Our Arctic–Boreal domain was defined based on The Boreal–Arctic Wetland and Lake Dataset – BAWLD (Olefeldt et al., 2021), and we divided this region into 6 sub-regions for more detailed spatial analyses (Alaska, western Canada, eastern Canada, Europe including Greenland, western Russia, eastern Russia, Fig. 1). In recent decades, the atmospheric observation network suitable for inverse modeling has expanded across the Arctic, with a considerable increase in available sites after 2010 (Vogt et al., 2025). However, due to data-sharing disruptions associated with the ongoing conflict involving Russia and Ukraine, observational data from Russian stations has been limited since 2022. Consequently, this study focuses on the period from 2010 to 2021, when data coverage from surface stations was more consistent across the full domain.

In this study, we utilize the JSBACH model (Reick et al., 2021), the land component of the MPI-ESM (Mauritsen et al., 2019), to estimate bottom-up wetland CH4 emissions. JSBACH is run in standalone mode at T63 resolution (approximately 1.85°, or 185 km) and driven by CRUJRA2.3 (Harris, 2019) climate forcing. Soil hydrology and thermodynamics follow the multilayer formulation of Hagemann and Stacke (2015), with permafrost-related processes implemented as described by Ekici et al. (2014). Soil organic carbon (SOC) decomposition is simulated as a first-order decay process that depends on surface air temperature, water availability, and litter size, following the YASSO model formulation (Tuomi et al., 2011) and its implementation in JSBACH by Goll et al. (2015).

The wetland area fraction of the grid is determined using TOPMODEL (Beven and Kirkby, 1979), a conceptual rainfall–runoff model that estimates inundation based on the compound topographic index (CTI). If the inundated fraction of the grid is non-frozen (depending on the soil temperature), it is considered a CH4-emitting area. The methodology for wetland CH4 production and transport is adopted from Riley et al. (2011), and the details of the TOPMODEL and its implementation for wetland CH4 within JSBACH are outlined in Kleinen et al. (2020).

Methane production and the transport pathways that move CH4 to the surface (diffusion, plant-mediated aerenchyma and ebullition) follow the scheme of Riley et al. (2011), as implemented in JSBACH by Kleinen et al. (2020). Under anaerobic conditions, a proportion of SOC (fCH4) is converted to CH4, while the remaining is converted to CO2. The temperature dependence of fCH4 is represented using a Q10 formulation. 1fCH4=fCH4,baseline⋅Q10(Tsoil-295)/10K

Oxidation reduces the amount of CH4 that reaches the atmosphere. Consequently, the net CH4:CO2 emission ratio depends on production and oxidation rates, as well as transport pathways, which control the amount of CH4 exposed to oxidation. A simplified conceptual relationship is as follows: 2CH4:CO2=fCH4P-OCH4(1-fCH4)P+OCH4 where P is gross anaerobic SOC decomposition and O is the amount of CH4 oxidized before emission.

To evaluate how sensitive CH4 wetland emission estimates are to key parameters, we conducted nine experiments in which we varied only the Q10 coefficient for CH4 production and the baseline fCH4 fraction (Fig. 2b). Specifically, we tested three different Q10 values ranging from 1.4 to 2.2, consistent with commonly used values reported in literature review (Moser et al., 2026), and baseline fCH4 fractions from 0.33 to 0.38. These combinations are summarized in Table 1 and were chosen to identify parameter sets that best align with the observed atmospheric data. All other carbon decomposition, hydrological, transport, and oxidation processes follow the standard JSBACH configuration.

We used the Jena CarboScope Inversion System (Rödenbeck, 2005) to quantify CH4 emissions between the surface and the atmosphere globally from 2010 to 2021, with the evaluation and interpretation of fluxes focused on the Arctic–Boreal region. This is a linear Bayesian framework that infers surface–atmosphere CH4 fluxes by combining prior flux estimates with atmospheric CH4 mole fraction measurements and accounting for their respective uncertainties. The flux vector f represents the net flux per grid cell per time step. The Jena CarboScope enables f to be represented as the sum of different flux components, each of which is modelled independently using its own statistical linear flux model. These independent a priori error covariance structures allow deviations from the prior flux estimate to be attributed to specific components during the inversion process. In this study, the a priori shape uncertainty was defined as 100 % of the prior flux for each flux category. All flux categories were optimized, assuming spatial correlation lengths of ∼500 km and temporal correlation lengths of about 15 d. Temporal and spatial fluxes are optimized within a Bayesian inversion framework that minimizes a cost function combining prior and observational constraints. The solution is obtained analytically using the linear Bayesian approach, which yields maximum posterior flux estimates and their associated uncertainties. Details of the cost function formulation and solution method can be found in the CarboScope technical report (Rödenbeck, 2005).

