Effective water quality monitoring in coastal regions is crucial due to the progressive deterioration caused by pollution and human activities. To address this, this study develops time-series models to predict chlorophyll-a (Chl-a), suspended solids (SS), and turbidity using Sentinel-2 satellite data and Google Earth Engine (GEE) in the coastal regions of Hong Kong. Leveraging Long Short-Term Memory (LSTM) Recurrent Neural Networks, the study incorporates extensive temporal datasets to enhance prediction accuracy. The models utilize spectral data from Sentinel-2, focusing on optically active components, and demonstrate that selected variables closely align with the spectral characteristics of Chl-a and SS. The results indicate improved predictive performance over previous methods, highlighting the potential for remote sensing technology in continuous and comprehensive water quality assessment.

Managing the quality of water for present and future generations of coastal regions should be a central concern of both citizens and public officials. Remote sensing can contribute to the management and monitoring of coastal water and pollutants. Algal blooms are a coastal pollutant that is a cause of concern. Many satellite data, such as MODIS, have been used to generate water-quality products to detect the blooms such as chlorophyll a (Chl-a), a photosynthesis index called fluorescence line height (FLH), and sea surface temperature (SST). It is important to characterize the spatial and temporal variations of these water quality products by using the mathematical models of these products. However, for monitoring, pollution control boards will need nowcasts and forecasts of any pollution. Therefore, we aim to predict the future values of the MODIS Chl-a, FLH, and SST of the water. This will not be limited to one type of water but, rather, will cover different types of water varying in depth and turbidity. This is very significant because the temporal trend of Chl-a, FLH, and SST is dependent on the geospatial and water properties. For this purpose, we will decompose the time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary. We explore three such time series machine learning models that can characterize the non-stationary time series data and predict future values, including the Seasonal ARIMA (Auto Regressive Integrated Moving Average) (SARIMA), regression, and neural network. The results indicate that all these methods are effective at modelling Chl-a, FLH, and SST time series and predicting the values reasonably well. However, regression and neural network are found to be the best at predicting Chl-a in all types of water (turbid and shallow). Meanwhile, the SARIMA model provides the best prediction of FLH and SST.