Articles | Volume 18, issue 9
https://doi.org/10.5194/essd-18-6859-2026
https://doi.org/10.5194/essd-18-6859-2026
Data description article
 | 
16 Sep 2026
Data description article |  | 16 Sep 2026

Signal-domain guided deep learning for gap-filling of XCO and XCH4: a masked spatio-temporal fusion of TROPOMI and GEOS-Chem (2019–2023)

Chengkun An, Yuan Tian, Zhiwei Li, Qiaoyu Jiang, Peize Lin, Bowen Chang, Jingkai Xue, and Youwen Sun

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Signal-Domain Guided Deep Learning for Gap-Filling of XCO and XCH₄: A Masked Spatio-Temporal Fusion of TROPOMI and GEOS-Chem (2019–2023) C. An et al. https://doi.org/10.5281/zenodo.22010891

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Short summary
This study addresses the problem of missing carbon monoxide and methane data in satellite observations by integrating satellite observations, atmospheric chemistry simulations, and artificial intelligence techniques to generate complete daily datasets from 2019 to 2023 with a global resolution of 0.25° and a China-specific resolution of 0.05°. The dataset can support research on atmospheric changes, wildfire signals, agricultural regions, and related environmental impacts.
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