A total of 154 stations were assimilated for the global domain (Fig. 1, Table S1). These CH4 observations were obtained from multiple global and regional networks (ICOS RI et al., 2024; Panov et al., 2021; Sasakawa et al., 2010, 2025; Schuldt et al., 2023), with the majority of sites located in the Northern Hemisphere, including 33 stations within the Arctic–Boreal domain. Most observational data used in this study were accessed through NOAA GML ObsPack (Schuldt et al., 2023), ICOS Carbon Portal (ICOS RI et al., 2024), World Data Centre for Greenhouse Gases (WDCGG) database (10.50849/WDCGG_CH4_ALL_2023; Dinoi et al., 2023), and JR-STATION network (Sasakawa et al., 2010, 2025); further details are provided in the “Data Availability section”. Detailed information on the stations with assimilated data is given in Table S1. For tower sites with multiple intake heights available, we assimilated only data from the highest height in the inversion, and for the continuous data, we use only daytime measurements. The transport model used in CarboScope is the TM3 global atmospheric tracer model, an Eulerian transport model that solves the continuity equation (and parameterizations of boundary layer and convective mixing) for atmospheric tracers in a three-dimensional grid over the globe (Heimann and Körner, 2003). The model has a spatial resolution of approximately 3.8° latitude by 5° longitude, with 19 vertical layers, and it is driven by meteorological inputs from the NCEP reanalysis dataset (Kalnay et al., 1996). Flux inversions were conducted at the TM3 spatial resolution and a daily temporal resolution. Since the model is initialized with a homogeneous background concentration of methane, it is run for at least one year before to the period of interest to avoid any impact resulting from the model spin-up. To account for model-data mismatch, including the representation error of the measurements within the transport model, each station is assigned a weekly error value based on how well the atmospheric transport model can capture local atmospheric dynamics. For example, for mountain sites and stations near shores samples are assigned a smaller error of 15 ppb, whereas surface sites in regions with complex circulation patterns receive a larger error of 30 ppb. Additionally, to ensure balanced representation across observational sites, particularly between continuous and sparse time series, we applied a data density weighting scheme, assigning equal influence to each weekly period, regardless of data frequency. Without this adjustment, sites with high-frequency data would dominate the cost function solely because of the greater number of observations. To avoid this, the uncertainty of each measurement is multiplied by the number of observations per week. This corresponds to the assumption that errors are correlated on weekly timescales, meaning that one week of hourly data provides roughly the same amount of independent information as one weekly flask sample (Rödenbeck, 2005).

Prior CH4 flux estimates include five source categories, all of which were optimized: wetlands, other natural sources, anthropogenic, ocean and fire emissions. The monthly mean emissions from wetlands and fires were obtained from the JSBACH model (Kleinen et al., 2020), as previously described. Fire emissions represent the simulated biomass burning emissions of JSBACH and were prescribed as monthly varying prior fluxes. Additional natural sources, such as termites and wild animal emissions taken from JSBACH (Kleinen et al., 2020) and geological emissions from Etiope et al. (2019) were combined as the “other natural source” category. Emissions from oceans were obtained from Weber et al. (2019) and implemented as a non-seasonal climatology. Anthropogenic emissions were obtained from the EDGAR inventories database (https://edgar.jrc.ec.europa.eu, last access: 24 January 2024) version 8 (Crippa et al., 2023) and are provided as monthly global fluxes. This category includes emissions from agriculture, livestock, waste management, fossil fuel exploitation and other minor anthropogenic sources except biomass burning. Emissions of CH4 from inland water (freshwater) were not included as a separate prior category and are therefore not explicitly optimized in the inversion framework.

