Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics. For several decades, the mapping of SSH products at regional and global scales has relied on nadir satellite altimeters, which provide one-dimensional-only along-track satellite observations of the SSH. The Surface Water and Ocean Topography (SWOT) mission deploys a new sensor that acquires for the first time wide-swath two-dimensional observations of the SSH. This provides new means to observe the ocean at previously unresolved spatial scales. A critical challenge for the exploiting of SWOT data is the separation of the SSH from other signals present in the observations. In this paper, we propose a novel learning-based approach for this SWOT calibration problem. It benefits from calibrated nadir altimetry products and a scale-space decomposition adapted to SWOT swath geometry and the structure of the different processes in play. In a supervised setting, our method reaches the state-of-the-art residual error of ~1.4cm while proposing a correction on the entire spectral from 10km to 1000k

翻译：海面高度（SSH）是监测和研究海洋中尺度表面动力学的关键地球物理参数。数十年来，区域和全球尺度的SSH产品制图依赖于星下点卫星测高仪，其仅能提供一维沿轨卫星观测数据。地表水与海洋地形（SWOT）任务部署了一种新型传感器，首次获取了宽幅二维SSH观测信号，为在先前未解决的空间尺度上观测海洋提供了新手段。开发SWOT数据的关键挑战在于将SSH与其他观测信号分离。本文提出了一种基于学习的新型SWOT校准方法，该方法受益于经校准的星下点测高产品以及适应SWOT测幅几何形状与不同过程结构的尺度空间分解。在监督学习框架下，本方法达到了约1.4厘米的最新残差水平，同时实现了对10公里至1000公里全谱段的校正。