This study introduces an enhanced version of the High Accuracy Low-Rank Tensor Completion (HaLRTC) algorithm, specifically optimized for reconstructing remote sensing images with significant missing data. Traditional methods, such as geostatistical interpolation, often suffer from high computational complexity and sensitivity to initial assumptions, limiting their effectiveness in large-scale or sparse datasets. Our improved HaLRTC method leverages the intrinsic low-rank structure of spatiotemporal image data, restructuring image time-series into a third-order tensor format to achieve more accurate and efficient pixel reconstruction. Through systematic experiments, our approach demonstrates superior performance across diverse missing data patterns and ratios, highlighting its robustness and efficacy. This enhanced HaLRTC algorithm offers valuable technical support for improving image recovery and analysis in remote sensing applications, including environmental monitoring, land-use classification, and disaster assessment, contributing to the development of more efficient and reliable methods for handling incomplete remote sensing imagery.