A Decade of Deep Learning for Remote Sensing Spatiotemporal Fusion: Advances, Challenges, and Opportunities

Hardware limitations and satellite launch costs make direct acquisition of high temporal-spatial resolution remote sensing imagery challenging. Remote sensing spatiotemporal fusion (STF) technology addresses this problem by merging high temporal but low spatial resolution imagery with high spatial but low temporal resolution imagery to efficiently generate high spatiotemporal resolution satellite images. STF provides unprecedented observational capabilities for land surface change monitoring, agricultural management, and environmental research. Deep learning (DL) methods have revolutionized the remote sensing spatiotemporal fusion field over the past decade through powerful automatic feature extraction and nonlinear modeling capabilities, significantly outperforming traditional methods in handling complex spatiotemporal data. Despite the rapid development of DL-based remote sensing STF, the community lacks a systematic review of this quickly evolving field. This paper comprehensively reviews DL developments in remote sensing STF over the last decade, analyzing key research trends, method classifications, commonly used datasets, and evaluation metrics. It discusses major challenges in existing research and identifies promising future research directions as references for researchers in this field to inspire new ideas. The specific models, datasets, and other information mentioned in this article have been collected in: https://github.com/yc-cui/Deep-Learning-Spatiotemporal-Fusion-Survey.