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Remote Sensing Band Ratios for the Assessment of Water Quality

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 19180

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Special Issue Editors

Dr. Anita Simic-Milas

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Guest Editor

School of Earth, Environment and Society, Bowling Green State University, 190 Overman Hall, Bowling Green, OH 43403, USA
Interests: mapping biophysical and biochemical properties; precision agriculture; radiative transfer modeling; machine learning and AI; ecohydrology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yuhong He

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Guest Editor

Department of Geography, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada
Interests: remote sensing; geospatial analysis; ecosystem modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Inland and coastal waters have a significant impact on regional weather and climate due to their huge heat storage capacity and low albedo. Inland and coastal waters are vital for human survival and sustainable regional economic development. The retrieval of surface water quality information on a large scale using remote sensing data is a powerful approach in monitoring changes in water quality parameters such as chlorophyll and phytoplankton pigments, nutrients, total suspended matter, and dissolved organic matter. However, water quality monitoring using satellite remote sensing remains challenging due to the low signal-to-noise ratio (SNR) and limited instrument resolution. While remote sensing band ratios including vegetation indices, following qualitative and quantitative field data collection, are effective methods for the retrieval of some water parameters, it has become evident that the retrieval of other parameters using an empirical modeling scenario is limited. With the recent development of spaceborne and airborne hyperspectral and multispectral technology, in combination with various artificial intelligence (AI) modeling approaches, the retrieval methods are becoming advanced and sophisticated.

In this context, this Special Issue is seeking contributions involving the monitoring of water quality using different remote sensing techniques based on band ratios including vegetation indices. We welcome papers that address retrieval methods of the chlorophyll content, harmful algal blooms (HABs), and other water-related parameters using empirical and/or non-parametric regression models, such as machine learning and AI.

Dr. Anita Simic-Milas
Prof. Dr. Yuhong He
Guest Editors

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Keywords

vegetation indices for water quality
empirical methods in monitoring inland and coastal waters
feature extraction techniques and machine learning methods for water quality
optimal wavelength(s) to measure water quality parameters
multispectral and hyperspectral imagery for water quality using band ratios
optimal sampling methods and data collection strategies in monitoring water quality
role of spatial resolution of imagery in mapping water quality parameters

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Published Papers (7 papers)

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Research

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24 pages, 12756 KiB

Open AccessArticle

An Empirical Algorithm for Estimating the Absorption of Colored Dissolved Organic Matter from Sentinel-2 (MSI) and Landsat-8 (OLI) Observations of Coastal Waters

by Vu Son Nguyen, Hubert Loisel, Vincent Vantrepotte, Xavier Mériaux and Dinh Lan Tran

Remote Sens. 2024, 16(21), 4061; https://doi.org/10.3390/rs16214061 - 31 Oct 2024

Cited by 1 | Viewed by 1472

Abstract

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, a_cdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating a_cdom at the wavelength 412 nm (a_cdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, R_rs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite R_rs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate a_cdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m⁻¹ from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived a_cdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of a_cdom (412) is provided for a coastal transect in the south of Vietnam. Full article

(This article belongs to the Special Issue Remote Sensing Band Ratios for the Assessment of Water Quality)

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20 pages, 11776 KiB

Open AccessArticle

Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile

by Lien Rodríguez-López, Lisandra Bravo Alvarez, Iongel Duran-Llacer, David E. Ruíz-Guirola, Samuel Montejo-Sánchez, Rebeca Martínez-Retureta, Ernesto López-Morales, Luc Bourrel, Frédéric Frappart and Roberto Urrutia

Remote Sens. 2024, 16(18), 3401; https://doi.org/10.3390/rs16183401 - 13 Sep 2024

Cited by 2 | Viewed by 2185

Abstract

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R² = 0.89, an RSME of 0.32 µg/L, an MAE 1.25 µg/L and an MSE 0.25 (µg/L)², consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R² = 0.96; MSE = 0.33 (µg/L)²; RMSE = 0.13 µg/L; and MAE = 0.06 µg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management. Full article

(This article belongs to the Special Issue Remote Sensing Band Ratios for the Assessment of Water Quality)

