# XCO\({}_{2}\) Super-Resolution Reconstruction Based on Spatial Extreme Random Trees
## 1 Introduction
They carry high-resolution infrared absorption spectrometers to measure the absorption spectra of CO2 in the atmosphere, providing high spatiotemporal resolution data on global CO2 column concentrations [30]. In addition, TanSat is a CO2 monitoring satellite launched by China, which utilizes an infrared absorption spectrometer for global CO2 monitoring [31]. The CO2 Monitoring Mission planned by the European Space Agency also aims to provide high-quality CO2 observation data [32]. These satellites, using techniques such as infrared absorption spectroscopy and microwave radiation, have achieved global coverage and relatively high spatiotemporal resolution monitoring, providing a deeper understanding of the spatiotemporal distribution of CO2 in the atmosphere and its impact on climate change. However, due to limitations in satellite operational cycles, they cannot provide real-time and continuous observational data. Furthermore, although the spatiotemporal resolution of the products has improved compared to previous versions, it still cannot meet the demands of small- to medium-scale carbon environmental monitoring. Obtaining long-term and high-resolution data on columnar carbon dioxide (CO2) concentration is crucial for carbon emission monitoring and related research at small to medium scales [33]. However, currently, remote sensing monitoring data with high-precision spatiotemporal resolution are not available. In order to obtain high-precision, high-coverage, and high spatiotemporal resolution CO2 data products, numerous scholars have conducted research on the high-resolution reconstruction of CO2 column concentration (XCO2) using methods such as physical data fusion [34], interpolation, and geostatistics [35; 36; 37; 38]. These interpolation and geostatistical methods model the correlation between known CO2 points to estimate the concentration at unknown points, providing powerful tools for understanding the spatiotemporal distribution of CO2. Among them, Kriging interpolation, as a classic geostatistical method, is widely used for estimating spatially continuous sur faces [39; 40; 41]. Spatial regression analysis combines statistical regression methods with geographic information, thus effectively considering spatial correlations, and provides a more accurate model for interpolation. Additionally, geostatistical cluster analysis divides the study area into spatial units with similar CO\({}_{2}\) concentration characteristics, revealing regional differences in the concentration distribution and providing a reference for regional CO\({}_{2}\) management. Despite the significant progress made as a result of these methods in CO\({}_{2}\) concentration research, they still present some challenges [42]. Geostatistical methods, represented by Kriging interpolation, are sensitive to small-scale spatial changes and cannot effectively handle unstable datasets. They have poor capability in handling the spatial variability of data and can only model and interpolate a single variable, which may ignore significant spatiotemporal correlations between XCO\({}_{2}\) and its influencing factors. Existing statistical models cannot effectively predict nonlinear relationships and are inefficient and less accurate in computing large amounts of data. Machine learning methods excel at capturing the intricate nonlinear relationships among multiple data sources and efficiently handling large-scale datasets. Therefore, researchers utilize machine learning methods such as artificial neural networks and support vector machines to detect atmospheric pollutants like CO\({}_{2}\) and PM\({}_{2.5}\)[43; 44; 45]. However, these methods also have their limitations. For instance, parameter tuning in support vector machines significantly affects model performance and requires careful optimization in experiments, while artificial neural network models typically require a large amount of sample data for training.