An improved sea surface salinity retrieval algorithm for the Chinese Bohai Sea based on hyperspectral reconstruction and its applicability analysis

Salinity is regarded as a critical factor in marine systems. It is essential to monitor the long-term changes in sea surface salinity (SSS), as well as its short-term changes. Along these lines, in this work, to monitor the hourly, daily, and monthly changes in SSS in the Chinese Bohai Sea from GOCI (Geostationary Ocean Color Imager) images, the feasibility of adopting a MERIS-based SSS retrieval algorithm was systematically explored to develop a GOCI-based SSS retrieval algorithm. To avoid the influence of the atmospheric correction error on R_rs(λ) of the similar bands of MERIS (490 nm, 560 nm, and 665 nm) and those of GOCI (490 nm, 555 nm, and 660 nm), a simple linear regression model was applied based on the ρ_rs(λ) matchup of the similar bands by using a hyperspectral reconstruction method with the measured ρ(λ). Despite the aerosol-based scattering errors, a more reasonable result (R² = 0.86, RMSE = 0.79 psu) over a long time scale was gained by the improved SSS retrieval algorithm, which was validated by the measured data (R² = 0.92, RMSE = 0.72 psu). On top of that, the temporal and spatial distribution of SSS, as well as its diurnal, daily, and monthly changes, were then obtained in the Chinese Bohai Sea from April 2011 to March 2012. The impact of runoff on SSS was also analysed based on instant and lag correlations.