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22 pages, 3742 KiB

Open AccessArticle

LAQUA: a LAndsat water QUality retrieval tool for east African lakes

by Aidan Byrne, Davide Lomeo, Winnie Owoko, Christopher Mulanda Aura, Kobingi Nyakeya, Cyprian Odoli, James Mugo, Conland Barongo, Julius Kiplagat, Naftaly Mwirigi, Sean Avery, Michael A. Chadwick, Ken Norris, Emma J. Tebbs and on behalf of the NSF-IRES Lake Victoria Research Consortium

Remote Sens. 2024, 16(16), 2903; https://doi.org/10.3390/rs16162903 - 8 Aug 2024

Viewed by 2678

Abstract

East African lakes support the food and water security of millions of people. Yet, a lack of continuous long-term water quality data for these waterbodies impedes their sustainable management. While satellite-based water quality retrieval methods have been developed for lakes globally, African lakes are typically underrepresented in training data, limiting the applicability of existing methods to the region. Hence, this study aimed to (1) assess the accuracy of existing and newly developed water quality band algorithms for East African lakes and (2) make satellite-derived water quality information easily accessible through a Google Earth Engine application (app), named LAndsat water QUality retrieval tool for east African lakes (LAQUA). We collated a dataset of existing and newly collected in situ surface water quality samples from seven lakes to develop and test Landsat water quality retrieval models. Twenty-one published algorithms were evaluated and compared with newly developed linear and quadratic regression models, to determine the most suitable Landsat band algorithms for chlorophyll-a, total suspended solids (TSS), and Secchi disk depth (SDD) for East African lakes. The three-band algorithm, parameterised using data for East African lakes, proved the most suitable for chlorophyll-a retrieval (R² = 0.717, p < 0.001, RMSE = 22.917 μg/L), a novel index developed in this study, the Modified Suspended Matter Index (MSMI), was the most accurate for TSS retrieval (R² = 0.822, p < 0.001, RMSE = 9.006 mg/L), and an existing global model was the most accurate for SDD estimation (R² = 0.933, p < 0.001, RMSE = 0.073 m). The LAQUA app we developed provides easy access to the best performing retrieval models, facilitating the use of water quality information for management and evidence-informed policy making for East African lakes. Full article

(This article belongs to the Special Issue Remote Sensing Band Ratios for the Assessment of Water Quality)

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26 pages, 6289 KiB

Open AccessArticle

Comparative Evaluation of Semi-Empirical Approaches to Retrieve Satellite-Derived Chlorophyll-a Concentrations from Nearshore and Offshore Waters of a Large Lake (Lake Ontario)

by Ali Reza Shahvaran, Homa Kheyrollah Pour and Philippe Van Cappellen

Remote Sens. 2024, 16(9), 1595; https://doi.org/10.3390/rs16091595 - 30 Apr 2024

Cited by 5 | Viewed by 2586

Abstract

Chlorophyll-a concentration (Chl-a) is commonly used as a proxy for phytoplankton abundance in surface waters of large lakes. Mapping spatial and temporal Chl-a distributions derived from multispectral satellite data is therefore increasingly popular for monitoring trends in trophic state of these important ecosystems. We evaluated products of eleven atmospheric correction processors (LEDAPS, LaSRC, Sen2Cor, ACOLITE, ATCOR, C2RCC, DOS 1, FLAASH, iCOR, Polymer, and QUAC) and 27 reflectance indexes (including band-ratio, three-band, and four-band algorithms) recommended for Chl-a concentration retrieval. These were applied to the western basin of Lake Ontario by pairing 236 satellite scenes from Landsat 5, 7, 8, and Sentinel-2 acquired between 2000 and 2022 to 600 near-synchronous and co-located in situ-measured Chl-a concentrations. The in situ data were categorized based on location, seasonality, and Carlson’s Trophic State Index (TSI). Linear regression Chl-a models were calibrated for each processing scheme plus data category. The models were compared using a range of performance metrics. Categorization of data based on trophic state yielded improved outcomes. Furthermore, Sentinel-2 and Landsat 8 data provided the best results, while Landsat 5 and 7 underperformed. A total of 28 Chl-a models were developed across the different data categorization schemes, with RMSEs ranging from 1.1 to 14.1 μg/L. ACOLITE-corrected images paired with the blue-to-green band ratio emerged as the generally best performing scheme. However, model performance was dependent on the data filtration practices and varied between satellites. Full article

(This article belongs to the Special Issue Remote Sensing Band Ratios for the Assessment of Water Quality)

