Generating Daily High-Resolution Regional XCO₂ by Deep Neural Network and Multi-Source Data

CO₂ is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO₂. Currently, commonly used satellites serve as the primary means of CO₂ observation, but they often suffer from striping issues and fail to achieve complete coverage. This paper proposes a method for constructing a comprehensive high-spatiotemporal-resolution XCO₂ dataset based on multiple auxiliary data sources and satellite observations, utilizing multiple simple deep neural network (DNN) models. Global validation results against ground-based TCCON data demonstrate the excellent accuracy of the constructed XCO₂ dataset (R is 0.94, RMSE is 0.98 ppm). Using this method, we analyze the spatiotemporal variations of CO₂ in China and its surroundings (region: 0°–60° N, 70°–140° E) from 2019 to 2020. The gapless and fine-scale CO₂ generation method enhances people’s understanding of CO₂ spatiotemporal variations, supporting carbon-related research.