Estimation of daily XCO₂ at 1 km resolution in China using a spatiotemporal ResNet model

Carbon dioxide (CO₂) serves as a crucial greenhouse gas that traps heat and regulates the Earth's temperature. High spatiotemporal resolution CO₂ estimation can provide valuable information to understand the characteristics of fine-scale climate change trends and to formulate more effective emission reduction strategies. This study presents a spatiotemporal ResNet model (ST-ResNet) specifically developed to estimate the highest resolution (1 km × 1 km) daily column-averaged dry-air mole fraction of CO₂ (XCO₂) in China from 2015 to 2020. The ST-ResNet model excels in estimating XCO₂ by comprehensively considering the complex relationships between XCO₂ and its various influencing factors, while efficiently capturing both temporal and spatial correlations, thereby demonstrating remarkable generalization capability. The results show that the ST-ResNet generates a highly accurate XCO₂ dataset, outperforming the traditional ResNet. Ground-based validation results further confirm the high accuracy and spatiotemporal resolution of our estimated data product. Using this dataset, the spatial and temporal characteristics of XCO₂ across the entire China and several urban agglomerations have been analyzed. The high spatiotemporal resolution estimated XCO₂ dataset for China is made publicly available at [https://doi.org/10.6084/m9.figshare.25272868], offering substantial potential for fine-scale carbon research.