离散全球网格系统（Discrete Global Grid Systems, DGGSs）通过将地球表面分层划分为一组离散单元，为地理空间数据的基于单元的索引、融合与多分辨率处理提供了全球一致的结构。当前将遥感影像映射至DGGS的方法主要依赖于对已正射校正产品的重采样，这导致流程冗余与信息损失，进而降低数据入库效率并削弱精度。本研究提出一种基于有理函数模型的、从1级影像直接正射校正至DGGS的算法；同时改进多面体等面积投影以处理跨面成像情形，并开发了从源数据直接生成DGGS正射校正产品的关键技术。以菱形三十面体‘切片-拼接’等面积六边形网格系统作为典型DGGS实现方案，利用高分一号（Gaofen-1）与高分二号（Gaofen-2）卫星影像开展实验。结果表明，在最近邻插值方法下，所提方法相较传统方法所得1级参考数据，均方误差降低52.98–59.25%，结构相似性指数提升33.53–101.47%，显著提高了格网化产品的辐射精度。该方法为DGGS内组织分析就绪数据（analysis-ready data）建立了新范式。

Discrete Global Grid Systems (DGGSs) utilize a hierarchical partition of the Earth’s surface into a set of discrete cells, providing a globally consistent structure for cell-based indexing, fusion, and multi-resolution processing of geospatial data. Current approaches to mapping remotely sensed imagery to DGGSs predominantly rely on resampling orthorectified products. This introduces procedural redundancy and information loss, resulting in slower data ingestion and reduced accuracy. In this study, we propose a direct orthorectification method from level-1 imagery to DGGSs based on a rational function model. We also enhance the polyhedral equal-area projection to address cross-face imaging cases, and develop a key technology for generating DGGS-orthorectified products directly from source data. Using a rhombic triacontahedron ‘slice-and-dice’ equal-area hexagonal grid system as a representative DGGS implementation, we conduct experiments using Gaofen-1 and Gaofen-2 satellite imagery. Our results demonstrate that, under the nearest-neighbor interpolation method, the proposed approach reduces mean squared error by 52.98-59.25% and improves the structural similarity index by 33.53-101.47% relative to level-1 reference data obtained with conventional methods, thereby significantly enhancing the radiometric accuracy of gridded products. This approach establishes a novel paradigm for organizing analysis-ready data within DGGSs.