastern	1831.39	1842.01	1852.13	1871.90	1878.49	1890.75	1861.11
Manitoba: Interlake	1830.15	1846.16	1858.30	1870.93	1878.39	1880.80	1860.79
Quebec: St. Lawrence	1830.06	1843.79	1850.97	1871.99	1877.62	1886.33	1860.13
Quebec: East Township	1824.83	1836.31	1848.91	1869.52	1875.14	1881.18	1855.98
Nova Scotia: Valley	1822.00	1829.88	1842.69	1872.58	1875.21	1884.50	1854.48
New Brunswick Central	1818.04	1825.77	1834.08	1865.82	1866.37	1878.46	1848.09
BC: Vancouver Island	1803.41	1811.13	1831.78	1857.73	1866.03	1840.91	1835.17

). The annual mean methane concentrations ranged from 1803.41 ppb (BC Vancouver Island, 2019) to 1899.79 ppb (Ontario Southern, 2024), with a zonal average of 1861.27 ppb. The temporal evolution of methane concentrations across the majority of dairy regions exhibits a consistent upward trend.

The spatial distribution of the methane concentrations demonstrated notable regional variations, with Ontario southern consistently maintaining the highest concentrations throughout the study period (1879.34 ppb 6-year average). This was followed by Saskatchewan central (1870.68 ppb) and Alberta southern (1869.51 ppb), suggesting a potential correlation between intensive dairy production systems and elevated methane levels in these regions. Conversely, lower methane concentrations were consistently observed in regions such as New Brunswick Central (1848.09 ppb) and BC Vancouver Island (1835.17 ppb). The collective regional average increased from 1832.60 ppb in 2019 to 1882.76 ppb in 2024, representing a 2.74% increase over the study period. This progressive increase aligns with global atmospheric methane trends and underscores the contribution of dairy production to rising greenhouse gas concentrations. Notably, several regions displayed a plateau in methane concentrations during the 2023–2024 period, potentially signaling the influence of emerging stabilizing factors within the dairy sector.

3.3.2. Seasonal Amplitude and Variability Analysis

The intra-annual variability in methane concentrations, quantified by the seasonal amplitude (

Table 8. Seasonal amplitude of XCH₄ (ppb) by region and year (2019–2024). The color gradient represents amplitude magnitude, with darker colors indicating higher values.

Dairy Region	2019	2020	2021	2022	2023	2024	Average
BC: Vancouver Island	61.12	51.38	13.86	86.63	156.60	9.60	63.20
Quebec: St. Lawrence Valley	40.21	74.56	62.19	66.96	44.84	65.56	59.06
Quebec: Eastern Township	20.65	54.06	64.86	62.79	51.74	56.08	51.70
New Brunswick: Central	55.04	38.71	84.89	79.43	63.39	57.23	63.11
Nova Scotia Annapolis Valley	32.63	45.12	55.88	93.40	46.33	54.91	54.71
Manitoba: Interlake	39.72	56.59	48.42	65.75	61.52	59.38	55.23
Ontario: Southern	30.55	48.22	45.33	42.99	34.87	47.76	41.62
Manitoba: Eastern	45.59	46.95	47.84	53.03	51.62	67.97	52.17
Alberta: Central	75.99	41.71	39.15	56.78	51.32	63.32	54.71
Saskatchewan: Central	43.49	29.38	35.74	63.22	37.36	66.04	45.87
PEI: Central	52.31	37.72	75.55	69.11	29.37	44.96	51.50
BC: Fraser Valley	37.34	24.81	35.66	82.82	49.12	35.09	44.14
Alberta: Southern	45.08	35.40	43.87	57.32	47.06	58.14	47.81
Ontario: Eastern	22.49	41.47	52.32	65.89	19.19	43.14	40.75

), revealed distinct patterns of temporal fluctuation across the dairy regions. The 6-year average seasonal amplitudes ranged from 40.75 ppb in Ontario eastern to 63.20 ppb in BC Vancouver Island, with a multi-region mean of 51.63 ppb. This represents an average intra-annual fluctuation of approximately 2.77% relative to the annual mean concentrations.

