Anaëlle Tréboutte (CLS, France); Marie-Isabelle Pujol (CLS, France); Gérald Dibarboure (CNES, France)

The recent launch of the new altimetric satellite SWOT (Surface Water and Ocean Topography) was a revolution in oceanography. It can observe ocean dynamics at mesoscale and submesoscale by measuring the Sea Surface Height (SSH) using two KaRIn (Ka-band Radar Interferometer) instruments. It provides two-dimensional measurements of Sea Surface Height (SSH) at high resolution: 2 km and 250 m (also known as Unsmooth product). For each product, SSH field is impacted by a noise that comes from the instrument and is referred to as KaRIn noise. Although the Karin noise is lower than initially expected (Fu et al, 2024), it does present some limitations to the use of the data, particularly when looking at SSH-derived components. A few millimeters of noise can be rapidly amplified by derivatives of order 1 and above, making the result difficult to exploit in terms of physical signal analysis.

Here, we present the denoising method applied to a KaRIn L3 production available on AVISO+. For the 2km product a neural network model based on a U-Net architecture was developed, and it was trained and tested with simulated data in the North Atlantic. The U-Net described in Tréboutte et al. (2023) gives satisfying results on real SWOT data except where the waves heights are important (Dibarboure et al., 2024). Improvements of the U-Net are ongoing and the method was recently consolidated to fix some limitations observed in previous versions. A validation benchmark has also been developed to evaluate the quality of the denoising. The U-Net methodology was also adapted on KaRIn unsmooth production.


REF:


Dibarboure, G., Anadon, C., Briol, F., Cadier, E., Chevrier, R., Delepoulle, A., Faugère, Y., Laloue, A., Morrow, R., Picot, N., Prandi, P., Pujol, M.-I., Raynal, M., Treboutte, A., and Ubelmann, C.: Blending 2D topography images from SWOT into the altimeter constellation with the Level-3 multi-mission DUACS system, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1501, 2024.

Fu, L.‐L., Pavelsky, T., Cretaux, J.‐F., Morrow, R., Farrar, J. T., Vaze, P., et al. (2024). The Surface Water and Ocean Topography Mission: A breakthrough in radar remote sensing of the ocean and land surface water. Geophysical Research Letters, 51, e2023GL107652. https://doi. org/10.1029/2023GL107652

Tréboutte, A., Carli, E., Ballarotta, M., Carpentier, B., Faugère, Y., Dibarboure, G., 2023. KaRIn Noise Reduction Using a Convolutional Neural Network for the SWOT Ocean Products. Remote Sens. 15, 2183. https://doi.org/10.3390/rs15082183