Ocean Salinity Retrieval and Prediction for Soil Moisture Active Passive Satellite Using Data-to-Data Translation

Sea surface salinity (SSS) is a key parameter in physical oceanography, global hydrological, biochemical cycles, and climate change studies. L-band radiometers onboard satellites, such as soil moisture active passive (SMAP), soil moisture, and ocean salinity (SMOS), have played a crucial role in monitoring and analyzing the global SSS in the past decades. This study presents a method to retrieve and predict the SMAP SSS through data-to-data translation (D2D) based on conditional generative adversarial networks (CGANs). The model was constructed from the polarized brightness temperatures (TBs), differences in the polarized TBs from the L-band of the SMAP satellite, and sea surface temperature (SST) and sea surface wind speed (SSW) from the European Center for Medium-Range Weather Forecasts data from April 2015 to July 2020, and applied to produce SMAP SSS. A comparison between the SMAP salinity and the D2D-generated SMAP salinity showed excellent agreement, evaluated through bias = 0.016, root mean square error (RMSE) = 0.173 in practical salinity unit (psu), and correlation coefficient (CC) = 0.985. Furthermore, a comparison between the D2D-generated and buoy-observed salinities showed good agreement (bias = −0.031 psu and RMSE = 0.196 psu, CC = 0.971). In addition, the results of the one-month prediction model were also in good agreement with the SMAP SSS (bias = 0.028 psu and RMSE = 0.218 psu, CC = 0.977). Consequently, the D2D-based model can be effectively used to generate SMAP SSS information and can be applied to various microwave satellites.