With continued development and urbanization in coastal zones, light pollution has become a pressing environmental issue particularly affecting the biology and ecology of different organisms such as sea turtles. In this study, we investigated the evolution of nighttime light (NTL) pollution in coastal areas adjacent to the main turtle nesting islands in the Saudi offshore waters of the Arabian Gulf using both remote sensing NASA BlackMarble surface bidirectional reflectance data and in-situ observations and measurement using the Sky Quality Meter (SQM). NTL data revealed that of the five coastal areas considered for the analysis, the built-up zone over Abu Ali Island produced the highest radiance of maximum mean value reaching 26,799 nWatts cm-2sr-1with most of the high light level values maintained at around 10,000 nWatts cm-2sr-1. Field observation revealed that light from these coastal areas reached the turtle nesting site of Jana Island. In-situ surveys using SQM instrument provided quantitative data on light pollution levels occurring at the turtle nesting islands of Jana and Karan.

The spatial and temporal variations of Chloropyll-a (Chl-a) in clear and coastal waters are critical for understanding the health of marine environment. To estimate Chl-a from oceanic and coastal waters, several empirical and semi-empirical models based on reflectance are available. However, Machine learning models have been used to simulate multiple environmental parameters over the past few decades, and their applications to water quality parameters have recently gained traction. This study proposes a novel approach to modeling Chl-a by using Fuzzy C-Means clustering based Neural Network (NN) which is optimized through Bayesian Optimization (BO) based on the band configuration of the Moderate Resolution Spectroradiometer Aqua (MODISA) with a wide range of variations. The training data were initially grouped into three clusters based on remote sensing reflectance values, and separate NN models were created for each cluster. Subsequently the hyperparameters of the NN models were optimized. The dataset includes (i) global in-situ measurements of NASA bio-Optical Marine Algorithm Dataset, (ii) SeaWiFS satellite matchups, and (iii) simulated dataset for the Red Sea. It exhibited significant variations in Chl-a levels under both oligotrophic and coastal conditions. Accuracy assessment of the present study is performed by comparing the modeled and observed values of the Chl-a. The performance matrices computed of the developed model were promising. Therefore, this study provides a potential approach for the retrieval of Chl-a in clear and coastal waters where the performance of existing algorithms is deteriorated to estimate precise values of Chl-a. The findings of this research could advance our understanding of biogeochemical cycles and processes in marine and open ocean waters.

In assessing and monitoring the shoreline changes across the Arabian Gulf starting from Al Khafaji near Saudi Kuwait border and ending at Al-Uqair region near Saudi Bahrain border has significantly showed the shoreline changes in coastal environment at multiple locations discussed in the study which have concerned the coastal communities. In this study, we have used freely available satellite datasets with varying satellite resolutions and sensors downloaded from Nasa Earth explorer which paved the way to possible the studies. The present research explored the shoreline shifts in Eastern Province of Saudi Arabia between 1973 to 2022 using remotely sensed medium resolution satellite data (i.e., landsat-8 (OLI), Landsat MSS (4-5) and Landsat TM (4-5)) to classify changes in shorelines for the different years. The results show variations where built-up has been increased drastically from 1973 to 2020. The shoreline length is increased to 1611.44 km in 2020 followed by 1057.63 in 1973.

Remote sensing has been widely used to determine marine chlorophyll by the property that Chl-a reflects electromagnetic radiations for certain wavelengths. Similarly, the same property can be used to determine other oceanographic properties as well such as nitrates, phosphates, iron concentration, etc. Now, it is not always possible to create Chl-a sensitive wavelengths because of hardware limitations and therefore there always exists a need to estimate Chl-a based on other oceanographic properties. For this purpose, supervised machine learning-based regression techniques can be utilized which can be used to train the model to predict marine chlorophyll based on other oceanographic properties. This also shows the dependencies of Chl-a with these oceanographic features. The experiment have been conducted on data obtained from Marine Copernicus Hindcast program where the data have been converted into time series, used different preprocessing techniques and applied regression algorithms. The experiment has obtained an R2 score of up to 0.904. The model can be used to remotely monitor Chl-a concentration in ocean based on other oceanographic properties like nitrates, phosphates, iron concentration, etc.

The advancement of multi-channel synthetic aperture radar (SAR) system is considered as an upgraded technology for surveillance activities. SAR sensors onboard provide data for coastal ocean surveillance and a view of the oceanic surface features. Vessel monitoring has earlier been performed using Constant False Alarm Rate (CFAR) algorithm which is not a smart technique as it lacks decision-making capabilities, therefore we introduce wavelet transformation-based Convolution Neural Network approach to recognize objects from SAR images during the heavy naval traffic, which corresponds to the numerous object detection. The utilized information comprises Sentinel-1 SAR -C dual-polarization data acquisitions over the western coastal zones of India and with help of the proposed technique we have obtained 95.46% detection accuracy. Utilizing this model can automatize the monitoring of naval objects and recognition of foreign maritime intruders.

The summer phytoplankton bloom events have been recently investigated using remote sensing observations over several geographical areas of the Red Sea and changed our impression of its oligotrophic characteristic. However, only limited blooms events were recorded due to active dust storms limiting the observations. This work focuses on predicting the potential bloom events in the central region of the Red Sea, indicated by the chlorophyll- a values, through the machine learning models built from the simulated and observed oceanographical parameters. Four subregions showing active eddy activities are selected to generate modeling datasets in the Case-1 waters (water depth > 300 meters) for each region. Automated model selection and tuning are performed among different candidate supervised models including linear regression, trees models, ensemble models and deep neural networks (101 in total). The ensemble models (random decision forest and bootstrap decision forest) outperform others in showing effective performance in estimating chlorophyll-a values with ( ) of the training and ( ) of the testing processes, respectively. This work shows the potential applications to use a machine learning model to reconstruct missing ocean color observations, as well as revealing the oceanographical mechanism to induce phytoplankton growth in the Red Sea.

Primary productivity (PP) has been recently investigated using remote sensing-based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models-the vertically generalized production model (VGPM), the Eppley-VGPM, and the carbon-based production model (CbPM)-produced over the Red Sea domain for the 1998-2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization, and moving averages correlative analysis to uncover the models' responses to various climate factors. Here, we use the models' eight-day composite and monthly averages compared with satellite-based variables, including chlorophyll-a (Chla), mixed layer depth (MLD), and sea-surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO), and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI, and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models' connection to oceanographic and atmospheric parameters, as well as the trends in the southern Red Sea, thus calling for further validation efforts.

An optical algorithm is developed for the retrieval of the downwelling diffuse attenuation coefficient Kd(490) in the Red Sea using a comprehensive hydrolight simulated data set (N = 5000). We found a robust relationship between the Kd(490) and the ratio of remote sensing reflectance Rrs(443)/Rrs(555), with an excellent determination coefficient (R2= 0.999) and a low root-mean-square error (RMSE = 0.00033). The performance of the developed algorithm is evaluated with in situ data collected in the Red Sea by comparing obtained model output with existing empirical (NASA, Morel et al., Zhang and Fell, Tiwari and Shanmugam) and semianalytical (Lee et al.) algorithms. On the used in situ data from the Red Sea, the new algorithm shows good retrievals of Kd(490) with a low bias, and a low RMSE compared to that of the existing algorithms. For satellite application, we applied our algorithm to selected MODIS-Aqua images acquired over the Red Sea, which captured spatial features of phytoplankton blooms and physical processes (e.g., cyclonic and anticyclonic circulations) in the Red Sea. The new algorithm has the potential to improve our understanding of water transparency and photosynthetic processes that rely on the availability of solar radiation.