Context Constraints-Guided Reconstruction Robust Network for Cloud-Induced Extensive Missing Area in Sea Surface Temperature

Sea surface temperature (SST), a crucial indicator widely applied in marine-related fields, is often observed by extensive cloud cover in satellite observations, resulting in significant data gaps. While deep learning approaches offer solutions for reconstructing SST images using ample historical data, the majority of current methods overlook the context constraints inherent in oceanographic data, such as sequential correlations and global consistency. This oversight leads to non-robust completion results in the presence of extensive data gaps. This paper proposes a robust SST reconstruction network guided by sequential correlations and global consistency, designed to handle the task of extensive missing data recovery in SST under large-scale cloud cover, namely CCG_RRN. Extensive comparison experiments and visualization results affirm the effectiveness and robustness of CCG_RRN compared with the State-Of-The-Art (SOTA) methods on the public NSOAS SST datasets.