Physics-Inspired Dual-Graph Reasoning for Submesoscale Ocean Super-Resolution

Accurately monitoring submesoscale (kilometer-scale) ocean dynamics is essential for understanding fine-grained energy transport and exchange, yet the high cost of in-situ observations results in limited data availability. Advances in visual super-resolution (SR) enable high-resolution (HR) predictions by refining abundant low-resolution (LR) remote sensing data. However, recent generative SR approaches can introduce visually rich but unsupported details, reducing reconstruction fidelity on fine-scale structures. To address this problem, we emphasize inductive biases inspired by multiscale ocean dynamics and propose a dual-graph reasoning network, namely DRONEThe anonymous code repository can be found at https://anonymous.4open.science/r/DRONE-1E41https://anonymous.4open.science/r/DRONE-1E41.. DRONE combines a heterogeneous channel graph with a spectral-aware pixel graph to improve reconstruction fidelity without imposing explicit physical equations or conservation constraints. The channel graph couples temporal observations with spatial-frequency representations, while the pixel graph allocates connectivity according to local spectral complexity. Extensive experiments on an HR MITgcm ocean simulation dataset demonstrate that DRONE surpasses Transformer-, diffusion-, and GNN-based SR methods on standard fidelity metrics, frontal-structure measurements, and dataset-wide spectral evaluations.