The Applicability of the Geostationary Ocean Color Imager to the Mapping of Sea Surface Salinity in the East China Sea

Abstract

During the summer season, low-salinity water (LSW) inputs from the Changjiang River are observed as filamentous or lens-like features in the East China Sea. Sea surface salinity (SSS) is an important factor in ocean science, and is used to estimate oceanic carbon fluxes, trace red tides, and calculate other physical processes at the surface. In this study, a proxy was developed using remote sensing reflectance (Rrs) from the Geostationary Ocean Color Imager (GOCI) centered at 490 nm (band 3), 555 nm (band 4), 660 nm (band 5), and 680 nm (band 6), and salinity (data from summer cruises during the period of 2011–2016). It was then validated to map LSW plumes in the East China Sea. The GOCI-derived surface salinity was determined by the empirical relationships between Rrs at the four bands and in situ wave glider SSS data (August 2016), and was validated with synchronous in situ hydrographic SSS data (August 2011, 2012, 2013, and 2016). The GOCI-derived SSS was considered reliable in terms of the validation with the in situ measurement with a high coefficient of determination along with a low RMSE (R2 = 0.803, RMSE = 0.914, N = 21), and in comparisons with two previous models that were used to derive SSS in the East China Sea. The GOCI-derived SSS was successfully used to examine time-series variations on diurnal and daily scales, and the effects of a typhoon in terms of marine physical and biological properties in combination with the chlorophyll-a concentration and sea surface temperature.

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

“…The estimation and mapping of the distribution on SSS are done by implementing the random forest (RF) algorithm using the single and multiple bands composite of Landsat 8 and 9, besides several equations of linear multiple regressions that are used to predict the most acceptable estimation result of the salt concentration (dS/m) and SSS (table 3). Although linear and multiple regression are often used in several studies of SSS [37][38][39]. The explanation about the water spectral reflectance is agreed with Ghazali et al [31] , who provide the spectral pattern to understand the observed object's characteristic, which is critical.…”

U-Net Super-Resolution Model of GOCI to GOCI-II Image Conversion

“…GOCI specializes in monitoring hourly variations in ocean color products, with high temporal capability allowing ocean color sensors to overcome the limitations caused by frequent cloud cover. GOCI has been used for many studies including the spatiotemporal variation of coastal water turbidity [14], [15], diurnal changes in red tide blooms [16], [17], [18], tracing floating green algae blooms [19], variation of sea surface salinity [20], and diurnal variation in ocean properties [21]. Park et al [22] investigated the spatial scale of mesoscale eddies using GOCI-derived chlorophyll-a concentration (CHL) products from the East Sea.…”

Estimating Sea Surface Salinity in the East China Sea Using Satellite Remote Sensing and Machine Learning

“…Traditional regression methods, including multiple linear regression (MLR) and multiple nonlinear regression (MNR) have estimated SSS using satellite R rs data in the Bohai Sea (BS), southern Yellow Sea (YS) and low‐salinity water plumes in the ECS (Choi et al., 2021; Qing et al., 2013; Sun et al., 2019, respectively), and Marghany and Hashim (2011) retrieved SSS from MODIS data in the South China Sea (SCS) using a Box‐Jenkins algorithm. Machine learning has also been applied to model SSS.…”

“…In order to observe the SSS distributions continuously for the coastal waters, ocean color measurements have been widely used due to their high spatial‐temporal resolution (Ahn et al., 2008; Bai et al., 2013; Chen & Hu, 2017; Choi et al., 2021; Geiger et al., 2013; Hu et al., 2003; Kim et al., 2020; Marghany & Hashim, 2011; Qing et al., 2013; Sun et al., 2019; Urquhart et al., 2012; Zhao et al., 2017). In these studies, there are two main routes to retrieve the SSS from space, developing the relationship between SSS and the colored dissolved organic matter (CDOM) absorption coefficient ( a CDOM , m −1 ) and the a CDOM can be estimated from ocean color measurements (Ahn et al., 2008; Bowers & Brett, 2008; Carder et al., 2003; Del Vecchio & Blough, 2004; Zhu et al., 2011); and directly developing the linear or nonlinear relationship between SSS and ocean color measurements, such as remote sensing reflectance ( R rs , sr −1 ) (e.g., Chen & Hu, 2017; Kim et al., 2020; Qing et al., 2013; Sun et al., 2019).…”