The magnitude of the seasonal amplitudes exhibited substantial regional differentiation, with coastal and maritime-influenced regions generally demonstrating higher intra-annual variability. BC Vancouver Island (63.20 ppb) and New Brunswick central (63.11 ppb) consistently maintained high seasonal amplitudes, suggesting that oceanic influence and associated meteorological patterns may amplify seasonal methane concentration cycles. Conversely, continental regions, such as Ontario eastern (40.75 ppb) and Ontario southern (41.62 ppb), exhibited more moderate seasonal variations, indicating potentially more stable emission patterns throughout the years. The temporal analysis of seasonal amplitudes revealed notable year-to-year fluctuations, without consistent trends, across the study period. The collective regional average seasonal amplitude varied from 44.56 ppb in 2019 to 56.62 ppb in 2024, representing a 27.07% increase in the intra-annual variability. However, this overall increase masks substantial inter-annual variations, with 2022 exhibiting a high mean regional amplitude (64.72 ppb) and 2019 showing the lowest mean regional amplitude (44.56 ppb). Notably, BC Vancouver Island, in 2023, exhibited an exceptional value, namely the largest seasonal amplitude of 156.60 ppb, representing an 8.39% fluctuation relative to its annual mean.

3.3.3. Phase Shift Analysis and Timing of Peak Emissions

The seasonal distribution of methane concentrations revealed consistent temporal patterns in the emission peaks across the study regions. Analysis of the seasonal maxima identified fall (September–November) and winter (December–February) as the predominant periods of peak methane emissions, with specific regional preferences evident in the data (Figure 4).

Fall emerged as the dominant peak season for methane emissions in 42.9% of the regions, with Ontario southern exhibiting the most consistent pattern and recording fall peaks in all six years. Other regions with strong fall dominance included BC Fraser Valley, Alberta southern, Ontario eastern, Quebec Eastern Township, and Prince Edward Island central. In contrast, winter dominance was observed in Saskatchewan central and Quebec St. Lawrence Valley, both characterized by continental climatic influences. The remaining 42.9% of regions displayed mixed seasonal patterns, alternating between fall and winter peaks. Notably, BC Vancouver Island stood out with unique occurrences of summer and spring peaks, reflecting its maritime climate.

The temporal analysis further revealed notable year-to-year shifts in peak timing. While 2019 and 2022 were predominantly winter dominated, 2020 and 2021 saw a strong fall signal. The 2022 winter shift, with 78.6% of regions peaking in winter, coincided with a narrowing of the gap between dairy and non-dairy methane levels. Despite this variability, individual regions generally maintained consistent seasonal preferences, indicating that local factors strongly govern emission timing. Spring and summer peaks were extremely rare, accounting for 1.9% of all the regional year observations, both largely limited to BC Vancouver Island. This pronounced seasonal exclusivity reinforces the diagnostic value of fall–winter peaks as a “fingerprint” of dairy methane emissions. The predominance of fall and winter maxima across 92.9% of the observations provides a reliable seasonal framework for distinguishing dairy emissions from other methane sources with differing temporal signatures.

3.3.4. Regional Fingerprint Derivation and Analysis

While the preceding seasonal analysis identified broad temporal patterns, effective source attribution requires the use of standardized methods to differentiate dairy emissions from other sources, with distinct seasonal behaviors. To address this, we developed normalized seasonal “fingerprints”, quantitative indices calculated as the ratio of seasonal to annual mean concentrations for each region (

Table 9. Normalized seasonal indices (seasonal mean/annual mean) by region.

