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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 5164

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Special Issue Editors

Dr. Xuejiao Hou

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Guest Editor

School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou, China
Interests: Inland lakes; estuarine and coastal zones; water environment remote sensing; quantitative retrieval of water quality parameters; lake algal blooms; aquatic vegetation

Dr. Shangbo Yang

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Guest Editor

School of Geography and Remote Sensing, Guangzhou University, Guangzhou, China
Interests: dominant algal species identification via remote sensing; mechanistic drivers of coastal algal blooms; water quality parameter retrieval

Dr. Jilin Men

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Guest Editor

Department of Geography and the Environment, The University of Alabama, Tuscaloosa, AL 35487, USA
Interests: water quality of inland waters; atmospheric correction; deep learning
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Special Issue Information

Dear Colleagues,

Good water quality is critical for sustaining aquatic biodiversity and ensuring the safety of drinking water. However, accurately monitoring water quality across broad spatial and temporal scales remains a significant challenge, as traditional in situ measurements typically yield limited data points and are often time-consuming and labor-intensive, lacking the capacity to resolve the spatiotemporal dynamics of pollutant transport and its sources. Space-based remote sensing technologies (including satellite and airborne platforms) have made it possible to monitor a wide range of water quality parameters on a large spatial scale, including both optically active constituents (e.g., chlorophyll-a, suspended sediment concentration, turbidity, colored dissolved organic matter, and water clarity), and even non-optically parameters (e.g., total nitrogen, total phosphorus, and dissolved oxygen) through advanced retrieval algorithms. These technologies enable repeatable, cost-effective, and synoptic observations, making them indispensable for both long-term water quality monitoring and for rapid responses to emerging threats such as water pollution events. Emerging ground-based monitoring technologies, particularly proximal sensing systems and citizen-contributed smartphone platforms, provide valuable high-resolution observations that complement large-scale remote sensing and significantly enhance traditional monitoring through localized data acquisition. The integration of multi-source data, combining space-based remote sensing, ground-based proximal sensing, and public participation monitoring, represents the future of comprehensive water quality assessments. When combined with universal retrieval algorithms, this integrated approach enables operational, near-real-time water quality assessment while improving both the monitoring accuracy and the spatial–temporal coverage. These technological advancements facilitate the early detection of risks in drinking water supplies, strengthen safety oversight and operational efficiency, and deepen our understanding of aquatic ecosystem responses to climate change.

This Special Issue aims to bring together cutting-edge studies that advance the use of remote sensing technologies in water quality monitoring across inland, coastal, and estuarine environments.

We invite submissions on a wide range of topics, including, but not limited to, the following: retrieval algorithms for water quality parameters, algorithm validation and uncertainty assessment, multi-source data integration, time series analysis, atmospheric correction for aquatic environment, eutrophication monitoring, pollution detection, and applications in water resource management. We welcome original research articles, reviews, and case studies that demonstrate practical applications or methodological advancements in remote sensing for water quality.

Dr. Xuejiao Hou
Dr. Shangbo Yang
Dr. Jilin Men
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing
water quality
water clarity
eutrophication
water turbidity
atmospheric correction
algal bloom
climate change
multi-source integrated observation
inherent/apparent optical properties

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Published Papers (6 papers)

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Research

32 pages, 8390 KB

Open AccessArticle

End-to-End Customized CNN Pipeline for Multiparameter Surface Water Quality Estimation from Sentinel-2 Imagery

by Essam Sharaf El Din, Karim M. El Zahar and Ahmed Shaker

Remote Sens. 2026, 18(5), 794; https://doi.org/10.3390/rs18050794 - 5 Mar 2026

Viewed by 501

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing in Water Quality Monitoring)

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31 pages, 4226 KB

Open AccessArticle

Enhancement of Cyanobacterial Bloom Monitoring in Lake Taihu Using Dual Red-Edge Bands of GF-6/WFV: Multi-Dimensional Feature Combination and Extraction Accuracy Analysis

by Yunxiao Sun, Ruolin Zhang, Chunhong Zhao, Qingyan Meng, Zhenhui Sun, Jialong Wang, Jun Wu, Yao Wang, Decai Gao and Shuyi Guan

