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.
Dai et al. (Tue,) studied this question.