Effective estimation of water quality parameters is essential for the sustainability of the coastal ecosystem. This research examines the potential use of Sentinel-2 Satellite images with machine learning models to measure the concentrations of Cholorophyll-a (Chl-a) and Total Suspended Solids (TSS) in the coastal area of Abu Dhabi. Google Earth Engine (GEE) was utilized to obtain Sentinel-2 Level-2A surface reflectance values, which are collocated with the in situ data. Field measurements were obtained from various locations, with 365 and 196 available samples for Chl-a and TSS, respectively. The former had 165 collocated points, whereas the latter had only 77 points. For feature engineering, two strategies were compared: spectral indices from the literature and Principal Component Analysis (PCA) with raw bands. Four machine learning algorithms were examined to find the optimal model for each parameter by using 5-fold cross-validated hyperparameter tuning. The selected models are Random Forest Regression (RFR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Partial Least Squares (PLS) Regression. For Chl-a, the analysis showed that a general model was limited by localized bloom events near coastal outlets. Creating a specialized “Ambient-Conditions” model by excluding these outliers greatly improved performance. The optimal Chl-a model (XGB with PCA on six bands) achieved the highest accuracy with Test R 2 = 0.7 and Test RMSE of 1.62 µg/L, representing an 80% improvement in precision compared to the general model trained on the full dataset (Test R² = 0.65, RMSE = 8.21 µg/L). PCA + Random Forest (on 10 bands) was the optimal model for TSS, with R 2 = 0.61, despite the small dataset size. The results demonstrated that merging machine learning and remote sensing is effective for retrieving Chl-a and TSS in challenging marine waters.
Ibrahim et al. (Fri,) studied this question.