Global estimates of gap-free and fine-scale CO2 concentrations during 2014-2020 from satellite and reanalysis data

Abstract

Carbon dioxide (CO₂) is a crucial greenhouse gas with substantial effects on climate change. Satellite-based remote sensing is a commonly used approach to detect CO₂ with high precision but often suffers from extensive spatial gaps. Thus, the limited availability of data makes global carbon stocktaking challenging. In this paper, a global gap-free column-averaged dry-air mole fraction of CO₂ (XCO₂) dataset with a high spatial resolution of 0.1° from 2014 to 2020 is generated by the deep learning-based multisource data fusion, including satellite and reanalyzed XCO₂ products, satellite vegetation index data, and meteorological data. Results indicate a high accuracy for 10-fold cross-validation (R² = 0.959 and RMSE = 1.068 ppm) and ground-based validation (R² = 0.964 and RMSE = 1.010 ppm). Our dataset has the advantages of high accuracy and fine spatial resolution compared with the XCO₂ reanalysis data as well as that generated from other studies. Based on the dataset, our analysis reveals interesting findings regarding the spatiotemporal pattern of CO₂ over the globe and the national-level growth rates of CO₂. This gap-free and fine-scale dataset has the potential to provide support for understanding the global carbon cycle and making carbon reduction policy, and it can be freely accessed at https://doi.org/10.5281/zenodo.7721945.

Keywords: CO(2); Deep learning; Gap-free; Global coverage; Growth rate.