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Remote Sensing Band Ratios for the Assessment of Water Quality

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2367

Special Issue Editors


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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

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

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • 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

Published Papers (3 papers)

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Research

26 pages, 6289 KiB  
Article
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
Viewed by 481
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 [...] Read more.
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  
Article
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
Viewed by 637
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 (Ctsm). In this study, according to the characteristics of the [...] Read more.
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 (Ctsm). 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 Ctsm. 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 B6/3 and B6/5 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 Ctsm 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 Ctsm 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 Ctsm, 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  
Article
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
Viewed by 819
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 [...] Read more.
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 Ca2+, 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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