PRELIMINARY RESULTS OF SEA SURFACE SALINITY MAPPING AROUND KOREAN COASTAL WATERS USING OCEAN COLOUR SATELLITE IMAGES AND IN-SITU MEASUREMENTS

Abstract

Sea surface temperature (SST) and sea surface salinity (SSS) are the most important parameters in the ocean science to estimate carbon flux between atmosphere and ocean surface. SST can be easily obtained by various ocean satellite sensors like MODIS, SeaWiFS, etc. While SSS can be estimated by microwave sensors such as SMOS and Aquarius, it is hard to obtain remotely sensed SSS in the Korean coastal waters due to their low spatial and temporal resolutions. As an alternative way of obtaining SSS data in the coastal region, we can use coloured dissolved organic matter (CDOM) absorption coefficient which has been known to have a high correlation with SSS in coastal region in relation to freshwater inflow from the land. That coefficient can be obtained from ocean color satellite data such as GOCI and MODIS. Here, we estimated SSS around Korean coastal waters by using the relationship between in-situ measured SSS and CDOM absorption coefficient, from which an empirical algorithm was developed to generate the SSS map from remotely sensed data. This preliminary result revealed that a further study on SSS mapping in the open ocean using remotely sensed data would be meaningful.like MODIS, SeaWiFS, etc. While SSS can be estimated by microwave sensors such as SMOS and Aquarius, it is hard to obtain remotely sensed SSS in the Korean coastal waters due to their low spatial and temporal resolutions. As an alternative way of obtaining SSS data in the coastal region, we can use coloured dissolved organic matter (CDOM) absorption coefficient which has been known to have a high correlation with SSS in coastal region in relation to freshwater inflow from the land. That coefficient can be obtained from ocean color satellite data such as GOCI and MODIS. Here, we estimated SSS around Korean coastal waters by using the relationship between in-situ measured SSS and CDOM absorption coefficient, from which an empirical algorithm was developed to generate the SSS map from remotely sensed data. This preliminary result revealed that a further study on SSS mapping in the open ocean using remotely sensed data would be meaningful.