CH4 chemical loss includes loss due to OH and Cl in the troposphere, as well as OH, Cl, and O(1D) in the stratosphere. For tropospheric OH, we use the monthly three-dimensional OH fields calculated by Spivakovsky et al. (2000), which are based on observed climatological distributions of OH precursors and scaled to match the observed CH3CCl3 lifetime. The monthly climatological loss rates of CH4 in the stratosphere due to OH, Cl, and O(1D) were derived from a simulation of the ECHAM5/MESSy1 chemistry transport model (Jöckel et al., 2006). Additionally, tropospheric Cl loss is simulated using a recent model-derived estimate of tropospheric Cl (Hossaini et al., 2016). The surface sink from upland soils and the ocean was implemented as a zeroth-order reaction with prescribed reaction rates that occur only in the surface-most model layer. Reaction rates for the microbial oxidation of atmospheric CH4 in soil were based on the uptake estimates from the LPJ-Bern model (Spahni et al., 2011).

Previous studies have used atmospheric inversion models to evaluate different bottom-up estimates and determine which one best reproduces observed atmospheric CH4 data (Kim et al., 2011; Miller et al., 2016), providing an effective framework for model evaluation. In this study, we evaluated the performance of different JSBACH parameterizations by using the CH4 wetland emission outputs from each experiment as wetland prior fluxes in a top-down atmospheric inversion framework. The inversion then generated posterior fluxes, reflecting the adjustments needed to align the prior emissions with atmospheric CH4 observations. These adjustments take into account uncertainties in atmospheric transport, observational errors, and model representation. In this study, we used the model adjustment defined as the difference between posterior and prior fluxes, calculated as the mean monthly and mean annual values across the Arctic–Boreal region from 2010 to 2021. First, we identified the parameterization resulting in the lowest mean model adjustment across the entire domain.

For the monthly analysis, we first computed the mean monthly prior flux and the mean monthly posterior flux, and then defined the model adjustment as the difference between these two means. For the annual analysis, we calculated the mean annual prior and posterior fluxes and again defined the adjustment as their difference. This allowed us to determine which JSBACH configuration provided the best overall agreement with atmospheric constraints at the pan-regional scale and investigate temporal variability.

Next, we examined spatial variability of the difference between posterior and prior fluxes using different JSBACH parameterizations as wetland priors. At the grid-cell level, we identified the parameter combination that minimized annual model adjustment, thereby providing the best match to the top-down atmospheric constraints. To conduct this analysis, an ensemble of posterior fluxes was calculated based on each CH4 production Q10 value from the prior wetland flux. This approach was supported by the observation that CH4 production Q10 significantly influenced CH4 emission estimates compared to the baseline fCH4 fraction. Additionally, posterior fluxes from priors with different baseline fCH4 fraction scenarios remained highly similar for a given Q10 value. As a result, maps were created by calculating the absolute difference between the posterior ensemble of the respectively Q10 value and prior CH4 fluxes for each experiment at each grid-cell. Then, the annual mean adjustment was calculated and we identified the parameterization that resulted in the smallest adjustment at each grid-cell. In summary, each grid-cell shows the experiment that best matched the atmospheric CH4 observations.

Table 1 summarizes the experiments and parameters combinations that have been tested in the JSBACH model and used as a wetland prior in the atmospheric inversions. Across the Arctic–Boreal region, our nine experiments produced annual mean CH4 wetland estimates ranging from 13.8 to 33.5 TgCH4yr-1. These estimates are consistent with previously published bottom-up estimates of ∼15–50 TgCH4yr-1, with most studies reporting mean values near 20–25 TgCH4yr-1 (Christensen et al., 1996; Ying et al., 2025; Yuan et al., 2024; Zhang et al., 2025). It should be noted that these studies consider different spatial domains and time periods. The estimates obtained using a Q10 value of 1.8 align most closely with this published range among our experiments.

Emissions peaked during the summer months (July–August), with a mean emission ranging from 6.8 to 15.5 TgCH4yr-1 (Fig. 2b). These larger emissions were followed by spring (May–June; range of 3.5–7.8 TgCH4yr-1), autumn (September–October; range of 2.8–7.7 TgCH4yr-1), and winter with the lower emissions (November–April; range of 0.7–2.7 TgCH4yr-1). The timing of the peak in wetland emissions aligns with previous bottom-up estimates (Ying et al., 2025). At the sub-regional scale, emissions showed substantial spatial variability (Fig. 2c). The highest annual mean fluxes were found in western Russia (3.4–8.7 TgCH4yr-1, depending on the parameter set), followed by eastern Canada (3.4–8.2 TgCH4yr-1), eastern Russia (3.1–7.2 TgCH4yr-1), western Canada (1.8–4.4 TgCH4yr-1), Europe including Greenland (1.5–3.4 TgCH4yr-1), and Alaska (0.5–1.6 TgCH4yr-1).

