Enhancing two-dimensional SWOT oceanic measurements using deep learning approaches for denoising and inpainting

The SWOT (Surface Water Ocean Topography) mission is currently providing unpreceded high-resolution measurements of Sea Surface Height (SSH), revealing ocean features at finer scales. Nevertheless, the two-dimensional observations of KaRIn altimeter of SWOT suffer from instrumental and geophysical correction errors. This noise degradation is polluting the high frequencies of SWOT signal, thus hiding the submesoscale dynamics from oceanographers. For this reason, Tréboutte et al. (2023) has developed a convolutional neural network (CNN) based on UNet architecture to separate the noise from the physical signals contained in the SSH. This work has already demonstrated great results on SWOT measurements. However, last version of the algorithm delivers poor performance in certain oceanic conditions. Therefore, we modify the training procedure to obtain a more robust version of the algorithm. We show that we manage to mitigate these issues significantly, avoiding biases and artefacts in the denoised observations.

This data is also incomplete. SWOT measurements are sometimes distorted by various factors, such as rain cells, boats, icebergs, etc. To address these errors, editing is applied to remove erroneous pixels from the data. However, this lost data is valuable to many users. That is why we have also developed a deep learning inpainting methodology using a CNN to retrieve the missing physical information. We demonstrate that it is possible to accurately restore measurements lost after the editing step, better than classical interpolation approaches.