Estimating sea surface salinity in the northern Gulf of Mexico from satellite ocean color measurements

Sea surface salinity (SSS) is an important tracer for monitoring the Changjiang Diluted Water (CDW) extension into Korean coastal regions; however, observing the SSS distribution in near real time is a difficult task. In this study, SSS detection algorithm was developed based on the ocean color measurements by Geostationary Ocean Color Imager (GOCI) in high spatial and temporal resolution using multilayer perceptron neural network (MPNN). Among the various combinations of input parameters, combinations with three to six bands of GOCI remote sensing reflectance (Rrs), sea surface temperature (SST), longitude, and latitude were most appropriate for estimating the SSS. According to model validations with the Soil Moisture Active Passive (SMAP) and Ieodo Ocean Research Station (I-ORS) SSS measurements, the coefficient of determination (R2) were 0.81 and 0.92 and the root mean square errors (RMSEs) were 1.30 psu and 0.30 psu, respectively. In addition, a sensitivity analysis revealed the importance of SST and the red-wavelength spectral signal for estimating the SSS. Finally, hourly estimated SSS images were used to illustrate the hourly CDW distribution. With the model developed in this study, the near real-time SSS distribution in the East China Sea (ECS) can be monitored using GOCI and SST data.

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Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico

Remote Sensing ◽

10.3390/rs13050881 ◽

2021 ◽

Vol 13 (5) ◽

pp. 881

Author(s):

Zhiyi Fu ◽

Fangfang Wu ◽

Zhengliang Zhang ◽

Linshu Hu ◽

Feng Zhang ◽

...

Keyword(s):

Gulf Of Mexico ◽

River Discharge ◽

River Flow ◽

High Accuracy ◽

Sea Surface ◽

Sea Surface Salinity ◽

Temporal Heterogeneity ◽

Surface Salinity ◽

Circular Distribution ◽

Heterogeneity Analysis

As an important parameter to characterize physical and biogeochemical processes, sea surface salinity (SSS) has received extensive attention. Cubist is a data mining model, which can be well-suited to estimate and analyze SSS in the Gulf of Mexico (GOM) because it can reflect the SSS internal heterogeneity in the GOM—overall circular distribution, and the seasonality related to temperature and river discharge changes. Using remote sensing reflectance (Rrs) at 412, 443, 488 (490), 555, and 667 (670) nm and sea surface temperature (SST), a cubist model was developed to estimate SSS with high accuracy with the overall performance demonstrates a root mean square error (RMSE) of 0.27 psu and correlation coefficient of 0.97 of R2. The model divides the GOM area according to model rules into four sub-regions, which include estuary, nearshore, and open sea, reflecting the gradient distribution of SSS. The division of sub-regions and seasonal changes can be explained by the distribution of water bodies, river discharges, and local wind forces since it is obvious that the estuary region reaches the largest low-value area and spreads eastward with the monsoon in the spring when the river flow increases to the highest value. While the east to west wind in the non-summer monsoon period guides the plume westward, and the lowest river discharge in winter corresponds to the smallest low value area. After comparison with other statistical models, the cubist model showed satisfactory results in independent verification of cruise data, proving the estimation capability under different geographical conditions (such as estuaries and open seas) and seasons. Therefore, considering high accuracy and heterogeneity mining, the cubist-based model is an ideal method for coastal SSS estimation and spatial-temporal heterogeneity analysis, and can provide ideas for model construction for coastal areas with similar geographic environments.

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

Remote Sensing ◽

10.3390/rs13142676 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2676

Author(s):

Jong-Kuk Choi ◽

Young-Baek Son ◽

Myung-Sook Park ◽

Deuk-Jae Hwang ◽

Jae-Hyun Ahn ◽

...

Keyword(s):

East China Sea ◽

Ocean Color ◽

Sea Surface ◽

Sea Surface Salinity ◽

East China ◽

Red Tides ◽

The East China Sea ◽

Surface Salinity ◽

China Sea

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.

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