In general, increasing the baseline value of the fCH4 fraction from 0.33 to 0.38 increases CH4 production. However, an increase in the CH4 production Q10 parameter decreases CH4 production for temperatures below 295 K (the reference temperature) and increases it for temperatures higher than 295 K. This means that increasing Q10 values from 1.4 to 2.2 reduces wetland CH4 emissions in the comparatively cold Arctic region (Table 1 and Fig. 2). The sensitivity of wetland CH4 to the Q10 temperature response and the baseline fCH4 fraction is evident when comparing seasonal cycles over the Arctic–Boreal domain (Fig. 2b). For example, contrasting the simulations with baseline fCH4 fraction equaling 0.33 and varying CH4 production Q10 values (from 1.4 to 2.2), shows that increasing Q10 significantly reduces annual wetland mean CH4 emission in this region by ∼54 % (∼17 TgCH4yr-1). This reduction is not uniform throughout the year. Although winter emissions are relatively low, increasing Q10 from 1.4 to 2.2 results in a ∼70 % decrease compared to a ∼50 %–59 % decrease during the summer, spring and fall. Similarly, the influence of the baseline fCH4 fraction can be observed by keeping Q10 constant, for example at 1.4, and varying the baseline fCH4 fraction from 0.33 to 0.38. This increase leads to an increase of up to 6 % in the annual wetland CH4 emissions for the region. In general, our parameter sensitivity tests show that CH4 production Q10 has a stronger effect on emission variability than the baseline fCH4 fraction. These wetland CH4 emission estimates with different parameterizations were subsequently integrated into the Jena CarboScope atmospheric inversion framework as wetland prior fluxes to determine the combination that closest align with atmospheric CH4 observations, which means those requiring the minimum adjustment to fluxes from prior to posterior.

Our nine inverse model estimates produce an annual mean total emission (i.e. including natural and anthropogenic sources) for the Arctic–Boreal region ranging from 44.2 to 47.1 TgCH4yr-1, with wetland emissions being the main CH4 source to the atmosphere. Depending on the parameter set in the prior flux setup by JSBACH, the annual mean wetland emission ranges from 20.9 to 25.0 TgCH4yr-1 (47 %–54 % of total emissions). The largest posterior wetland CH4 emissions were estimated for western Russia (range of 6.9–8.4 TgCH4yr-1, depending on the parameter set), followed by eastern Russia (range of 6.0–7.5 TgCH4yr-1), eastern Canada (range of 4.3–4.9 TgCH4yr-1), western Canada (range of 1.7–1.8 TgCH4yr-1), Alaska (range of 1.0–2.0 TgCH4yr-1) and Europe including Greenland (range of 0.7–0.8 TgCH4yr-1)

At the pan-Arctic scale, posterior wetland fluxes are higher than prior fluxes in the experiments using CH4 production Q10 values of 1.8 (8 %–22 % higher than prior) and 2.2 (37 %–54 % higher), see Table 1 and Fig. 3a. This suggests that these prior estimates underestimate CH4 emissions in the Arctic–Boreal region relative to the observation-constrained posterior fluxes. However, prior fluxes estimated using a Q10 value of 1.4 are higher than posterior fluxes (16 %–25 % higher than posterior), indicating overestimation of CH4 emissions in this case. When comparing the model adjustment for the three experiments (varying only the Q10 parameters), the prior flux using Q10 values of 1.8 produces the best agreement between prior and posterior flux budgets, meaning that a minimum adjustment in the inverse model optimization is required when considering annual mean emissions in the entire Arctic–Boreal region. Additionally, when comparing the different baseline fCH4 fractions (using the Q10 value with the best fit: 1.8), the minimum adjustment in the inverse model optimization is required for the prior flux with the largest baseline fCH4 fraction (0.38), with posterior flux being 8 % (2.0 TgCH4yr-1) higher than the prior.