Dairy Region	Winter/Annual	Spring/Annual	Summer/Annual	Fall/Annual
BC: Fraser Valley	0.9993	0.9993	0.9976	1.0038
BC: Vancouver Island	1.0048	0.9988	0.9976	0.9988
Alberta: Central	1.0045	0.9969	0.9959	1.0027
Alberta: Southern	1.0037	0.9964	0.9961	1.0038
Saskatchewan: Central	1.0044	0.9964	0.9964	1.0028
Manitoba: Interlake	1.0055	0.9954	0.9962	1.0028
Manitoba: Eastern	1.0043	0.9950	0.9969	1.0038
Ontario: Southern	1.0005	0.9968	0.9981	1.0046
Ontario: Eastern	1.0024	0.9962	0.9974	1.0040
Quebec: St. Lawrence Valley	1.0063	0.9950	0.9961	1.0026
Quebec: Eastern Township	1.0032	0.9955	0.9970	1.0043
New Brunswick: Central	1.0046	0.9944	0.9972	1.0038
Nova Scotia Annapolis Valley	1.0033	0.9966	0.9968	1.0033
Prince Edward Island: Central	1.0026	0.9949	0.9972	1.0053

). The normalized seasonal indices revealed a consistent fundamental pattern across all the regions, characterized by below-average concentrations in spring and summer, contrasted with above-average concentrations in fall and winter.

This universal pattern was modulated by region-specific variations that constituted the subtly different regional fingerprints of each area. All the regions exhibited spring minima, with spring-to-annual ratios ranging from 0.9944 (New Brunswick central) to 0.9993 (BC Fraser Valley), representing a 0.49% spread across the regions. Similarly, summer concentrations were consistently below the annual averages, with summer-to-annual ratios ranging from 0.9959 (Alberta central) to 0.9981 (Ontario southern). The elevated methane concentrations during winter and fall exhibited greater regional differentiation, serving as key diagnostic components of the seasonal regional signatures. The winter-to-annual concentration ratios ranged from 0.9993 (British Columbia, Fraser Valley) to 1.0063 (Quebec St. Lawrence Valley), corresponding to a spread of 0.70%. Similarly, the fall-to-annual ratios ranged from 0.9988 (British Columbia, Vancouver Island) to 1.0053 (Prince Edward Island, central), yielding a spread of 0.65%. The high degree of consistency in the seasonal indices across the dairy regions suggests common underlying mechanisms that influence methane dynamics within Canada’s dairy sector. Prairie regions (Manitoba, Saskatchewan, and Alberta) exhibited strong winter maxima coupled with pronounced spring minima, a pattern consistent with continental climate influences on both emission processes and atmospheric dynamics. Atlantic provinces (Prince Edward Island, Nova Scotia, and New Brunswick) demonstrated a consistent pattern of fall dominance with moderate winter maxima. Ontario and Quebec regions showed mixed patterns, potentially reflecting their transitional position between continental and maritime influences.

The most distinctive regional fingerprints included Quebec St. Lawrence Valley, which exhibited the highest winter maximum (1.0063) coupled with a substantial spring minimum (0.9950), representing a 1.13% seasonal swing. Manitoba Interlake demonstrated a strong winter pattern (1.0055), with the lowest spring value (0.9954). Prince Edward Island central showed the highest fall maximum (1.0053), with a pronounced spring minimum (0.9949). BC Vancouver Island displayed a more balanced seasonal pattern, with moderate winter elevation (1.0048), but the smallest fall enhancement (0.9988), consistent with its maritime climate, which moderates seasonal extremes.

The quantitative similarity analysis using Euclidean distance metrics confirmed the high level of consistency in the seasonal patterns across all the regions, with all paired-region distances below 0.01 (Figure 5). The closest similarities were observed between geographically proximate regions, including Quebec Eastern Township and New Brunswick central (distance: 0.0011), and Manitoba regions (distance: 0.0012). Despite these variations, the overall pattern of spring minima and fall–winter maxima remain remarkably consistent across all the regions, suggesting similar underlying drivers of seasonal methane dynamics throughout Canada’s dairy sector. This pattern of distance-based clustering provides quantitative evidence that while seasonal patterns follow a common fundamental structure, subtle regional “fingerprints” do emerge that reflect the local climate and potentially the management conditions.