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28 pages, 13381 KiB

Open AccessArticle

Retrieval of Total Suspended Matter Concentration Based on the Iterative Analysis of Multiple Equations: A Case Study of a Lake Taihu Image from the First Sustainable Development Goals Science Satellite’s Multispectral Imager for Inshore

by Xueke Hu, Jiaguo Li, Yuan Sun, Yunfei Bao, Yonghua Sun, Xingfeng Chen and Yueguan Yan

Remote Sens. 2024, 16(8), 1385; https://doi.org/10.3390/rs16081385 - 14 Apr 2024

Cited by 3 | Viewed by 1664

Abstract

Inland waters consist of multiple concentrations of constituents, and solving the interference problem of chlorophyll-a and colored dissolved organic matter (CDOM) can help to accurately invert total suspended matter concentration (

C_{tsm}

). In this study, according to the characteristics of the [...] Read more.

C_{tsm}

). In this study, according to the characteristics of the Multispectral Imager for Inshore (MII) equipped with the first Sustainable Development Goals Science Satellite (SDGSAT-1), an iterative inversion model was established based on the iterative analysis of multiple linear regression to estimate

C_{tsm}

. The Hydrolight radiative transfer model was used to simulate the radiative transfer process of Lake Taihu, and it analyzed the effect of three component concentrations on remote sensing reflectance. The characteristic band combinations

B_{6 / 3}

and

B_{6 / 5}

for multiple linear regression were determined using the correlation of the three component concentrations with different bands and band combinations. By combining the two multiple linear regression models, a complete closed iterative inversion model for solving

C_{tsm}

was formed, which was successfully verified by using the modeling data (R² = 0.97, RMSE = 4.89 g/m³, MAPE = 11.48%) and the SDGSAT-1 MII image verification data (R² = 0.87, RMSE = 3.92 g/m³, MAPE = 8.13%). And it was compared with iterative inversion models constructed based on other combinations of feature bands and other published models. Remote sensing monitoring

C_{tsm}

was carried out using SDGSAT-1 MII images of Lake Taihu in 2022–2023. This study can serve as a technical reference for the SDGSAT-1 satellite in terms of remote sensing monitoring of

C_{tsm}

, as well as monitoring and improving the water environment. Full article

(This article belongs to the Special Issue Remote Sensing Band Ratios for the Assessment of Water Quality)

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25 pages, 133890 KiB

Open AccessArticle

Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction

by John Waczak, Adam Aker, Lakitha O. H. Wijeratne, Shawhin Talebi, Ashen Fernando, Prabuddha M. H. Dewage, Mazhar Iqbal, Matthew Lary, David Schaefer and David J. Lary

Remote Sens. 2024, 16(6), 996; https://doi.org/10.3390/rs16060996 - 12 Mar 2024

Cited by 4 | Viewed by 2050

Abstract

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions

{Ca}^{2 +}

{Cl}^{-}

, and

{Na}^{+}

. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition. Full article

(This article belongs to the Special Issue Remote Sensing Band Ratios for the Assessment of Water Quality)

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Review

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30 pages, 1419 KiB

Open AccessReview

Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management

by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang and Bahram Gharabaghi

Remote Sens. 2024, 16(22), 4196; https://doi.org/10.3390/rs16224196 - 11 Nov 2024

Cited by 2 | Viewed by 4967

Abstract

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring and management of lake water quality. It critically evaluates the performance of various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, and Hyperion, in assessing key water quality parameters including chlorophyll-a (Chl-a), turbidity, and colored dissolved organic matter (CDOM). This review highlights the specific advantages of each satellite platform, considering factors like spatial and temporal resolution, spectral coverage, and the suitability of these platforms for different lake sizes and characteristics. In addition to remote sensing platforms, this paper explores the application of a wide range of machine learning models, from traditional linear and tree-based methods to more advanced deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). These models are analyzed for their ability to handle the complexities inherent in remote sensing data, including high dimensionality, non-linear relationships, and the integration of multispectral and hyperspectral data. This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. Moreover, this paper identifies and discusses the key challenges associated with data quality, model interpretability, and integrating remote sensing imagery with machine learning models. It emphasizes the need for advancements in data fusion techniques, improved model generalizability, and the developing robust frameworks for integrating multi-source data. This review concludes by offering targeted recommendations for future research, highlighting the potential of interdisciplinary collaborations to enhance the application of these technologies in sustainable lake water quality management. Full article

(This article belongs to the Special Issue Remote Sensing Band Ratios for the Assessment of Water Quality)

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