Accurate monitoring of chlorophyll-a (Chl-a) is critical for assessing eutrophication and water quality in inland aquatic ecosystems. While integrating Sentinel-2 MultiSpectral Instrument (MSI) imagery with machine learning (ML) is widely used for water quality assessment, challenges remain in optimizing Chl-a retrieval in optically complex small-scale reservoirs, particularly in semi-arid environments where traditional models often fail. This study introduces a machine learning (ML)-based approach to enhance Sentinel-2 MSI Chl-a retrieval, systematically evaluating six ML models: Artificial Neural Networks (ANN), CatBoost (CB), random forest (RF), ridge regression (RR), support vector regression (SVR), and extreme gradient boosting (XGB). By integrating Sentinel-2 MSI reflectance data with in-situ measurements from reservoirs exhibiting diverse trophic conditions, this study not only compares model performance but also applies feature importance analysis to refine spectral band selection for improved Chl-a retrieval. Among the models, RF demonstrated the highest predictive accuracy (R² = 0.93 calibration, R² = 0.91 validation), outperforming CB and XGB, whereas linear models such as RR and SVR were inadequate for capturing the nonlinear spectral relationships of Chl-a. The results highlight the advantages of ensemble-based ML models in handling complex water quality datasets, particularly in semi-arid and data-scarce regions. The proposed framework provides a robust, data-driven approach for integrating Sentinel-2 MSI imagery with AI techniques, supporting real-time water quality assessment and environmental management in diverse aquatic ecosystems. This research advances the application of ML for remote sensing-based water quality monitoring by systematically assessing model reliability, feature selection, and efficient application in semi-arid small-scale reservoirs. The proposed approach enhances the scalability of ML-driven remote sensing, offering a transferable framework for Chl-a estimation in data-scarce and environmentally vulnerable regions.

This study focuses on the utilization of multispectral satellite images for remote water-quality evaluation of inland water body in Jordan. The geophysical parameters based on water’s optical properties, due to the presence of optically active constituents, are used to determine contaminant level in water. It has a great potential to be employed for continuous and cost-effective water-quality monitoring and leads to a reliable regularly updated tool for better water sector management. Three sets of water samples were collected from three different dams in Jordan. Chl-a concentration of the water samples was measured and used with corresponding Sentinel 2 surface reflectance (SR) data to develop a predictive model. Chl-a concentrations and corresponding SR data were used to calibrate and validate different models. The predictive capability of each of the investigated models was determined in terms of determination coefficient (R²) and lowest root mean square error (RMSE) values. For the investigated sites, the B3/B2 (green/blue bands) model and the Ln (B3/B2) model showed the best overall predictive capability of all models with the highest R² and the lowest RMSE values of (0.859, 0.824) and (30.756 mg/m³, 29.787 mg/m³), respectively. The outcome of this study on selected sites can be expanded for future work to cover more sites in the future and ultimately cover all sites in Jordan.

An urban heat island phenomenon has increased in the last decades due to rapid urbanization, resulting in a significant impact on local climate. In this study, remote sensing data was used to analyze Spatiotemporal patterns of Urban Heat Island Intensity (UHII) over a 20 years period in a semi-arid climate area. The relationship between the Land Surface Temperature (LST), vegetation and Land Cover Types (LCTs) were examined. The relation between the UHII and its driving factors (different LCTs and meteorological conditions) was analyzed. Analysis of 8-day daytime and nighttime LST data acquired from MODerate-resolution Imaging Spectroradiometer (MODIS) shows that Amman has a significant UHII in both daytime and night time. The results show a negative correlation between the LST and vegetation indicators and between the UHII and the wind speed average, indicating a positive correlation between the UHII and temperature. Vegetation has been proven to significantly reduce LST, mainly in the daytime, due to its cooling effect that results from the transpiration process and shadow effect.