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Applications of Remote Sensing in Water Quality Assessment of Lakes, Rivers and Reservoirs

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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: 31 October 2025 | Viewed by 2599

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Special Issue Editors

Prof. Dr. Yuankun Wang

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Guest Editor

School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China
Interests: ecohydrology; ecohydraulic; environmental flow; river management; regulated river; ecological modelling
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Dr. Feng Huang

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Guest Editor

College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: ecohydrology; environmental water requirements; watershed management; rugulated rivers and lakes; ecological water diversion
Special Issues, Collections and Topics in MDPI journals

Dr. Zhenxing Zhang

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Guest Editor

Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
Interests: multi-objective optimizations; stochastic and deterministic hydrologic modelling; water resources systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In many regions of the world, lakes, rivers, and reservoirs are of key significance in purely environmental terms, but also given their relevance for human life and socio-economics activities. In this regard, human activities have contributed to the ongoing alterations in inland water bodies, e.g., the progressive lake water quality deterioration due to the over-exploitation of water resources and high-pollutant loadings. In many countries and regions, the observed water quality data of lakes, rivers, and reservoirs based on in situ measurements are not widely available due to its costs and the geographical restrictions. Satellite-based observation has been shown to be highly powerful in providing continuous worldwide records of water quality data and has developed rapidly in recent decades with increasingly higher temporal and spatial resolutions.

This Special Issue aims to collect contributions on remote sensing advances in the water quality assessment of lakes, rivers, and reservoirs, with the objective of providing the researcher and practitioner in hydrological and environmental sciences with compiled research on the state of the art in remote sensing application. We welcome any related studies on the proposed topic. For example, studies analyze water quality around the globe, discuss regional differences, and also present novel insights for inland water in given regions and novel methods in water quality retrieval. Water quality variables include, but are not limited to, water temperature, chlorophyll, etc. Analyses of satellite images via deep learning techniques are also welcome.

This Special Issue collects studies on water quality assessment via remote sensing. We seek methodological and application studies. The topics covered by this Special Issue include, but are not limited to, the following:

Water temperature reconstruction in inland water;
Chlorophyll reconstruction in inland water;
Remote sensing monitoring;
Water quality assessment;
Artificial intelligence for remote sensing data analysis;
Unmanned aerial vehicles.

Prof. Dr. Yuankun Wang
Dr. Feng Huang
Dr. Zhenxing Zhang
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

water temperature retrieval
chlorophyll retrieval
remote sensing monitoring
water quality assessment
artificial intelligence
unmanned aerial vehicles

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Published Papers (3 papers)

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Research

22 pages, 10719 KiB

Open AccessArticle

A Mobile Triaxial Stabilized Ship-Borne Radiometric System for In Situ Measurements: Case Study of Sentinel-3 OLCI Validation in Highly Turbid Waters

by Haoran Jiang, Peng Zhang, Hong Guan and Yongchao Zhao

Remote Sens. 2025, 17(7), 1223; https://doi.org/10.3390/rs17071223 - 29 Mar 2025

Viewed by 261

Abstract

This study presents the “Mobile Triaxial Stabilized Water-leaving Reflectance Measurement System” (MTS-WRMS), a ship-borne radiometric system designed for high-precision acquisition of water-leaving radiance (L_w) and remote sensing reflectance (R_rs) in mobile aquatic environments. The system employs a triaxial stabilized gimbal to maintain the orientation of three spectrometers, effectively mitigating angular deviations. The system also features automatic azimuth adjustment to maintain the relative sun-sensor azimuth angle within the optimal range of 90° ≤ φ ≤ 135° and supports long-range wireless telemetry for autonomous real-time monitoring. The system’s accuracy was validated through the “direct approach” experiments, which demonstrated low systematic bias, with a mean weighted absolute percentage deviation (WAPD) of 4.42% in the 440–720 nm range, which covers 90% of radiant energy. Additionally, ground validation involving 296 matched spectra from Gaoyou and Zhuhai revealed that Sentinel-3 A/B OLCI products tend to overestimate R_rs in highly turbid waters, with weighted percentage deviation (WPD) and WAPD values of about 16% and 31%, respectively. The overestimation was particularly pronounced in the 400–443 nm range, likely due to low R_rs and inadequate atmospheric correction. The MTS-WRMS provides an advanced tool for accurate, real-time R_rs measurements, offering valuable insights into temporal and spatial variations in water bodies. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Water Quality Assessment of Lakes, Rivers and Reservoirs)

