Remote Sensing Estimation of Sea Surface Salinity from GOCI Measurements in the Southern Yellow Sea

Abstract

Knowledge about the spatiotemporal distribution of sea surface salinity (SSS) provides valuable and important information for understanding various marine biogeochemical processes and ecosystems, especially for those coastal waters significantly affected by human activities. Remote-sensing techniques have been used to monitor salinity in the open ocean with their advantages of wide-area surveys and real-time monitoring. However, potential challenges remain when using satellite data with coarse spatiotemporal resolutions, leading to a loss of valuable information. In the current study, based on the local dataset collected over the southern Yellow Sea (SYS), a region-customized algorithm was developed to estimate SSS by using the remote sensing reflectance. The model evaluations indicated that our algorithm yielded good SSS estimation, with a root-mean-square error (RMSE) of 0.29 psu and a mean absolute percentage error (MAPE) of 0.75%. Satellite-derived SSS results compared well with those derived from in situ observations, further suggesting the good performance of our developed algorithm for the study regions. We applied this algorithm to Geostationary Ocean Color Imager (GOCI) data for the month of August from 2011 to 2018 in the SYS, and produced the spatial distribution patterns of the SSS for August of each year. The SSS values were high in offshore waters and lower in coastal waters, especially in the Yangtze River estuary. The negative correlation between the monthly Changjiang River discharge (CRD) and SSS (R = −0.71, p < 0.001) near the Yangtze River estuary was observed, suggesting that the SSS distribution in the Yangtze River estuary was potentially influenced by the CRD. In offshore waters, the correlation between SSS and CRD was weak (R < 0.2), suggesting that the riverine discharge’s effect might be weak.

Seasonal and Interannual Variability of Sea Surface Salinity Near Major River Mouths of the World Ocean Inferred from Gridded Satellite and In-Situ Salinity Products

“…Sea Surface Salinity (SSS) observations have been extensively used to trace major river plumes into the ocean [7][8][9][10][11][12][13][14]. Satellite measurements of SSS have been available from the Soil Moisture and Ocean Salinity (SMOS) satellite of the European Space Agency (ESA) since 2010 and from the Soil Moisture Active Passive (SMAP) satellite of the National Aeronautics and Space Administration (NASA) since 2015.…”

Estimation of Hourly Sea Surface Salinity in the East China Sea Using Geostationary Ocean Color Imager Measurements

“…To continuously monitor the SSS distribution along coastal regions, ocean color measurements have been used in various coastal regions [22][23][24][25][26][27][28]. Some studies have primarily applied the relationship between the SSS and the colored dissolved organic matter (CDOM) in different regions, such as the Clyde Sea [23], the ECS [6,25,26], and Osaka Bay in Japan [21].…”

“…Therefore, GOCI data were used to develop the SSS estimation model and produce SSS images in the ECS. Liu et al [27], Nakada et al [21], and Sun et al [28] attempted to analyze the SSS using GOCI remote sensing reflectance (Rrs) measurements in the Bohai Sea, the Osaka Bay, and the Southern Yellow Sea (SYS), respectively. Liu et al [27] developed a simple multi-linear statistical regression model to estimate the SSS using GOCI data.…”

“…Their study also compared a plume area before and after a typhoon period. Sun et al [28] estimated the SSS for the month of August from 2011 to 2018 in the SYS and achieved a root mean square error (RMSE) of 0.29 psu. Nevertheless, the algorithms discussed in these studies were specific to each study region and require further validations to apply to other regions.…”

“…Nevertheless, the algorithms discussed in these studies were specific to each study region and require further validations to apply to other regions. Moreover, the three studies described above analyzed the same ocean color satellite data (GOCI) to develop their algorithms with different wavelength GOCI bands (i.e., Liu et al [27] used Rrs490, Rrs555, and Rrs660; Nakada et al [21] used Rrs412 and Rrs555; and Sun et al [28] used Rrs490 and Rrs555).…”