Remote Sens. 2026, 18(4), 653; https://doi.org/10.3390/rs18040653 - 20 Feb 2026

Viewed by 493

Abstract

Cyanobacterial blooms pose a serious threat to freshwater ecosystems, necessitating accurate remote sensing monitoring. Although red-edge bands show potential in terrestrial monitoring, their multi-dimensional features (i.e., spectral, textural, and index-based characteristics) remain underutilized for aquatic blooms. This study leverages the dual red-edge bands (710 nm and 750 nm) of GF-6/WFV to enhance cyanobacterial bloom identification in Lake Taihu. Multi-temporal images from 2019–2023 were used to construct red-edge features in three dimensions: spectral (evaluated via adaptive band selection method) and Jeffries–Matusita–Bhattacharyya distance), texture (based on Gray Level Co-occurrence Matrix and principal component analysis), and indices (nine vegetation indices ranked by Random Forest importance). Twelve feature-combination schemes were designed and implemented with a Random Forest classifier. Results show that red-edge features consistently improve identification accuracy. Quantitatively, compared to the basic four-band (RGBN) combination, the 710 nm band improved spectral separability by an average of 9.63%, whereas the 750 nm band yielded a lower average improvement of 5.69%. Red-edge indices, especially the modified chlorophyll absorption reflectance index 1 (MCARI1) and normalized difference red-edge index (NDRE), exhibited higher importance than non-red-edge indices. All schemes incorporating red-edge features achieved mean overall accuracies of 92.8–94.9% and Kappa coefficients of 0.86–0.94, surpassing the basic four-band scheme. Among these features, red-edge indices contributed most significantly to accuracy gains, increasing the overall accuracy by an average of 0.36–6.06% and the Kappa coefficient by up to 0.06. The enhancement effect of the red-edge 710 nm band features was superior to that of the 750 nm band. This study demonstrates that multi-dimensional red-edge features effectively enhance the identification accuracy of cyanobacterial blooms and provides a methodological reference for operational GF-6 applications in water quality monitoring. Full article

(This article belongs to the Special Issue Remote Sensing in Water Quality Monitoring)

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25 pages, 9023 KB

Open AccessArticle

A Comparison of Non-Contact Methods for Measuring Turbidity in the Colorado River

by Natalie K. Day, Tyler V. King and Adam R. Mosbrucker

Remote Sens. 2026, 18(4), 638; https://doi.org/10.3390/rs18040638 - 18 Feb 2026

Viewed by 603

Abstract

Monitoring suspended-sediment concentration (SSC) is essential to better understand how sediment transport could adversely affect water availability for human communities and ecosystems. Aquatic remote sensing methods are increasingly utilized to estimate SSC and turbidity in rivers; however, an evaluation of their quantitative performance is limited. This study evaluates the performance of three multispectral sensors, which vary in resolution and ease of deployment, to estimate turbidity in the Colorado River: the Multispectral Instrument (MSI) on board the European Space Agency’s Sentinel-2 satellite, an industrial-grade 10-band dual camera system mounted on a cable car, and a consumer-grade 6-band dual camera system positioned on the riverbank. We use multivariate linear regression to compare in situ turbidity measurements with concurrent spectral reflectance data from each sensor. Models for all three sensors selected similar spectral information and resulted in mean errors <35% in predicting turbidity. A cross-sensor comparison showed that little accuracy is lost when applying models developed for satellite-based systems to ground-based systems, and vice versa. Transferability of satellite-based models to ground-based systems could support continuous water-quality monitoring between satellite overpasses and avoid issues associated with cloud interference. Conversely, continuously operating ground-based systems could be used to rapidly establish datasets and models for application in satellite imagery, thus accelerating remote sensing applications. The encouraging performance of the consumer-grade system indicates that SSC could be monitored for low cost. Full article

(This article belongs to the Special Issue Remote Sensing in Water Quality Monitoring)

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20 pages, 2880 KB

Open AccessArticle

Development and Calibration of Sentinel-2 Spectral Indices for Water Quality Parameter Estimation in Alqueva Reservoir, Southern Portugal

by Vítor H. Neves, Lisette Sánchez-Pérez, Sara C. Antunes, Giorgio Pace, Xavier Sòria-Perpinyà and Jesús Delegido

