## 1 Introduction
### Current Research Status of Earth Spatial Grid  
Spatial grids are categorized into Planar Spatial Grids (PSGs) and Global Spatial Grids (GSGs). PSGs partition planar spaces based on Euclidean geometry, with common types including quadtree grids, finite-element grids, square grids, and Triangulated Irregular Networks (TINs). GSGs, on the other hand, divide global spaces based on spherical or spheroidal manifolds, further classified into spherical GSGs (2D manifolds) and spheroidal GSGs (3D manifolds). Discrete Global Grid Systems (DGGS) uniformly discretize Earth's space into seamless, non-overlapping multi-resolution hierarchical grids. These systems inherently support the unified modeling of multi-layer Earth observation data, enabling multi-scale, multi-resolution, and multi-level data integration while offering flexibility in representing vertical elevation information in near-Earth space [10; 11; 12]. Additionally, the homogeneous structure of DGGS enhances the generalization and adaptability of multi-layer analytical models at global scales, and their discrete data organization and encoding-based operations align well with cloud computing and big data environments [13]. [PERSON] et al. proposed that universal sphere GSG oriented to the Earth system can be called the Earth-System Spatial Grid (ESSG) [6]. The classification system of global spatial grid is shown in Figure 1.  
Spherical spatial grids (or Discrete Global Grids, DGGs) typically generate discrete units through the recursive subdivision of (ellipsoidal) spheres to support multi-scale data organization and modeling [14]. Existing DGGs can be divided into two categories:  
1. Latitude-Longitude Grids  
These grids partition the sphere using latitude and longitude lines, divided into equidistant and variable-interval types. Equidistant grids (e.g., GTOPO30 [15], ETOPO5 [16]) are structurally simple but suffer from high-latitude grid distortion (quadrilaterals \(\rightarrow\) rhombuses \(\rightarrow\) triangles) [17], leading to data redundancy and reduced accuracy. Variable-interval grids (e.g., GTED, Spherical Degenerated Quadtree [18]) mitigate distortion by adjusting latitude band intervals but increase model complexity. 2. Regular Polyhedron-Based Grids  
Figure 1: Classification system of global spatial grid. These grids use regular polyhedrons (tetrahedrons, hexahedrons, octahedrons, etc.) as base surfaces, constructing approximately uniform grids through subdivision and projection. Regular polyhedron-based grids, summarized in Table 1, include triangular, quadrilateral, and hexagonal types, each with distinct advantages and limitations. Because of their different division and coding structure, these grids have different characteristics in application. For example, H3, developed by Uber (2018) [23], leverages a seven-aperture hexagonal structure, offering high computational efficiency with encoding requiring approximately 32 bits per cell and fast retrieval times due to its hierarchical indexing. However, its focus on surface-level applications limits its vertical stratification capabilities for 3D modeling. In contrast, rHEALPix ([PERSON] 2016 [21]) provides equal-area pixelization with adjustable resolution, excelling in global environmental sampling, but its complex projection adjustments increase the computational overhead by 15-20% compared to simpler grids like latitude-longitude systems ([PERSON] et al. 2019 [17]). Real-world applications, such as H3's use in ride-sharing optimization, prioritize speed over multi-layer integration, while rHEALPix supports climate modeling but struggles with dynamic vertical data fusion.\" These comparisons are grounded in published documentation (Uber 2018 [23]; [PERSON] 2016 [21]) and our analysis of their performance metrics. However, existing spherical-grid models primarily focus on Earth's surface and lack rigorous grid-based modeling for the vertical 3D space (near-Earth space) along Earth's diameter, making it challenging to scientifically describe 3D spatial entities, processes, and information.