Inferring methane emissions from African livestock by fusing drone, tower, and satellite data
Abstract. Considerable uncertainties and unknowns remain in the regional mapping of methane sources, especially in the extensive agricultural areas of Africa. To address this issue, we developed an observing system that estimates methane emission rates by assimilating drone and flux tower observations into an atmospheric dispersion model. In this study, we apply this approach to verify and quantify potential methane sources identified through radiance anomalies observed in hyperspectral satellite data. We compare different methods to estimate emissions from various ruminant livestock species in sub-Saharan Africa, including diverse herds of cattle, goats, and sheep, as well as camels, for which methane emission estimates are particularly sparse. Our estimates, derived from Bayesian inference, align with Tier 2 emission values of the Intergovernmental Panel on Climate Change. We moreover observe the hypothesized increase in methane emissions following feeding. Our findings suggest that the Bayesian inference method is more robust under non-stationary wind conditions compared to a mass balance approach using drone observations. Furthermore, the Bayesian inference method performs better in quantifying emissions from weaker sources, estimating methane emission rates as low as 100 g h-1. We find a ± 50 % uncertainty in emission rate estimates for these weaker sources, such as sheep and goat herds, which reduces to ± 12 % for stronger sources, like cattle herds emitting 1,000–1,500 g h-1. These promising results demonstrate the potential and efficacy of the Bayesian inference method for source term estimation. Future applications of drone-based Bayesian inference could extend to estimating methane emissions in Africa and other regions from various sources with complex spatiotemporal emission patterns, such as wetlands, landfills, and wastewater disposal sites. The Bayesian observing system could thereby contribute to the validation and improvement of climate models and emission inventories.