Signal-domain guided deep learning for gap-filling of XCO and XCH 4 : a masked spatio-temporal fusion of TROPOMI and GEOS-Chem (2019–2023)

Abstract

Long-term, high-resolution monitoring of carbon monoxide (CO) and methane (CH 4 ) is essential for understanding their spatiotemporal variability and supporting climate mitigation strategies. However, satellite observations from instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) are often spatially and temporally incomplete, while existing fusion methods still struggle to achieve both high accuracy and spatiotemporal continuity. Here, we propose a signal-domain fusion approach that combines three-dimensional discrete cosine transform (3D DCT) and singular value decomposition (SVD) to integrate TROPOMI retrievals with GEOS-Chem simulations. A lightweight residual U-Net is further employed to refine the initial reconstruction by learning residual fields from GEOS-Chem simulations and DCT/SVD reconstruction outputs, guided by a masked loss. The method generates global 0.25° and China-specific 0.05° daily gap-free XCO and XCH 4 datasets from 2019 to 2023. In the time-series comparison analysis at representative sites, the fused datasets generally follow the temporal variations observed by the Total Carbon Column Observing Network (TCCON) and TROPOMI. Missing-rate-threshold experiments further show that the fused products perform comparably to original TROPOMI retrievals under low and moderate missing-rate conditions and show improved performance under sparse TROPOMI coverage (MR >0.5), with R 2 values of 0.91 for XCO and 0.83 for XCH 4 , along with reduced biases and standard deviations. The fused datasets also capture regional XCO increases in parts of North America, decreases over eastern China, and widespread XCH 4 growth, wildfire-related enhancements in Chongqing in 2022, and short-term variations over rice-growing regions. These results indicate that the proposed framework can reconstruct missing satellite observations with improved continuity and provide useful fused datasets for studying regional variability, event-related enhancements, and atmospheric composition changes. The generated datasets are publicly available at https://doi.org/10.5281/zenodo.22010891 (An et al., 2026).