Sea Ice classification in SWOT L3 products

The launch of the altimetric satellite SWOT (Surface Water and Ocean Topography) was a revolution in oceanography and hydrology. With its120 km swath width, a spatial resolution of 500m² (in Low Resolution acquisition mode) and an instrumental random error significantly lower than the one from nadir altimetry, the Ka-Band Radar Interferometer (KaRIn) onboard SWOT mission also present a huge potential to develop applications in the polar regions. Indeed, the SWOT product  enables the observation of leads, icebergs and polynyas (Dibarboure and al 2024) through the measures of surface topography and backscatter coefficient.

 

The surface discrimination between leads and floes is the first step toward polar ocean monitoring, ice thickness and snow depth estimations. However because of the complexity of the surface (different surface roughness properties in the leads, presence of melt pounds) added to residual sensing errors (residual KaRIn random error, residual systematic errors, …) this first required achieved is not straightforward. Therefore several classification approaches were developed : one based on a statistical method (Markov Random Field), one based on an unsupervised machine learning method (Kmeans) and another one based on a supervised machine learning method (XGBoost). The objective of this paper is thus to present the results of these methods (their robustness, strengths and weaknesses) through local comparisons with respect to optical, SAR images and global comparison with existing state of the art products (OSISAF ice concentration products).