# XCO\({}_{2}\) Super-Resolution Reconstruction Based on Spatial Extreme Random Trees
## 1 Introduction  
In recent years, global climate change has become increasingly significant. As carbon dioxide (CO\({}_{2}\)) is one of the most harmful greenhouse gases [1, 2], CO\({}_{2}\) emissions have become a major factor driving changes in the Earth's climate system, profoundly impacting its climate. Since the Industrial Revolution, significant amounts of CO\({}_{2}\) have been emitted due to the development of heavy industry and the combustion of large quantities of fossil fuels [3, 4], which has caused serious harm to the Earth's environment and human society [5] and resulted in global warming and an increase in extreme weather events [6, 7, 8]. Therefore, scholars have extensively explored methods to reduce CO\({}_{2}\) emissions [9, 10, 11, 12], including the development of renewable energy technologies, improving energy efficiency, researching carbon capture and storage methods, and promoting ecosystem restoration. Additionally, recent studies on carbon dioxide (CO\({}_{2}\)) emissions assessment have garnered attention [13, 14, 15]. These studies evaluate the effectiveness of emission reduction policies by examining CO\({}_{2}\) emissions, focusing not only on the technical aspects but also revealing their role in policy implementation and their association with sustainable chemistry. Despite these efforts, global average CO\({}_{2}\) concentrations have continued to increase in recent years. In response to this situation, many governments have enacted greenhouse gas reduction policies and targets [16; 17], striving to limit CO2 concentrations to certain levels. China has also made significant strategic decisions to achieve carbon peaking by 2030 and carbon neutrality by 2060. Therefore, to ensure the realization of these \"dual carbon goals\" and effectively reduce CO2 emissions, it is necessary and urgent to accurately understand the spatiotemporal distribution characteristics and trends of CO2 concentrations. A surface carbon dioxide monitoring network is a system composed of various ground stations, which is used to monitor the concentration of carbon dioxide (CO2) in the atmosphere [18]. Examples of such networks include the Global Atmosphere Watch (GAW) led by the World Meteorological Organization (WMO), the Total Carbon Column Observing Network (TCCON) consisting of multiple ground stations worldwide [19; 20], the Integrated Carbon Observation System (ICOS) established through collaboration among multiple European countries [21], and the atmospheric monitoring networks under the National Oceanic and Atmospheric Administration (NOAA)'s Earth System Research Laboratory (ESRL), among others. These networks, distributed across multiple ground stations worldwide, monitor the atmospheric CO2 concentration, providing valuable data on the spatiotemporal distribution of CO2, which helps improve our understanding of the climate system and carbon cycle. Despite providing important observational data for a period of time, surface CO2 monitoring networks still have some limitations [22]. For example, the spatial distribution of surface monitoring stations is often sparse and uneven, and the temporal and spatial resolution of the products is low, meaning they are unable to provide high-precision data for the continuous monitoring of atmospheric CO2 concentrations. This limitation restricts the usability of the data for widespread scientific research. Satellite remote sensing offers advantages such as wide coverage and long time series, which address the shortcomings of ground-based carbon dioxide monitoring. The development of this technology has provided an important method for accurately monitoring columnar carbon dioxide concentration (XCO2) and studying the carbon cycle, thereby offering richer data support for global carbon dioxide research [23; 24; 25; 26]. OCO-2 (Orbiting Carbon Observatory-2) and GOSAT (Greenhouse Gases Observing Satellite), launched by NASA and JAXA, respectively, are two of the main satellites providing remote sensing data [27; 28; 29].