Our posterior estimates of CH4 emissions from wetlands are similar to previous Arctic–Boreal estimates. Using a process-oriented ecosystem model, Christensen et al. (1996) estimated a total CH4 emissions from northern wetlands and tundra (>50° N) to be 20±13 TgCH4yr-1. Yuan et al. (2024) reported a mean annual emission of 20.3±0.9 TgCH4yr-1 from Boreal–Arctic wetland based on upscaled flux observations for the period 2002–2021. The Global Carbon Project estimated a mean annual wetland (including inland freshwaters) CH4 emission for regions north of 60° N at 24 (9–53) TgCH4yr-1, while top-down approaches resulted in a lower estimate of 9 (7–17) TgCH4yr-1 for the same region (Saunois et al., 2025). Recently, Ying et al. (2025) estimated an annual mean CH4 emissions from vegetated wetlands north of 45° N during 2016–2022 at 22.8±2.4 TgCH4yr-1, ranging from 15.7±1.8 to 51.6±2.2 TgCH4yr-1, depending on the wetland dataset used in the machine-learning-based upscaling approach. Although our posterior estimates are within the range of previous Arctic–Boreal estimates, direct comparisons are difficult because of differences in the study period, methodological approach, and inconsistent or unclear definitions of the spatial domain.

Before analyzing regional differences in optimum CH4 production Q10 settings, we first focused on a clear seasonal pattern in the adjustments between prior and posterior CH4 emissions, which showed a peak of changes occurring during summer. We therefore assessed whether the Q10 value resulting in the minimum adjustment remained constant throughout the year or varied by season. At a pan-Arctic scale, seasonal variations were evident: estimates using CH4 production Q10 equaling 1.8 aligned better with atmospheric observations in spring and fall but substantially underestimated summer emissions (Fig. 3b). In contrast, estimates using a Q10 of 1.4 best aligned with the atmospheric observation during summer, reducing the discrepancy between top-down and bottom-up estimates during the growing season, but strongly overestimating emissions in spring and fall (Fig. 3b). This pattern is primarily driven by wetlands in Russia. Bergman et al. (2000) found temporal variation in Q10 at peatland sites, suggesting that factors such as the availability of easily degradable compounds (e.g., root exudates) and the activity of anaerobic microbial biomass influence CH4 production rates alongside temperature.

CH4 emissions exhibited spatial variability, and model adjustments were not uniform across the domain. This suggests that the optimal parameterization varies by region and seasons (as discussed in Sect. 3.3). In some areas, Q10 values of 1.4 or 2.2 resulted in minimal adjustments (Fig. 3c), outperforming the model using a Q10 equaling 1.8 that was shown to work best as an average setting across the entire domain. To better evaluate this variability and explore ways to reduce uncertainty in specific regions, we assessed the best parameterization fit with observations at the per grid-cell level (Fig. 4).

In our first analysis, we evaluated the spatial best fit model by keeping the baseline constant at a value of 0.35 and varying the CH4 production Q10 values (Fig. 4a). This spatial analysis showed that, in general, in regions with large wetland areas and high annual CH4 emissions (for example the Western Siberian Lowlands, Figs. S1 and S2 in the Supplement) a Q10 value of 1.4 resulted in the smallest model adjustment. As an increase in the Q10 parameter decreases CH4 production for temperatures below 295 K, a higher Q10 value in these regions results in an underestimation of emissions. In contrast, regions such as Europe and northern Canada showed, in general, minimum model adjustments with a Q10 value of 2.2, suggesting that lower Q10 value would overestimate wetland CH4 emissions in these regions. Interestingly, we observed adjustments with different signs in eastern Canada depending on the parameterization. For example, positive adjustments were associated with Q10 value of 2.2, as the prior emissions were underestimated compared with the estimated flux inferred from atmospheric observations. Additionally, we analyzed the effect of varying baseline flux values while keeping Q10 constant as 1.8, which showed that in high-emission areas, for example the Western Siberian Lowlands (Figs. S1 and S2), in general a larger baseline flux value led to the smallest model adjustments (Fig. 4b). When considering the model adjustment for all sensitivity tests (varying both CH4 production Q10 and baseline fCH4 fraction) as shown in Fig. 4c, we also found a consistent pattern that confirmed the above findings varying only single parameters: the combination of higher baseline fluxes and lower Q10 value (Q10=1.4) best captured CH4 dynamics in CH4 hotspots, as the Western Siberia Lowlands.

