Quantifying sector-resolved methane fluxes in complex emissions environments is challenging yet necessary to improve emissions inventories and guide policy.  Here, we separate energy and agriculture sector emissions using a dynamic linear model analysis of methane, ethane, and ammonia data measured at a Northern Colorado site from November 2021 to January 2022. By combining these sector-apportioned observations with spatially resolved inventories and Bayesian inverse methods, energy and agriculture methane fluxes are optimized across the study’s ~850 km² sensitivity area. Energy sector fluxes are synthesized with previous literature to evaluate trends in energy-sector methane emissions. Optimized agriculture fluxes in the study area were 3× larger than inventory estimates; we demonstrate this discrepancy is consistent with differences in the modeled vs. real-world spatial distribution of agricultural sources. These results highlight how sector-apportioned methane observations can yield multi-sector inventory optimizations in complex environments.