SirP: Semi-Recurrent Paradigm-Based Prediction Network for Satellite Image Sequences

Satellite image sequence prediction, which supports critical applications in disaster prevention and environmental monitoring, remains challenging due to the detail-rich and long-range chaotic nature of satellite data. Existing data-driven paradigms—whether recurrent or nonrecurrent—struggle to effectively capture both short- and long-term spatiotemporal dynamics. To address this, we design a novel semi-recurrent paradigm that combines parallel input encoding with a recurrent, progressive output mechanism, enabling robust reasoning and prediction for both short and long sequences. In addition, we propose SirP, a new prediction network featuring three key innovations specifically designed for the semi-recurrent paradigm and the characteristics of satellite imagery: a TVAM, an accumulated-and-current feature-based progressive predictor, and a nearest-frame skip connection. Comprehensive experiments on five datasets constructed from Himawari-8, FY-4B, and GOES-16 satellite observations, covering the coastal East Asia and North American continental regions, show that SirP consistently outperforms state-of-the-art methods from both recurrent and nonrecurrent paradigms across pixel-level and event-level metrics, with greater gains as the prediction horizon extends. Detailed ablation studies confirm the effectiveness of each core component. The code will be made publicly available.