Informing landfill emission reporting programs through remote sensing

Landfills are a significant source of anthropogenic methane emissions. Governments and operators rely on models with process and waste characterization assumptions to quantify emissions, assess management strategies like gas collection systems, and set reduction targets. However, validating these models through atmospheric measurements is difficult given the variability of landfill emissions and the challenge of sampling a diverse range of sites. In this study, we use remote sensing observations to guide parameter adjustments in policy-relevant emissions models, reconciling the observed and modeled emission estimates for our observed landfills. We use observations from multi-season campaigns with airborne and satellite remote sensing instruments in California in 2024 and 2025 and the Southeastern United States in 2022. We focus specifically on large municipal solid waste landfills with gas collection systems, as this class of sites have been shown to contribute significantly to net jurisdictional landfill emission inventories. Throughout intensive airborne sampling at California sites in 2024, we quantify variability of site-level emissions throughout the study period, yet do not find a clear seasonal pattern to this variability. We show significant correlation between measured annual gas collection as reported to the United States Greenhouse Gas Reporting Program (GHGRP) and time-averaged emissions estimates derived from airborne remote sensing data. We compare our average observed emissions to the GHGRP’s generation-first and recovery-first landfill emission models used for reporting, finding strong correlation with the recovery-first model. For this model, we find a regionally variable low bias compared to observations, and we show that modeled emissions can be reconciled with observations by making observation-informed adjustments to an existing parameter, gas collection efficiency. We test this reconciliation methodology against varying monitoring schemes using satellite and airborne data. We find that observed and modeled emissions agree when reported collection efficiencies at individual landfills are replaced with optimized regional values (42% for 9 observed landfills in the Southeast, 69% for 22 observed landfills in California). These results demonstrate a potential pathway for top-down and bottom-up landfill emission reconciliation at large landfills, helping to improve reporting and verification programs which are crucial for attributing progress toward sustainability goals.