State of the Art in Monitoring Methane Emissions from Arctic–boreal Wetlands and Lakes

Author(s)
Ali Radman
Celestine Suh
Celtie Ferguson
David Gee
David Risk
Fariba Mohammadimanesh
Garfield Giff
Jean Granger
Jianghua Wu
Martin Lavoie
Masoud Mahdianpari
Matthias Peichl
Mohammad Marjani
Oliver Sonnentag
Peter Morse
Phil Marsh
Resource Date:
2026

Abstract Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region. Keywords: wetlands and lakes; Earth observation; remote sensing; methane monitoring; top-down and bottom-up approaches; Eddy covariance