Generating a monthly variability of sea surface salinity based on source tracing of salt concentration and the estimated SEBAL-evaporation

Abstract

Abstract The variation and spatial distribution of sea surface salinity (SSS) depend on the geographic condition of the water surfaces and the temporal variation of atmospheric conditions. The SSS might differ in a local coastal area compared to similar situations in global and regional oceans. The SSS values have been estimated based on spatial regression of extracted water-salt concentration as a source tracing of salt against corrected Landsat 8 satellite data during the drought season of April 2023. Here, the electrical conductivity (EC) from the Cimanuk River can be used as primary data. This result, paired with the evaporation-derived surface energy balance algorithm for land (SEBAL) algorithm, explains a monthly SSS variability after the validation using pre-defined resampled regional SSS and evaporation data. The result shows variations in estimated SSS values along with fluctuated SEBAL evaporation ranging from 1.64 to 1.62 dS/m and 1.04 to 0.41 W/m2, respectively. It describes monthly variability and their relationship in a local coastal area limited to the condition of a drought season. However, the validation shows that the root means square error (RMSE) of 1.00 from the SSS map, produced by the regression model involving band 7 of Landsat 8 and 9, has satisfied the reasonable SSS value ranges besides the best accuracy.