Remote Sens. 2026, 18(3), 469; https://doi.org/10.3390/rs18030469 - 2 Feb 2026

Cited by 1 | Viewed by 559

Abstract

Monitoring water quality in large reservoirs is essential yet challenging, particularly in regions with limited in situ coverage. This study presents a robust methodology for integrating a decade-long in situ dataset (2014–2022) with Sentinel-2 multispectral imagery to develop and validate localized algorithms for water quality assessment in the Alqueva Reservoir, the largest artificial lake in Western Europe. Three atmospheric correction algorithms (C2RCC, C2X, C2X-COMPLEX) were evaluated, with C2RCC-COMPLEX identified as the most suitable for capturing the reservoir’s optical complexity, yielding the lowest RMSE for Total Suspended Solids (TSS: 2.4 g/m³) and Secchi Disk Depth (SDD: 0.85 m). Empirical models using Sentinel-2 bands 7 (783 nm), 6 (740 nm), and 8A (865 nm) demonstrated strong correlations (R² ≈ 0.69–0.71) for Chlorophyll-a (Chl-a) with a range data of 0.1–65 mg/m³, TSS with a range data of 2–13.1 g/m³, and SDD with a range data of 0.4–8 m. Spatially explicit water quality maps illustrate the models’ capacity to capture distinct gradients and seasonal dynamics, e.g., elevated Chl-a (>30 mg/m³) and TSS (>7.5 g/m³) in the reservoir’s nutrient-rich northern section during drought (August 2022), and more uniform conditions following winter recovery (March 2019), with SDD exceeding 2 m near the dam. These results underscore the utility of Sentinel-2 for resolving spatial and temporal variability in optically complex inland waters. The proposed workflow offers a transferable, cost-effective framework for monitoring eutrophication risks and sediment dynamics under increasing hydrological variability. Full article

(This article belongs to the Special Issue Remote Sensing in Water Quality Monitoring)

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27 pages, 5970 KB

Open AccessArticle

SIGMaL: An Integrated Framework for Water Quality Monitoring in a Coastal Shallow Lake

by Anja Batina, Ante Šiljeg, Andrija Krtalić and Ljiljana Šerić

Remote Sens. 2026, 18(2), 312; https://doi.org/10.3390/rs18020312 - 16 Jan 2026

Cited by 1 | Viewed by 513

Abstract

Coastal lakes require monitoring approaches that capture spatial and temporal variability beyond the limits of conventional in situ measurements. In this study, a SIGMaL framework (Satellite–In situ–GIS-multicriteria decision analysis (MCDA)–Machine Learning (ML)) was developed, a unified methodology that integrates in situ monitoring, GIS MCDA-derived water quality index (WQI), satellite imagery, and ML models for comprehensive coastal lake water quality assessment. A WQI, derived from a 12-month series of in situ measurements and environmental parameters, was used alongside four physicochemical parameters measured by a multiparameter probe. First, satellite reflectance from each sensor was used to train a set of nine regression models for modelling electrical conductivity (EC), turbidity, water temperature (WT), and dissolved oxygen (DO). Second, convolutional neural networks (CNNs) with spectral and temporal inputs were trained to classify WQI classes, enabling a cross-sensor evaluation of their suitability for lake water quality monitoring. Third, the trained CNNs were applied to generate WQI maps for a subsequent 12-month period without in situ data. Across all analyses, WQI-based models provided more stable and accurate models than those trained on raw parameters. Sentinel-2 achieved the most consistent WQI performance (AUC ≈ 1.00, R² ≈ 0.84), PlanetScope captured fine-scale spatial detail (R² ≈ 0.77), while Landsat 8–9 was most effective for WT but less reliable for multi-class WQI discrimination. Sentinel-2 is recommended as the primary satellite sensor for WQI mapping within the SIGMaL framework. These findings demonstrate the advantages of WQI-based modelling and highlight the potential of ML–remote sensing integration to support coastal lake water quality monitoring. Full article

(This article belongs to the Special Issue Remote Sensing in Water Quality Monitoring)

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29 pages, 12119 KB

Open AccessArticle

Method for Obtaining Water-Leaving Reflectance from Unmanned Aerial Vehicle Hyperspectral Remote Sensing Based on Air–Ground Collaborative Calibration for Water Quality Monitoring

by Hong Liu, Xingsong Hou, Bingliang Hu, Tao Yu, Zhoufeng Zhang, Xiao Liu, Xueji Wang and Zhengxuan Tan

Remote Sens. 2025, 17(20), 3413; https://doi.org/10.3390/rs17203413 - 12 Oct 2025

Viewed by 1405

Abstract

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring. Full article

(This article belongs to the Special Issue Remote Sensing in Water Quality Monitoring)

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