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

Dynamics of sewage outfall plumes based on Landsat-8-derived sea surface salinity and tidal characteristics

In the face of growing marine pollution, assessment of the sewage outfall discharges is essential as it affects the seawater quality. The study demonstrates sea surface salinity (SSS) variations caused by sewage discharges and links it with tidal characteristics to hypothesize the dynamics of sewage outfall plumes. SSS is estimated using a multilinear regression model based on Landsat-8 (L8) OLI reflectance and in situ SSS data of 2013-2014. Using the validated model, the SSS of the 2018 image is predicted and evidenced by its relationship with colored dissolved organic matter (CDOM). The preliminary results of the hypothesis are encouraging and found that the dispersion patterns of outfall plumes exhibit distinct characteristics depending on the intra-tidal range and hour. The findings indicate a lower SSS in the outfall plume zone than in ambient seawater due to dilution caused by partially treated sewage discharges from diffusers. The plumes observed during the macro tidal range are long and narrowly spread alongshore. In contrast, during the meso and microtidal ranges, the plumes are shorter and are primarily dispersed offshore rather than alongshore. During slack times, low salinity levels are visibly concentrated around outfalls as there is no water movement to disperse the accumulated sewage discharges from diffusers. These observations suggest that slack periods and low-tidal conditions could be significant factors contributing to the accumulation of pollutants in coastal waters. The study further suggests more datasets such as wind speed, wind direction, and density variations are needed to understand the processes influencing the outfall plume dynamics and variation in SSS. The study recommends increasing the treatment capabilities of existing treatment facilities from primary to tertiary treatment levels. Furthermore, it is important to warn and educate the public about the health risks associated with exposure to partially treated sewage that is discharged from outfalls.

Surface Water Salinity Evaluation and Identification for Using Remote Sensing Data and Machine Learning Approach

Knowledge of the spatio-temporal distribution of salinity provides valuable information for understanding different processes between biota and environment, especially in hypersaline lakes. Remote sensing techniques have been used for monitoring different components of the environment. Currently, one of the biggest challenges is the spatio-temporal monitoring of the salinity level in water bodies. Due to some limitations, such as the inability to be located there permanently, it is difficult to obtain these data directly. In this study, machine learning techniques were used to evaluate the salinity level in hypersaline East Sivash Bay. In total, 93 in situ data samples and 6 Sentinel-2 datasets were used, according to field measurements. Using linear regression, random forest and AdaBoost models, eight water salinity evaluation models were built (six with simple, one with random forest and one with AdaBoost). The accuracy of the best-fitted simple linear regression model was 0.8797; for random forest, it was equal, at 0.808, and for AdaBoost, it was −0.72. Furthermore, it was found that with an increase in salinity, the absorbing light shifts from the ultraviolet part of the spectrum to the infrared and short-wave infrared parts, which makes it possible to produce continuous monitoring of hypersaline water bodies using remote sensing data.

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

Large rivers are key components of the land-ocean branch of the global water and biogeochemical cycles. River discharges can have important influences on physical, biological, optical, and chemical processes in coastal oceans. It is, therefore, of importance to routinely monitor the time-varying dispersal patterns of river plumes. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the NASA Soil Moisture Active Passive (SMAP) satellites provide Sea Surface Salinity (SSS) observations capable of characterizing the spatial and temporal variability of major river plumes. The main objective of this study is to examine the consistency of SSS products, from these two missions, and two in-situ gridded salinity products in depicting SSS variations on seasonal to interannual time scales within a few hundred kilometers of major river mouths. We show that SSS from SMOS and SMAP satellites have good consistency in depicting seasonal and interannual SSS variations near major river mouths. The two gridded in-situ products underestimate these variations substantially. This underestimation, most notably associated with the low SSS season following the high-discharge season, is attributable to the limited in-situ sampling of the river plumes when they are the most active. This work underscores the importance of using satellite SSS to study river plumes, as well as to evaluate and constrain models.

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