Reconstruction of Sea Surface Temperature Under Clouds Using Masked Autoencoders

This paper presents a methodology for reconstructing high- spatial-resolution sea surface temperature (SST) fields under cloud cover using masked autoencoders (MAE). The MAE model is trained on high-resolution SST maps from the ECCO forward simulation, LLC4320, and reconstructs missing data by masking out a portion of the input pixels. The impact of masking ratios and methods, as well as network architecture variations, is investigated. Preliminary results demonstrate the promise of the MAE approach in reconstructing SST un- der cloud cover. Applying this methodology to SST data with significant cloud contamination can enhance dataset quality, uncovering details hidden by clouds and expanding the use of high-resolution SST images.