Multi-satellite U-Net for high-resolution sea surface temperature reconstruction

Abstract. High-resolution sea surface temperature (SST) products are critical for understanding ocean dynamics at submesoscales (less than tens of kilometers) and their influence on upper ocean physics. While modern infrared (IR) radiometers measure SST at high (∼ 1 km) resolution, they cannot image through clouds, resulting in large gaps in remotely sensed SST. In this study, we address the challenge of reconstructing gap-free high-resolution SST by fusing complementary observations across sensors and time using machine learning (ML). We present the Multi-satellite U-Net for SST Estimation (MUSE), a residual U-Net fuses two days (eight 6-hourly snapshots) of multi-satellite IR and microwave (MW) data into cloud-free SST, and further mosaicked into global SST fields. The MUSE model is trained on 9 months of simulated cloudy SST from the MITgcm LLC4320 1/48° SST product, and evaluated on 2 months of held-out LLC4320 data and out-of-distribution Level 3 satellite data. MUSE outperforms single-time and single-satellite baselines across error, correlation and coherence metrics, achieving a global reconstruction error of 0.035 °C on the simulated dataset. On the satellite dataset, MUSE produces results comparable to the state-of-the-art Level 4 MUR 0.01° product. Our results demonstrate the power of ML in synthesizing diverse satellite measurements, each with inherent limitations, into a submesoscale-resolving dataset that enables critical insights into ocean dynamics. Both our data fusion strategy and simulation-to-satellite paradigm can be generalized to other geophysical variables to produce high-resolution, observation-based Earth system fields.