Improving timeliness and accuracy of daily gap-free satellite sea surface salinity using a self-supervised deep neural network

Spatiotemporally continuous sea surface salinity (SSS) is essential for monitoring rapid changes in the physical and biogeochemical characteristics of oceans and plays a crucial role in effective coastal environment management. Traditional physics-based SSS methods often oversmoothed salinity variations and struggle to capture sharp gradients, leading to reduced accuracy in river-dominated areas. In addition, the long revisit times of satellite data limit real-time monitoring. To address these challenges, we propose the multi-scale aware interpolation network (MAIN), a self-supervised deep neural network designed to generate near-real-time, gap-free daily SSS data without relying on future observations. The input data consisted of soil moisture active passive (SMAP) L2B swath data and the bias-corrected SMAP data that were enhanced using in situ measurements. Validation against in situ measurements and existing satellite-derived SSS products demonstrates that MAIN significantly improves salinity estimation accuracy. These findings demonstrate that MAIN is a robust and scalable framework that enhances gap-free SSS estimation, offering new opportunities for monitoring transient oceanic processes in diverse marine environments.