The wide range of reported incubation-based Q10 values for CH4 production in Arctic and northern wetlands depending on the site, substrate, and season, shows that the temperature sensitivity of CH4 production varies considerably across environments (Bergman et al., 2000; Roy Chowdhury et al., 2015; Treat et al., 2015). This variability, which could be driven by factors such as vegetation type, organic matter quality, and microbial activity, emphasizes the necessity of models to account for spatial differences in process rates. For example, one synthesis study reported a mean Q10 value of 1.18 for CH4 production under Arctic soil conditions (Roy Chowdhury et al., 2015; Treat et al., 2015). Roy Chowdhury et al. (2015) used anoxic laboratory incubations of active layer and permafrost samples from the Barrow Environmental Observatory in Alaska and reported a range of Q10 values from 1.8 to 22. Lupascu et al. (2012) reported that Q10 values describing the CH4 production response of peat to a 10 °C temperature change ranged from 1.9 to 3.5 in sedge sites and from 2.4 to 5.8 in Sphagnum mire sites, and suggested that using spatially variable CH4 production Q10 values could improve the accuracy of CH4 flux modeling in northern wetlands. Furthermore, Bergman et al. (2000) found that the seasonal average Q10 values ranged from approximately 4.6 to 9.2 depending on the plant community of the various peat types. Here, our intent is not to directly compare our results with reported incubation-based values, since our adjustments in the CH4 production Q10 refer to the Q10 of the CH4:CO2 production ratio, as represented in the model, and could not directly be comparable with CH4 production Q10 from the literature review. In JSBACH, the Q10 applied to CH4 production controls the fraction of CH4 generated, but the surface emission ratio may still be lower due to oxidation and transport pathways. Together, these examples highlight that CH4 production is strongly temperature dependent, and that the degree of this dependency can differ across regions and time periods. However, most current models cannot fully capture the influence of these factors due to structural limitations or a lack of detailed input data that is both spatially and temporally resolved. Consequently, these environmental drivers are often oversimplified or overlooked. Adjusting the CH4 production Q10 values, as we do here, offers a useful initial approach, but it should not be seen as a long-term solution. Ideally, future model and data developments will enable CH4 production Q10 values to adjust dynamically in response to underlying biophysical conditions, such as shifts in vegetation or organic matter characteristics. This will allow models to operate with a more generalizable formulation that still captures observed heterogeneity. Recent studies have demonstrated the potential of methane data assimilation techniques to optimize process-model parameters using observational constraints (Bernard et al., 2025; Monteil et al., 2025); however, regional and seasonal optimization remains largely unexplored. While our model experiments identified a single CH4 production Q10 value that best agrees with observations at the pan-Arctic scale, they also showed that CH4 emissions and model adjustments vary regionally. Some areas showed a substantial response to different Q10 values, which further demonstrates that an approach using a single parameter value is not sufficient. This highlights the need for future data assimilation frameworks that allow for regional, and potentially seasonal, parameter optimization.

Our analysis shows that atmospheric inverse modeling is a useful tool for evaluating and guiding process-model parameterizations when estimating wetland CH4 emissions. However, it is important to note the limitations of the top-down approach, especially its relatively coarse spatial resolution. Global inversions usually operate at spatial scales larger than the process models, limiting their ability to resolve fine-scale heterogeneity, local emission hotspots, and small-scale processes. Consequently, grid-cell-level emission estimates represent aggregated signals and cannot fully capture localized variability.

Additionally, top-down estimates rely heavily on the spatial and temporal distribution of atmospheric observations assimilated into the model. Regions with sparce coverage, such as central and eastern Russia, can limit the ability to accurately identify emission sources and increase dependence on prior estimates. Furthermore, most surface observation sites at high latitudes only started providing measurements in the early 2010s. This limits the ability to assess multi-decadal changes in CH4 emissions (Vogt et al., 2025).

At the grid-cell scale, assimilating only atmospheric CH4 observations reflect the combined influence of all sources and sinks, making it difficult to distinguish overlapping source sectors. However, differences in the spatial patterns and seasonality of emissions can be constrained by atmospheric CH4 observations in inversions that solve for different source categories (Saunois et al., 2025). Furthermore, errors in atmospheric transport model can propagate into emission estimates (Houweling et al., 2017; Locatelli et al., 2013; Schuh et al., 2019). Despite these limitations, our approach demonstrated a strong potential to help reduce the discrepancy between bottom-up and top-down estimates, therefore improving the accuracy of wetland CH4 emission estimates.