Satellite-based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, spaceborne altimetry provides valuable measurements of Sea Surface Height (SSH), which is used to estimate surface geostrophic currents. However, due to the sensor technology employed, important gaps occur in SSH observations. Complete SSH maps are produced by the altimetry community using linear Optimal Interpolations (OI) such as the widely-used Data Unification and Altimeter Combination System (DUACS). However, OI is known for producing overly smooth fields and thus misses some mesostructures and eddies. On the other hand, Sea Surface Temperature (SST) products have much higher data coverage and SST is physically linked to geostrophic currents through advection. We design a realistic twin experiment to emulate the satellite observations of SSH and SST to evaluate interpolation methods. We introduce a deep learning network able to use SST information, and a trainable in two settings: one where we have no access to ground truth during training and one where it is accessible. Our investigation involves a comparative analysis of the aforementioned network when trained using either supervised or unsupervised loss functions. We assess the quality of SSH reconstructions and further evaluate the network's performance in terms of eddy detection and physical properties. We find that it is possible, even in an unsupervised setting to use SST to improve reconstruction performance compared to SST-agnostic interpolations. We compare our reconstructions to DUACS's and report a decrease of 41\% in terms of root mean squared error.

翻译：基于卫星的遥感任务深刻革新了我们对海洋状态与动力学的理解。其中，星载测高技术提供了宝贵的海面高度（SSH）测量数据，用于估算地表地转流。然而，受限于所用传感器技术，SSH观测数据存在显著空缺。测高学界利用线性最优插值（OI）方法（如广泛使用的数据统一与高度计组合系统DUACS）生成完整的SSH海图。但OI以产生过度平滑场而著称，导致部分中尺度结构和涡旋信息缺失。另一方面，海面温度（SST）产品具有更高的数据覆盖率，且SST通过平流过程与地转流存在物理关联。我们设计了逼真的孪生实验来模拟SSH与SST的卫星观测，以评估插值方法。引入了一种能够利用SST信息的深度学习网络，该网络可在两种设置下训练：一种是在训练过程中无法获取真实值，另一种则是可获取真实值。本研究对上述网络在采用监督损失函数与无监督损失函数训练时的性能进行了比较分析。我们评估了SSH重建质量，并进一步从涡旋检测能力与物理特性角度评价了网络性能。研究发现，即使在无监督设置下，利用SST信息仍能提升重建性能，优于不依赖SST的插值方法。将重建结果与DUACS对比显示，均方根误差降低了41%。