3.4. TROPOMI Methane Validation Using TCCON Ground-Based Observations

The temporal matching algorithm successfully identified 230,443 matched pairs between the TROPOMI and TCCON observations over the 6-year study period (2019–2024), representing a validation dataset with an average of 38,407 pairs per year (

Table 10. Validation statistics and inter-annual performance (statistics computed from all 230,443 matched pairs across the entire study period. Overall correlation computed from all individual paired measurements, not from annual means).

Year	TCCON Mean (ppb)	TROPOMI Mean (ppb)	Bias (ppb)	Correlation (r)	MAE (ppb)
2019	1827.11	1839.09	11.99	−0.70	16.25
2020	1851.27	1862.12	10.85	0.52	13.14
2021	1863.91	1870.27	6.36	0.61	9.84
2022	1868.86	1865.40	−3.46	0.22	9.89
2023	1888.36	1880.32	−8.04	0.78	9.84
2024	1895.29	1886.47	−8.83	0.84	9.78
Overall	1865.80	1867.28	1.48	0.84	11.46

).

The validation performance exhibits an improvement over the study period, with the correlation coefficients increasing from a poor initial agreement (r = −0.70 in 2019) to a strong positive correlation (r = 0.84 in 2024), as detailed in Table 10. This upward trend, equivalent to an average increase of approximately 0.26 correlation units per year, likely reflects enhanced atmospheric correction procedures, and a growing understanding of regional retrieval challenges. The bias characteristics also reveal a systematic shift over time (Table 10), transitioning from a consistent positive bias in the early years (+11.99 ppb in 2019 and +10.85 ppb in 2020) to a negative bias in the later years (−8.04 ppb in 2023 and −8.83 ppb in 2024). Despite this temporal evolution, the overall mean bias across the 6-year period remains modest at 1.48 ppb, suggesting that positive and negative deviations largely offset one another when aggregated over longer timescales. The mean absolute error (MAE) demonstrates a notable improvement, decreasing from 16.25 ppb in 2019 to 9.78 ppb in 2024, thereby representing an overall reduction of approximately 40%. After 2020, the MAE stabilizes around 10 ppb, indicating enhanced retrieval precision in the latter half of the study period. Both observational systems consistently capture the long-term increase in atmospheric methane. TCCON observations indicate an annual growth rate of +11.4 ppb per year, while the TROPOMI retrievals reflect a slightly lower trend of +9.5 ppb per year, accounting for approximately 83% of the TCCON-observed trend magnitude. The trend correlation between the two datasets (r = 0.85; R² = 0.72) underscores the TROPOMI’s reliability in detecting inter-annual variability. Over the 6-year study period, the TCCON measurements recorded a total methane increase of 3.7%, while the TROPOMI observed a 2.6% rise, both values aligning with established global atmospheric methane growth trends.

