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 degrees-60 degrees N, 70 degrees-140 degrees 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.