Preprints
https://doi.org/10.5194/egusphere-2026-1034
https://doi.org/10.5194/egusphere-2026-1034
16 Mar 2026
 | 16 Mar 2026
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Enhanced methane monitoring: A globally harmonized daily 0.1° XCH4 through machine learning-based fusion of GOSAT, GOSAT-2, and TROPOMI

Jebun Naher Keya, Yejin Kim, Hyunyoung Choi, and Jungho Im

Abstract. Accurate global monitoring of atmospheric methane (CH4) is essential for tracking progress toward climate mitigation targets such as the Global Methane Pledge (GMP). Ground-based measurement networks are too sparse to provide sufficient spatial coverage, while satellite-derived retrievals are hindered by systematic biases and uncertainties, limiting their reliability for consistent global monitoring. We present the first global fusion of GOSAT, GOSAT-2, and TROPOMI to generate a globally consistent daily 0.1° land dataset for 2020–2023 for enhanced global XCH4 mapping. The framework employs a three-step machine-learning (ML) approach: (1) sensor-specific bias correction using TCCON observations, (2) cross-sensor harmonization to GOSAT-2, the sensor with the strongest post-correction TCCON agreement, and (3) priority-based fusion. Tree-based ensemble regressors were trained with satellite retrieval parameters to reduce systematic biases and inter-sensor discrepancies. Independent validation at three withheld TCCON stations demonstrates robust generalization of the fused product (R2 = 0.81, RMSE = 10.78 ppb), outperforming standard and operational bias-corrected satellite products and previously reported ML-based approaches. Regional assessments show that fusion substantially improves data availability and reduces systematic errors, delivering up to 12 % relative coverage gains and 33–94 % bias reductions compared to TROPOMI operational products in challenging regions (South Asia, Amazon Basin, Eastern Siberia).The fused dataset reveals intensifying positive XCH4 anomalies (+60 ppb) over South Asia, East Asia, and Central Africa during 2020–2023, linked to MODIS-derived agricultural and urban land classes as well as known oil and gas fields. The dataset provides a scalable resource for regional CH4 emissions assessment and continuous monitoring, with the framework extendable to upcoming satellite missions (GOSAT-GW, CO2M) for long-term GMP progress tracking.

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Jebun Naher Keya, Yejin Kim, Hyunyoung Choi, and Jungho Im

Status: open (until 21 Apr 2026)

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Jebun Naher Keya, Yejin Kim, Hyunyoung Choi, and Jungho Im
Jebun Naher Keya, Yejin Kim, Hyunyoung Choi, and Jungho Im
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Short summary
Monitoring atmospheric methane is essential, yet current satellite observations are limited by measurement errors and incomplete coverage. This study combines three satellite missions using machine learning to generate a daily global 0.1° XCH4 dataset for 2020–2023. The resulting dataset improves coverage in data-sparse regions and reveals intensifying methane concentrations over South Asia, East Asia, and Central Africa, providing a valuable resource for enhanced regional methane monitoring.
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