4. Discussion

A six-year analysis from 2019 to 2024 of Sentinel-5P TROPOMI-derived methane concentrations across Canadian dairy-intensive and selected non-dairy regions reveals pronounced spatial and temporal patterns in the atmospheric methane concentration. At the national scale, the methane levels increased by 3.83%, aligning with global trajectories [2]. However, the modest 0.37% decline between 2022 and 2023 highlights substantial inter-annual variability, which is likely driven by climatic anomalies. Despite convergence in the provincial means, the overall range of methane concentrations expanded by 62.47%, indicating rising spatial heterogeneity, and challenging conventional assumptions of uniformly increasing emissions in northern latitudes [55]. At the provisional level, an inverse relationship emerged between the baseline methane concentrations and their growth rates, contrasting with previous findings that identified persistent emission hotspots, exhibiting acceleration [44]. While certain convergence patterns resonate with the regional trends reported by [56], the broader spatial dynamics diverge from global assessments [2], pointing to distinct methane behaviors within Canada’s agroecosystems. The temporal variations across the provinces further validates the hypothesis that methane distributions are shaped by intricate environmental interactions [57,58]. The quasi-experimental comparison between the dairy and non-dairy zones revealed a 57.23% reduction in the methane anomaly, raising concerns about the reliability of established satellite-based emission attribution frameworks. Traditional models, including GOSAT inversion studies, have reported stable sectoral enhancements of 15 to 25 ppb [12,13]. Our findings, however, deviate from earlier model projections [59] that predicted persistent enhancements of 20 to 35 ppb in dairy regions without evidence of narrowing anomalies. The moderate temporal trends (R² = 0.312) and the substantial unexplained variance (68.8%) highlight the influence of complex and interacting drivers, consistent with prior findings on methane’s multifactorial controls [58,60]. Despite a narrowing differential, elevated atmospheric methane concentrations over dairy regions persist (mean: 17.39 ppb), consistent with national emission inventories. For instance, [61] documented 23 Gg of methane emissions from the dairy sector in 2021, primarily from enteric fermentation and manure management, sources that spatially correspond with the satellite-detected enhancements. The validation using TCCON ground-based observations yielded strong agreement (R = 0.85, R² = 0.72), with the mean absolute error (MAE) improving from 10 to 16 ppb to 9.78 ppb in 2024, confirming the TROPOMI’s robustness for inverse modeling applications [51,62]. The seasonal methane signatures were highly consistent across the regions, as the Euclidean distance-based quantitative similarity shows that all the inter-regional distances are below 0.01, thereby validating the spatial coherence of the seasonal cycles. Over 92.9% of the zones exhibited peak atmospheric methane concentrations during the fall and winter season, a pattern consistent with seasonal emission intensification from confined livestock systems. These peaks coincide with reduced grazing access and increased use of high fiber preserved forages, reinforcing findings by [63] that identified dry matter and fiber intake as major predictors of enteric methane production. In contrast, the widespread spring minimum aligns with biogeochemical theories emphasizing enhanced soil methane oxidation as temperatures rise and soils thaw. Studies by [64,65] demonstrate the capacity of microbial communities in thawed soils to consume atmospheric methane, converting it into carbon dioxide. These biophysical processes, coupled with modified manure management during planting seasons [66,67], perhaps explain the observed declines. Additionally, increased atmospheric mixing and turbulence during spring [44] enhance methane dispersion, offering further mechanistic support for these patterns. The regional variability in seasonal amplitudes, such as the exceptionally high 156.60 ppb anomaly over the BC Vancouver Island region in 2023, reflects the influence of mesoscale meteorological conditions. As previously documented by [45,68], coastal and maritime climates are prone to methane accumulation due to altered boundary layer dynamics. Similarly, winter enhancement mechanisms are supported by evidence of reduced atmospheric mixing and persistent inversions [58], which suppress vertical dispersion and trap methane near the surface. Findings from [64,69] also show that permafrost dynamics during freeze-up can push methane upward, while snow cover acts as an effective barrier, reducing emission release rates by up to 20 times compared to exposed soils. The observed synchronicity of seasonal patterns, largely governed by climatic zones rather than provincial boundaries, supports the conclusions from [45] that coastal ecosystems exhibit more pronounced seasonal methane signals than continental interiors. These interpretations are reinforced by inverse modeling efforts, such as the Integrated Methane Inversion (IMI) model [70], which attributes over 81% of regional anthropogenic methane emissions to livestock. However, the moderate coefficient of determination (R² = 0.312) reflects inherent limitations of satellite-based source attribution. Prior work by [12,71] cautions against simplistic assumptions in the absence of detailed meteorological, land management, and policy variables. The considerable residual variance suggests that more nuanced frameworks are required to fully capture the complexity of methane dynamics [13]. Evolving emission profiles increasingly challenge legacy attribution methods. The inflection observed in 2022 aligns with the regional meteorological shifts described by [44] and may also reflect increased natural emissions from wetlands or altered transport dynamics [65,69]. These seasonal patterns largely reflect livestock husbandry and environmental interactions, as outlined by [72,73]. During colder months, cattle are housed in confined environments, concentrating on emissions from manure storage systems compared to more dispersed summer grazing [5]. While these results, which align with [71], suggest that the observed variability may also be driven by atmospheric transport rather than emission source differences. The recurring spring minimum is likely driven by a combination of methane oxidation [65], changes in manure handling [5,66], and intensified atmospheric mixing [12]. The convergence of seasonal signals across dairy-producing provinces reflects standardized management regimes and environmental conditions. While this supports the findings by [5], alternative interpretations proposed by [74] attribute such regularity to shared atmospheric transport processes rather than emission uniformity, underscoring the complexity of emission attribution in dynamic atmospheric systems [14]. These dynamics unfold within the context of Canada’s evolving climate policy landscape, which prioritizes reductions in livestock-related methane [61]. The post-2022 acceleration suggests that the observed trends may be equally attributable to non-anthropogenic factors, such as wetland emissions [62] or atmospheric shifts [63]. Given the considerable inter-annual variability and the intricate convergence patterns observed, direct attribution to policy action remains challenging [75]. This underscores the need for integrative analytical frameworks that can disentangle anthropogenic signals from natural variability. The fall–winter emission peaks identified in this study offer clear temporal windows for implementing targeted mitigation strategies, in line with temporally optimized interventions [70,76]. Moreover, the regional clustering of emission behaviors aligns with calls for spatially differentiated mitigation efforts, as supported by Canadian modeling initiatives [70]. To enhance attribution accuracy, developing a multi-variable attribution framework that incorporates meteorological reanalysis, detailed emission inventories, and policy implementation timelines is essential to surpass the current explanatory threshold. These region-specific atmospheric methane signatures provide an essential foundation for addressing global calls for improved methane monitoring and mitigation strategies for climate change [1].

