Bayesian merging of numerical modeling and remote sensing for saltwater intrusion quantification in the Vietnamese Mekong Delta

Saltwater intrusion has become one of the most concerning issues in the Vietnamese Mekong Delta (VMD) due to its increasing impacts on agriculture and food security of Vietnam. Reliable estimation of salinity plays a crucial role to mitigate the impacts of saltwater intrusion. This study developed a hybrid technique that merges satellite imagery with numerical simulations to improve the estimation of salinity in the VMD. The salinity derived from Landsat images and by numerical simulations was fused using the Bayesian inference technique. The results indicate that our technique significantly reduces the uncertainties and improves the accuracy of salinity estimates. The Nash-Sutcliffe coefficient is 0.74, which is much higher than that of numerical simulation (0.63) and Landsat estimation (0.6). The correlation coefficient between the ensemble and measured salinity is relatively high (0.88). The variance of the ensemble salinity errors (5.0 ppt2) is lower than that of Landsat estimation (10.4 ppt2) and numerical simulations (9.6 ppt2). The proposed approach shows a great potential to combine multiple data sources of a variable of interest to improve its accuracy and reliability wherever these data are available.

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.

MODIS Derived Sea Surface Salinity, Temperature, and Chlorophyll-a Data for Potential Fish Zone Mapping: West Red Sea Coastal Areas, Saudi Arabia

In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R² = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.

A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques

Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water’s surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD).

The global distribution and dynamics of chromophoric dissolved organic matter

Chromophoric dissolved organic matter (CDOM) is a ubiquitous component of the open ocean dissolved matter pool, and is important owing to its influence on the optical properties of the water column, its role in photochemistry and photobiology, and its utility as a tracer of deep ocean biogeochemical processes and circulation. In this review, we discuss the global distribution and dynamics of CDOM in the ocean, concentrating on developments in the past 10 years and restricting our discussion to open ocean and deep ocean (below the main thermocline) environments. CDOM has been demonstrated to exert primary control on ocean color by its absorption of light energy, which matches or exceeds that of phytoplankton pigments in most cases. This has important implications for assessing the ocean biosphere via ocean color-based remote sensing and the evaluation of ocean photochemical and photobiological processes. The general distribution of CDOM in the global ocean is controlled by a balance between production (primarily microbial remineralization of organic matter) and photolysis, with vertical ventilation circulation playing an important role in transporting CDOM to and from intermediate water masses. Significant decadal-scale fluctuations in the abundance of global surface ocean CDOM have been observed using remote sensing, indicating a potentially important role for CDOM in ocean-climate connections through its impact on photochemistry and photobiology.