End-to-End Customized CNN Pipeline for Multiparameter Surface Water Quality Estimation from Sentinel-2 Imagery.
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| Title: | End-to-End Customized CNN Pipeline for Multiparameter Surface Water Quality Estimation from Sentinel-2 Imagery. |
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| Authors: | Sharaf El Din, Essam1,2 (AUTHOR), El Zahar, Karim M.1,2 (AUTHOR), Shaker, Ahmed1,2 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p794. 32p. |
| Subjects: | Convolutional neural networks, Parameter estimation, Water quality, Dissolved organic matter, Remote-sensing images, MatLab (Computer software), Dissolved oxygen in water, Remote sensing |
| Geographic Terms: | New Brunswick |
| Abstract: | Highlights: What are the main findings? A customized end-to-end CNN, developed in MATLAB and trained on Sentinel-2 and in situ data, accurately and simultaneously predicts TOC and DO with R2 > 0.97, while generating spatially coherent, high-resolution maps over New Brunswick. The pipeline, including image mosaicking, k-means water masking, 4D spectral–spatial dataset construction, and memory-efficient patch-based inference, yields a robust, reproducible framework that successfully captures both optical and non-optical water quality dynamics. What are the implications of the main findings? Demonstrating reliable retrieval of both optically active and non-optically active parameters from freely available satellite data, the framework enables non-invasive, cost-effective, and continuous monitoring to support evidence-based management of freshwater systems. The incorporation of SHAP-based explainability identifies key Sentinel-2 bands driving DO and TOC predictions, guiding sensor/band selection and facilitating transfer of the method to other regions and indicators for operational water quality services. This study addresses the critical need for accurate, continuous monitoring of surface water quality parameters (SWQPs) using remote sensing, overcoming limitations in existing models that often rely on pre-trained networks ill-suited for complex aquatic environments. We present a customized convolutional neural network (CNN) architecture, implemented in the MATLAB environment, designed to simultaneously predict optically active (Total Organic Carbon, TOC) and non-optically active (Dissolved Oxygen, DO) parameters from eighteen Sentinel-2 Level-2A satellite images, acquired between 2023 and 2024. Our approach integrates spatial and spectral data through a customized CNN with three convolutional layers and two dense layers, optimized via adaptive learning strategies, data augmentation, and rigorous regularization to enhance predictive performance and prevent overfitting. The models were trained and validated on fused datasets of satellite imagery and in situ measurements, organized into comprehensive four-dimensional arrays capturing spectral, spatial, and sample dimensions. The results demonstrated high accuracy, with coefficient of determination (R2) values exceeding 0.97 and low root mean square error (RMSE) across training, validation, and testing subsets. Spatial prediction maps generated at high resolution revealed realistic ecological and hydrological patterns consistent with known regional water quality dynamics in New Brunswick. Our contribution, accessible to users with MATLAB, lies in the development of a transparent, adaptable, and reproducible CNN framework tailored for multiparameter water quality estimation, which extends beyond traditional empirical, site-specific regression models by enabling non-invasive, cost-effective, and continuous monitoring from satellite platforms over a large, heterogeneous province-scale domain. Additionally, model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis, which identified key spectral bands influencing predictions and provided ecological insights, offering guidance for future sensor design and data reduction strategies. This study addresses a significant research gap by providing a dual-parameter focused, end-to-end deep learning solution optimized for province-scale remote sensing data, facilitating more informed environmental management. This study can support water managers and agencies by providing province-wide DO and TOC maps derived from freely available Sentinel-2 imagery, reducing reliance on sparse field sampling alone and helping to identify areas of low oxygen or high organic carbon. Future work will extend this framework temporally and spatially and explore hybrid CNN architectures incorporating temporal dependencies for improved generalization and accuracy. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A customized end-to-end CNN, developed in MATLAB and trained on Sentinel-2 and in situ data, accurately and simultaneously predicts TOC and DO with R2 > 0.97, while generating spatially coherent, high-resolution maps over New Brunswick. The pipeline, including image mosaicking, k-means water masking, 4D spectral–spatial dataset construction, and memory-efficient patch-based inference, yields a robust, reproducible framework that successfully captures both optical and non-optical water quality dynamics. What are the implications of the main findings? Demonstrating reliable retrieval of both optically active and non-optically active parameters from freely available satellite data, the framework enables non-invasive, cost-effective, and continuous monitoring to support evidence-based management of freshwater systems. The incorporation of SHAP-based explainability identifies key Sentinel-2 bands driving DO and TOC predictions, guiding sensor/band selection and facilitating transfer of the method to other regions and indicators for operational water quality services. This study addresses the critical need for accurate, continuous monitoring of surface water quality parameters (SWQPs) using remote sensing, overcoming limitations in existing models that often rely on pre-trained networks ill-suited for complex aquatic environments. We present a customized convolutional neural network (CNN) architecture, implemented in the MATLAB environment, designed to simultaneously predict optically active (Total Organic Carbon, TOC) and non-optically active (Dissolved Oxygen, DO) parameters from eighteen Sentinel-2 Level-2A satellite images, acquired between 2023 and 2024. Our approach integrates spatial and spectral data through a customized CNN with three convolutional layers and two dense layers, optimized via adaptive learning strategies, data augmentation, and rigorous regularization to enhance predictive performance and prevent overfitting. The models were trained and validated on fused datasets of satellite imagery and in situ measurements, organized into comprehensive four-dimensional arrays capturing spectral, spatial, and sample dimensions. The results demonstrated high accuracy, with coefficient of determination (R2) values exceeding 0.97 and low root mean square error (RMSE) across training, validation, and testing subsets. Spatial prediction maps generated at high resolution revealed realistic ecological and hydrological patterns consistent with known regional water quality dynamics in New Brunswick. Our contribution, accessible to users with MATLAB, lies in the development of a transparent, adaptable, and reproducible CNN framework tailored for multiparameter water quality estimation, which extends beyond traditional empirical, site-specific regression models by enabling non-invasive, cost-effective, and continuous monitoring from satellite platforms over a large, heterogeneous province-scale domain. Additionally, model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis, which identified key spectral bands influencing predictions and provided ecological insights, offering guidance for future sensor design and data reduction strategies. This study addresses a significant research gap by providing a dual-parameter focused, end-to-end deep learning solution optimized for province-scale remote sensing data, facilitating more informed environmental management. This study can support water managers and agencies by providing province-wide DO and TOC maps derived from freely available Sentinel-2 imagery, reducing reliance on sparse field sampling alone and helping to identify areas of low oxygen or high organic carbon. Future work will extend this framework temporally and spatially and explore hybrid CNN architectures incorporating temporal dependencies for improved generalization and accuracy. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050794 |
