Satellite remote sensing and artificial intelligence for livestock greenhouse gas benchmarking: measurement, attribution, and verification challenges

Abstract

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. This review synthesizes recent advances in satellite remote sensing and artificial intelligence (AI) for benchmarking methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O) emissions from livestock systems. Drawing on an 80-study PRISMA synthesis (2019–2025), we evaluate detection capabilities, retrieval uncertainties, and algorithmic limitations that constrain operational deployment. Medium-resolution sensors such as TROPOMI enable regional CH₄ verification but cannot resolve individual farms, while high-resolution systems like GHGSat detect only the largest emitters. AI models achieve strong accuracy for well-characterized systems but degrade substantially when applied to novel practices or sparse datasets. N₂O remains effectively undetectable from space, creating persistent blind spots for 20–40% of livestock GHG footprints. We propose a hybrid multi-scale framework integrating top-down satellite observations, bottom-up process models, and AI-driven fusion to bridge facility-level management zones, satellite footprint scales, and national inventories. This integrated approach could reduce monitoring uncertainties to approximately 15–25%, enabling farm-level benchmarking and independent verification of mitigation actions. Using Canada as a case study, we outline measurement-monitoring-verification requirements necessary for transparent, policy-relevant pathways toward net-zero agriculture by 2050.