5. Conclusions

This study presents a comprehensive satellite-based seasonal fingerprinting analysis of atmospheric methane dynamics across Canadian dairy-farming regions, representing a significant advancement from traditional static monitoring toward dynamic attribution frameworks. These frameworks are designed to account for temporal variability, spatial heterogeneity, and evolving environmental and policy conditions, thereby enhancing attribution accuracy in complex agricultural landscapes. The findings challenge prevailing assumptions of spatially persistent methane hotspots, revealing instead a notable trend toward regional equilibration. This observed convergence between dairy and non-dairy regions, which is characterized by strong negative trends and high statistical significance, indicates that methane emission patterns are governed by region-specific drivers rather than uniform agricultural intensification. Divergent growth trajectories in provinces such as British Columbia and Prince Edward Island, each originating from different baselines, further highlight the influence of localized environmental, operational, and policy factors on emission dynamics. Central to this study is the seasonal fingerprinting approach, which demonstrates that methane exhibits distinct temporal signatures that are not only detectable, but are also actionable. This capability transforms satellite-based observations from passive monitoring tools into strategic instruments for guiding agricultural emission reduction. By identifying specific seasonal windows with heightened emission sensitivity, the method supports precision agriculture practices aimed at maximizing mitigation effectiveness. Moreover, the observed regional consistency in seasonal patterns facilitates the development of coordinated management strategies, while still allowing flexibility for local adaptation. The research underscores the need for a fundamental reconceptualization of emission monitoring systems. Effective attribution requires more than observational precision. It demands analytical sophistication and adaptive capacity to accommodate non-stationary baselines, evolving source distributions, and variable meteorological influences. This shift is essential to ensure that monitoring frameworks remain robust and policy relevant under dynamic environmental conditions. While the observed convergence patterns are statistically robust, their moderate explanatory power also underscores the current limitations of satellite-based systems. Future research should embrace the complexity unveiled in this analysis by developing multi-variable attribution models that integrate meteorological drivers, evolving management practices, and policy implementation timelines for the development of comprehensive emission attribution systems. Together, these insights provide a foundation for next-generation methane monitoring frameworks, offering actionable guidance for evidence-based emission reduction in the agricultural sector.