Livestock agriculture contributes 14.5% of global anthropogenic greenhouse gas (GHG) emissions, yet current monitoring approaches carry 30–50% uncertainty, limiting credible mitigation assessment and carbon-market verification.

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.

Satellite Earth observation provides spatially continuous measurements of environmental drivers governing nitrous oxide (N₂O) emissions from agricultural soils, yet the extent to which these observations constrain emission variability remains unresolved. In particular, it is unclear whether satellite-derived signals can explain the order-of-magnitude variability observed in local emission factors associated with livestock manure under heterogeneous soil and climatic conditions. Here, we develop a satellite-constrained, semi-mechanistic emission factor (EF) framework that integrates multi-sensor observations of soil moisture, thermal regimes, vegetation dynamics, and precipitation within a multiplicative formulation grounded in soil biogeochemical processes. Applied across Canada’s agricultural domain at 500 m resolution, the framework estimates national livestock N₂O emissions in 2025 at 33.40 Gg yr⁻¹ (90% CI: 29.8–40.8 Gg), representing a 12.8% increase relative to the IPCC Tier 1 default. Provincial deviations span from +45.8% in Québec to -72.5% in Newfoundland, reflecting strong spatial heterogeneity in environmental controls. Validation against independent chamber measurements across eight provinces yields a correlation of 0.755, an RMSE of 0.49%, and negligible bias. Variance decomposition identifies satellite-derived soil moisture as the dominant independent driver of emission variability, exceeding the influence of temperature and vegetation, while interaction effects reveal transient, non-linear responses that govern episodic fluxes. Incorporating satellite-constrained inputs reduces unexplained variance relative to static EF approaches, although limitations in temporal sampling and surface heterogeneity restrict the representation of short-duration emission pulses. These results demonstrate that Earth observation can directly constrain key environmental controls on N₂O emissions, providing a scalable, measurement-informed pathway for spatially explicit greenhouse gas estimation.