## 5 Discussion  
### Advantages of Grid-Based Block Partitioning  
In the grid-based block-partitioning strategy, the selection of the sub-block size and overlap degree is essentially a dynamic balancing process between memory efficiency, computational accuracy, and processing time. Smaller sub-block sizes significantly reduce the memory usage by decreasing the number of PS points processed at a time. Meanwhile, the increased local network connection density (more redundant arcs) enhances the accuracy and robustness of the adjustment calculation. A higher overlap degree enlarges the interaction area between adjacent sub-blocks, which not only improves the connectivity (ensuring sufficient common points in the overlap area) but also increases the observation redundancy for the sub-block adjustment calculations, thereby mitigating error propagation. As demonstrated in Section 4.1, the selected partitioning parameters (grid size of 1200 \(\times\) 1200 pixels, overlap of 300 pixels) produced 210 sub-blocks with a total of 23,837,354 PS points. After de-duplication in the overlapping regions, 13,867,836 points were retained, indicating that \(\
eg\)9.97 million points (42% of the total) resided in overlapped areas. This highlights the dual effect of a higher overlap: redundant observations for a robust adjustment (elevating solution precision) versus an increased computational load from duplicate processing. Critically, these parameters (sub-block size, overlap degree, sliding step) primarily influence the memory management and connectivity during stitching but do not inherently alter the deformation or elevation solutions at individual PS points. The core PSInSAR inversion (e.g., phase unwrapping, weighted least squares) operates identically within each sub-block, and systematic biases introduced by partitioning are corrected during the global adjustment (Section 2.3). As demonstrated in Section 4.2, the deformation rates and elevation residuals from the proposed method exhibited near-perfect consistency with traditional global PSInSAR results (correlation coefficient \(\geq\) 0.98, standard deviation \(\leq\) 0.48 mm/y for deformation, \(\leq\)3.38 m for elevation residuals; Table 3). This confirms that the parameter choices affected the computational workflow rather than solution fidelity. However, this optimization came at the cost of increased repeated processing--smaller sub-blocks led to a linear increase in the number of blocks, and a higher overlap degree led to exponentially increased duplicate computation. Therefore, partitioning parameters need to be carefully balanced based on hardware resources and task objectives. The grid-based partitioning was the key preliminary step for the block-based PSInSAR processing in this study. Its core advantage lies in the strict geometric structure and deterministic spatial logic. The precise correspondence between sub-block row/column indices and geographic coordinates ensures that the block positions remain consistent in the global coordinate system, avoiding the need for dynamic spatial index reconstruction, as required by irregular block partitioning. This significantly simplifies the retrieval of overlapping regions and the matching of common points, reducing the complexity of the process. The parameterized nature of the grid allows for critical parameters, such as the sub-block size, overlap degree, and sliding step size, to be pre-defined through mathematical formulas. Combined with the boundary adjustment mechanism, this approach achieves fully automated seamless coverage of the data without relying on prior knowledge of PS point distribution, ensuring both efficiency and robustness in the partitioning process. The spatial symmetry between blocks further optimizes the subsequent adjustment process: the uniformly distributed overlap areas not only guarantee redundancy in common point observations but also give the coefficient matrix a structured sparse feature, greatly improving the efficiency of large-scale matrix computations.