Assessing the Feasibility of Satellite-Based Machine Learning for Turbidity Estimation in the Dynamic Mersey Estuary (Case Study: River Mersey, UK)

Nangir, D ORCID: 0009-0006-6931-3832, Andredaki, M and Carnacina, I ORCID: 0000-0001-5567-7180 (2025) Assessing the Feasibility of Satellite-Based Machine Learning for Turbidity Estimation in the Dynamic Mersey Estuary (Case Study: River Mersey, UK). Remote Sensing, 17 (21). ISSN 2072-4292

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Abstract

The monitoring of turbidity in estuarine environments is a challenging essential task for managing water quality and ecosystem health. This study focuses on the lower reaches of the River Mersey, Liverpool. Harmonized Sentinel-2 MSI Level-2A imagery was integrated with in situ measurements from seven Environment Agency monitoring stations for two consecutive years (January 2023–January 2025). The workflow included image preprocessing, spectral index calculation, and the application of four machine learning algorithms: Gradient Boosting Regressor, XGBoost, Support Vector Regressor, and K-Nearest Neighbors. Among these, Gradient Boosting Regressor achieved the highest predictive accuracy (R2 = 0.84; RMSE = 15.0 FTU), demonstrating the suitability of ensemble tree-based methods for capturing non-linear interactions between spectral indices and water quality parameters. Residual analysis revealed systematic errors linked to tidal cycles, depth variation, and salinity-driven stratification, underscoring the limitations of purely data-driven approaches. The novelty of this study lies in demonstrating the feasibility and proof-of-concept of using machine learning to derive spatially explicit turbidity estimates under data-limited estuarine conditions. These results open opportunities for future integration with Computational Fluid Dynamics models to enhance temporal forecasting and physical realism in estuarine monitoring systems. The proposed methodology contributes to sustainable coastal management, pollution monitoring, and climate resilience, while offering a transferable framework for other estuaries worldwide.

Item Type:	Article
Uncontrolled Keywords:	turbidity monitoring; Sentinel-2; GEE; machine learning; River Mersey; 37 Earth Sciences; 3704 Geoinformatics; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; 0203 Classical Physics; 0406 Physical Geography and Environmental Geoscience; 0909 Geomatic Engineering; 3701 Atmospheric sciences; 3709 Physical geography and environmental geoscience; 4013 Geomatic engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Biological and Environmental Sciences (from Sep 19) Civil Engineering and Built Environment
Publisher:	MDPI
Date of acceptance:	29 October 2026
Date of first compliant Open Access:	27 May 2026
Date Deposited:	27 May 2026 09:50
Last Modified:	27 May 2026 09:50
DOI or ID number:	10.3390/rs17213617
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28649

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