The Improved MNSPI Method for MODIS Surface Reflectance Data Small-Area Restoration

Low-resolution satellites, due to their wide coverage and fast data acquisition, are commonly used in large-scale studies. However, these optical remote sensing data are often limited by weather conditions and sensor system issues during acquisition, which leads to missing information. For example, MODIS data, as a typical representative of low-resolution satellites, often encounter issues of small-region data loss, which corresponds to a large area on the surface of the earth due to the relatively large spatial scale of the pixels, thereby limiting the high-quality application of the data, especially in building datasets for deep learning. Currently, most missing data restoration methods are designed for medium-resolution data. However, low-resolution satellite data pose greater challenges due to the severe mixed-pixel problem and loss of texture features, leading to suboptimal restoration results. Even MNSPI, a typical method for restoring missing data based on similar pixels, is not exempt from these limitations. Therefore, this study integrates four-temporal phase characteristic information into the existing MNSPI algorithm. By comprehensively utilizing temporal–spatial–spectral information, we propose an algorithm for restoring small missing regions. Experiments were conducted under two scenarios: areas with complex surface types and areas with homogeneous surface types. Both simulated and real missing data cases were tested. The results demonstrate that the proposed algorithm outperforms the comparison methods across all evaluation metrics. Notably, we statistically analyzed the optimal restoration range of the algorithm in cases where similar pixels were identified. Specifically, the algorithm performs optimally when restoring regions with connected pixel areas smaller than 1936 pixels, corresponding to approximately 484 km2 of missing surface area. Additionally, we applied the proposed algorithm to global surface reflectance data restoration, further validating its practicality and feasibility for large-scale application studies.