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20 pages, 4144 KiB

Open AccessArticle

Fine Estimation of Water Quality in the Yangtze River Basin Based on a Geographically Weighted Random Forest Regression Model

by Fuliang Deng, Wenhui Liu, Mei Sun, Yanxue Xu, Bo Wang, Wei Liu, Ying Yuan and Lei Cui

Remote Sens. 2025, 17(4), 731; https://doi.org/10.3390/rs17040731 - 19 Feb 2025

Viewed by 511

Abstract

Water quality evaluation usually relies on limited state-controlled monitoring data, making it challenging to fully capture variations across an entire basin over time and space. The fine estimation of water quality in a spatial context presents a promising solution to this issue; however, traditional analyses often ignore spatial non-stationarity between variables. To solve the above-mentioned problems in water quality mapping research, we took the Yangtze River as our study subject and attempted to use a geographically weighted random forest regression (GWRFR) model to couple massive station observation data and auxiliary data to carry out a fine estimation of water quality. Specifically, we first utilized state-controlled sections’ water quality monitoring data as input for the GWRFR model to train and map six water quality indicators at a 30 m spatial resolution. We then assessed various geographical and environmental factors contributing to water quality and identified spatial differences. Our results show accurate predictions for all indicators: ammonia nitrogen (NH₃-N) had the lowest accuracy (R² = 0.61, RMSE = 0.13), and total nitrogen (TN) had the highest (R² = 0.74, RMSE = 0.48). The mapping results reveal total nitrogen as the primary pollutant in the Yangtze River basin. Chemical oxygen demand and the permanganate index were mainly influenced by natural factors, while total nitrogen and total phosphorus were impacted by human activities. The spatial distribution of critical influencing factors shows significant clustering. Overall, this study demonstrates the fine spatial distribution of water quality and provides insights into the influencing factors that are crucial for the comprehensive management of water environments. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Water Quality Assessment of Lakes, Rivers and Reservoirs)

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16 pages, 5889 KiB

Open AccessArticle

How Useful Are Moderate Resolution Imaging Spectroradiometer Observations for Inland Water Temperature Monitoring and Warming Trend Assessment in Temperate Lakes in Poland?

by Mariusz Sojka, Mariusz Ptak, Katarzyna Szyga-Pluta and Senlin Zhu

Remote Sens. 2024, 16(15), 2727; https://doi.org/10.3390/rs16152727 - 25 Jul 2024

Cited by 1 | Viewed by 961

Abstract

Continuous software development and widespread access to satellite imagery allow for obtaining increasingly accurate data on the natural environment. They play an important role in hydrosphere research, and one of the most frequently addressed issues in the era of climate change is the thermal dynamics of its components. Interesting research opportunities in this area are provided by the utilization of data obtained from the moderate resolution imaging spectroradiometer (MODIS). These data have been collected for over two decades and have already been used to study water temperature in lakes. In the case of Poland, there is a long history of studying the thermal regime of lakes based on in situ observations, but so far, MODIS data have not been used in these studies. In this study, the available products, such as 1-day and 8-day MODIS land surface temperature (LST), were validated. The obtained data were compared with in situ measurements, and the reliability of using these data to estimate long-term thermal changes in lake waters was also assessed. The analysis was conducted based on the example of two coastal lakes located in Poland. The results of 1-day LST MODIS generally showed a good fit compared to in situ measurements (average RMSE 1.9 °C). However, the analysis of long-term trends of water temperature changes revealed diverse results compared to such an approach based on field measurements. This situation is a result of the limited number of satellite data, which is dictated by environmental factors associated with high cloud cover reaching 60% during the analysis period. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Water Quality Assessment of Lakes, Rivers and Reservoirs)

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