# XCO\({}_{2}\) Super-Resolution Reconstruction Based on Spatial Extreme Random Trees
## 2 Materials and Methods
### Influencing Factors  
Studies have shown that there are many factors that influence carbon dioxide concentration, among which population density (POP), temperature (TEM), normalized difference vegetation index (NDVI), precipitation (PRE), and gross primary productivity (GPP) are important factors affecting columnar carbon dioxide concentration (XCO\({}_{2}\)) [55; 56]. In this study, we classified influencing factors into three categories: human activities, vegetation, and meteorological factors [57]. Areas with higher population density exhibit significantly higher carbon dioxide concentrations, as the distribution of emission sources such as industry and transportation directly affects carbon dioxide concentrations [58; 59]. Vegetation conditions directly affect the absorption of carbon dioxide by vegetation and also influence the exchange processes between vegetation and the atmosphere. Meteorological factors such as temperature and precipitation also have significant effects on carbon dioxide concentrations. Therefore, in this study, factors related to human activities included population density (POP) and nighttime light index (NTL), vegetation conditions include gross primary productivity (GPP) and normalized difference vegetation index (NDVI), and meteorological conditions included temperature (TEM), precipitation (PRE), humidity (RH), and wind speed (WND). Additionally, we considered the influence of topography on carbon dioxide column concentration and incorporated digital elevation models (DEMs) for modeling purposes. Population density has a significant impact on carbon dioxide (CO\({}_{2}\)) concentration. Regions with high population density are often associated with increased urbanization, industrialization, and commercial activities, which may lead to higher energy consumption and industrial emissions, thus increasing CO\({}_{2}\) emissions [60; 61]. The LandScan dataset used in this study is developed and maintained by the Oak Ridge National Laboratory (ORNL) of the Department of Energy (DOE) in the United States. It combines high-resolution satellite imagery, census data, geographic information system (GIS) data, and other geographical, topographical, and meteorological data, generated through complex models and algorithms, with a spatial resolution of 1 km [62]. The nighttime light (NTL) index is typically calculated using nighttime light images obtained from remote sensing satellites [63]. Its primary applications include urban planning, environmental monitoring, and economic development research. By measuring and recording the brightness of the Earth's surface, nighttime light images can reveal the population density, level of economic activity, and urban development of an area. A high NTL index may indicate a higher level of economic development in the area, accompanied by increased energy use and CO\({}_{2}\) emissions [64]. The NTL data used in this study were obtained from the VIIRS sensor on the Suomi NPP satellite and processed to eliminate noise from different dimensions to obtain the final dataset. NDVI can accurately reflect the vegetation cover status of the Earth's surface, and previous studies have shown a significant negative correlation between NDVI and carbon dioxide concentration [65]. We utilized an annual vegetation index dataset based on uninterrupted time series derived from SPOT/VEGFATION NDVI satellite remote sensing, with a spatial resolution of 1 km [66]. The NDVI data were sourced from the Resource and Environmental Science Data Center [67]. Gross primary productivity (GPP) is commonly used to describe the total amount of photosynthesis performed by plants in terrestrial ecosystems. Photosynthesis is the process by which plants utilize sunlight to convert carbon dioxide and water into organic substances. This process enables plants to capture and store carbon dioxide from the atmosphere, making it crucial for the carbon cycle in the atmosphere.