Data-Driven Reconstruction of Sea Surface Turbidity Dynamics with 4dVarNet Neural Scheme Applied To Gappy Satellite Images

Optical remote sensing is increasingly used to assess various sea surface biogeochemical parameters (e.g., Chl-a [1] , turbidity [2] ). If today's systems offer a better spatiotemporal coverage, it still depends on both the satellite revisit period and the cloud cover at the time of the acquisition. The resulting sea surface observations generally present large proportions of missing data, limiting their use. Typically, in our experiments, we worked with datasets containing up to 98% of missing data.