First assessment of CyGNSS-incorporated SMAP sea surface salinity retrieval over pan-tropical ocean

In sea surface salinity (SSS) retrieval using L-band passive radiometry, radiometer-independent ocean wind speed is needed as auxiliary data. Wind speed data from scatterometer and weather models are commonly used as auxiliary data in satellite SSS missions. This papers overarching goal is to explore the feasibility of incorporating the Cyclone Global Navigation Satellite System (CyGNSS) data into the SSS retrieval algorithm of the Soil Moisture Active and Passive (SMAP) mission over tropical and subtropical oceans. As a proof-of-concept study, empirical geophysical model functions (GMF) in the retrieval algorithm are developed using the statistics of collocated SMAP, CyGNSS, and referenced buoys measurements. The SSS accuracy of CyGNSS-incorporated salinity retrieval is investigated against the SMAP SSS data product. Comparisons show that the proposed CyGNSS-incorporated retrieval algorithm improves the SSS accuracy by 0.1~0.2 psu at low wind speed (< 2 m/s). To some extent, it proves that Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) could be a new and helpful data source to understand wind-induced emissivity over a smooth ocean. The dependencies of emissivity on different geophysical parameters (i.e., sea surface temperature, significant wave height, and precipitation) are analyzed, and the spatial and seasonal variabilities of SSS errors are shown and linked to these geophysical parameters. The findings of this research provide valuable insights for future development and operation of the radiometer-based SSS retrieval algorithm using wind speed data from spaceborne GNSS-R.