## 1 Introduction
### Current Status of Multi-Layer Observational Data in the Earth System  
Atmospheric space is key for spacecraft monitoring, where atmospheric processes and astronomical phenomena significantly impact human societal development and daily life. Key physical characteristics of the atmospheric environment include temperature, humidity, wind speed, and air pressure [4]. Ground-based observatories, weather balloons, lidar systems, and meteorological satellites are employed to detect and record parameters such as atmospheric wind fields, temperature fields, and humidity. Near-Earth space, spanning approximately 100 km to 35,786 km above the Earth's surface, is the orbital region for spacecraft. Environmental factors in this domain encompass the ionosphere, magnetic-field parameters, and space radiation. Terrestrial, oceanic, airborne, and satellite-based monitoring networks currently form the primary observational infrastructure for near-Earth space environments [4;5]. Observing and simulating atmospheric and near-Earth space dynamics are vital scientific endeavors, commonly relying on supercomputers, sensor networks, and operational modeling systems to analyze and predict environmental changes. However, the complex three-dimensional structures of atmospheric phenomena, combined with large spatial and temporal data scales, pose significant challenges for precise vertical stratification modeling and computation. A three-dimensional spatial grid covering the atmospheric sphere is essential for organizing atmospheric and near-Earth data and enabling synergistic analysis with surface-layer data [6].  
Since the beginning of the 21 st century, satellite remote sensing has evolved into an integrated space-air-ground observation system, becoming a pivotal tool for surveying, monitoring, analyzing, and forecasting resources, environments, disasters, regions, and urban areas. Advances in Earth observation technologies and computational capabilities have enabled the acquisition of multi-source, multi-dimensional, and massive multi-layer geospatial datasets through remote sensing and numerical modeling. These datasets include surface data (e.g., natural disaster records, land-use patterns), atmospheric layer data (e.g., wind fields, temperature profiles) [6;7], and ionospheric data (e.g., electron density, total electron content), providing a foundational resource for multi-layer Earth-system research [5]. Rapid advancements in space exploration and data acquisition technologies have expanded the scope of spatial phenomena monitoring, achieving increasingly finer granularity in information collection.  
These datasets--encompassing traditional digital maps, remote sensing imagery, and specialized data from meteorology, disaster studies, hydrology, and geophysics--require systematic organization, management, modeling, analysis, and visualization to transform raw data into interpretable spatial information. Such processed information is crucial for analyzing spatial patterns and uncovering underlying principles [8]. However, contemporary human activities exhibit globally distributed, multi-layered, multi-scale, and cross-disciplinary characteristics, necessitating a unified global spatial framework to integrate massive, heterogeneous data across Earth's spheres. Challenges arise from significant disparities in data storage formats, syntactic structures, processing tools, and management protocols across disciplines, creating disciplinary barriers and technical obstacles that hinder interdisciplinary, multi-scale, and cross-layer Earth-system research [9]. Furthermore, the reliance on latitude-longitude grid systems for multi-layer observational data exacerbates structural inequivalence in grid resolution across low-, mid-, and high-latitude regions (particularly at the poles), leading to regionalized model parameters and impeding global-scale or large-scale interaction studies. This limitation becomes even more pronounced in multi-layer research contexts.