Abstract: Objectives: Spatiotemporal fusion technology enables the reconstruction of high spatiotemporal resolution images by leveraging the complementary spatiotemporal information of multisource optical images, offering cost-effective and flexible solutions that have garnered widespread attention. Among these, the reliable and adaptive spatiotemporal data fusion method (RASDF) has gained recognition for its robustness in handling discrepancies between heterogeneous images and its ability to reconstruct surface information under drastic change scenarios. However, RASDF is hindered by inefficiencies and blurred boundary information in fusion results. To address these limitations, this study introduces an enhanced RASDF method based on an objectoriented strategy, termed object level RASDF (OL-RASDF). Methods: By incorporating multi-scale image segmentation techniques and assuming consistent spatiotemporal variation patterns within segmented objects, OL-RASDF replaces the original pixel-level fusion steps, which rely on a moving window strategy, with objectlevel fusion steps that use image segments as the basic processing units. This approach streamlines the fusion process and enhances the preservation of boundary information. Results: Experimental results from two regions demonstrate that OL-RASDF achieves the highest fusion accuracy, with the sharpest boundary details and a significant improvement in efficiency. The average processing time in the two study areas was reduced by 62.1%. Conclusions: The results indicate that OL-RASDF is highly applicable for long-term and large-scale image fusion tasks, offering a superior balance of accuracy and efficiency.