A Dual-Path Recurrent Framework Integrating Optical Flow Guidance and Spatiotemporal-Aware Learning for Sea Surface Temperature Prediction

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Guangwen Peng, Yingbing Liu, Juncheng Luo, Cong Xiao, Wenying Du, Changjiang Xiao

Published: 01 Jan 2025, Last Modified: 23 Mar 2026IEEE Transactions on Geoscience and Remote SensingEveryoneRevisionsCC BY-SA 4.0
Abstract: The accurate prediction of sea surface temperature (SST) is highly important for climate change research and the management of marine ecosystems. Traditional numerical models rely on complex physical processes and precise initial conditions, resulting in high computational costs and limited generalizability. Although deep learning methods can improve the physical consistency and interpretability of ocean processes by incorporating physical constraints, their performance may degrade under anomalous or extreme SST conditions, where rigid constraints limit the model’s adaptability to complex variations. In contrast, incorporating motion-aware mechanisms enables models to flexibly capture dynamic patterns from data, thus increasing their responsiveness to nonstationary processes. Therefore, we propose a novel SST prediction model that integrates optical flow guidance with spatiotemporal awareness, aiming to enhance the modeling of motion and spatiotemporal features in SST evolution. The proposed model consists of three key modules—a spatiotemporal information extraction module (SIEM), a motion trend extraction module (MTEM), and a spatiotemporal feature fusion module (SFFM). First, the SIEM, composed of multiple layers of SwinLSTM, captures spatial and temporal dependencies in the SST time series. The MTEM then estimates the optical flow to extract motion information from the SST data. Finally, the motion information is used to dynamically adjust the spatiotemporal features, which are then fused in the SFFM. To evaluate the performance of the proposed model, we conducted SST prediction experiments at both daily (1–10 days) and weekly (1–10 weeks) scales over the South China Sea (SCS) and the Pacific Ocean, and compared the results with several existing models. The experimental results demonstrate that our model outperforms existing methods across multiple evaluation metrics, with superior prediction accuracy and robustness.