Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Monitoring Surface Water Dynamics and Quality Using Modern Remote Sensing

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 2026 | Viewed by 1662

Share This Special Issue

Special Issue Editors

Dr. Sensen Wu

E-Mail Website
Guest Editor

School of Earth Sciences, Zhejiang University, Zhejiang 310058, China
Interests: marine big data and earth system science; geospatial artificial intelligence (GeoAI); spatial modelling and spatiotemporal prediction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hua Su

E-Mail Website
Guest Editor

The Academy of Digital China, Fuzhou University, Fuzhou 350108, China
Interests: ocean remote sensing; coastal remote sensing; deep-ocean remote sensing; global climate change; AI oceanography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Surface water bodies—including rivers, lakes, reservoirs, wetlands, coasts, and oceans—play a fundamental role in sustaining ecosystems, supporting human societies, and regulating the Earth’s climate system. However, these environments face growing pressures from climate change, population growth, industrialization, and intensified land–sea interactions. Monitoring both the dynamics and quality of surface waters, across inland and marine environments, is therefore critical for water resource management, ecological health, disaster risk reduction, and sustainable development.

Modern remote sensing technologies are revolutionizing how we observe surface waters at multiple scales. Satellite, airborne, and UAV-based platforms equipped with multispectral, hyperspectral, thermal, SAR, and LiDAR sensors now provide unprecedented capabilities to monitor spatial–temporal variability and water quality indicators such as turbidity, chlorophyll, harmful algal blooms, suspended sediments, salinity, temperature, and nutrients. Meanwhile, machine learning, artificial intelligence, and cloud computing enable more accurate retrievals, automated mapping, and near-real-time applications that bridge science and decision-making.

For this Special Issue, we invite contributions that advance the monitoring and understanding of surface water dynamics and quality in both inland and oceanic systems using modern remote sensing approaches. Topics of interest include, but are not limited to, the following:

Remote sensing of inland and ocean surface water extent, dynamics, and variability;
Monitoring water quality indicators: turbidity, chlorophyll, algal blooms, sediments, salinity, temperature, nutrients;
Integration of multi-source observations (satellite, UAV, airborne, in situ) for water studies;
AI, machine learning, and cloud-based methods for water monitoring and forecasting;
Applications in flood, drought, water resource, and coastal management;
Advances in SAR, hyperspectral, LiDAR, and thermal sensing of aquatic environments;
Long-term monitoring of climate change and anthropogenic impacts on inland and ocean waters;
Case studies and applications in lakes, rivers, reservoirs, wetlands, coastal, and marine systems.

We welcome submissions of original research, reviews, case studies, methodological papers, and short communications.

Dr. Sensen Wu
Prof. Dr. Hua Su
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

surface water dynamics
water quality monitoring
inland and ocean remote sensing
hyperspectral and multispectral imaging
SAR and LiDAR applications
AI and machine learning in water studies
climate and anthropogenic impacts
hydrology and water resources
aquatic ecosystems
UAV and satellite observations

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

20 pages, 16597 KB

Open AccessArticle

Risk Assessment of Potential Black and Odorous Water Body Based on Satellite and UAV Multispectral Remote Sensing

by Yuan Jiang, Zili Zhang, Yulan Yuan, Yin Yang, Yuling Xu and Wei Ding

Remote Sens. 2026, 18(7), 1029; https://doi.org/10.3390/rs18071029 - 29 Mar 2026

Viewed by 392

Abstract

Satellite remote sensing offers a cost-effective solution for the continuous monitoring of black and odorous water bodies (BOWs). However, limitations in spatial and spectral resolution hinder the quantitative inversion of water quality parameters and the precise assessment of risk levels using satellite data alone. To address this challenge, this study proposes a synergistic approach combining satellite and Unmanned Aerial Vehicle (UAV) remote sensing to rapidly identify potentially polluted water bodies and quantitatively assess their risk levels. First, a Black and Odorous Water Index (MBOWI) was constructed based on reflectance characteristics in the visible to near-infrared bands to screen for potential black and odorous water bodies using satellite imagery. Subsequently, high-resolution multispectral UAV imagery, integrated with in situ sampling data, was employed to develop machine learning models for inverting key water quality parameters, including Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Total Phosphorus (TP) and Ammonia Nitrogen (NH₃-N). Comparative analysis of Polynomial Regression (PR), Random Forest (RF), and Simulated Annealing-optimized Support Vector Regression (SA-SVR) revealed that RF and SA-SVR exhibited superior performance in inverting four non-optically active water quality parameters due to their robust nonlinear fitting capabilities, with the mean Adjusted Coefficient of Determination (

R_{a d j}^{2}

) ranging from 0.57 to 0.69. Water quality classification based on the single-factor worst-case method achieved an overall accuracy of 0.70 across validation samples. Notably, for Class V (heavily polluted) water bodies, both classification accuracy and recall rate reached 0.89, demonstrating the model’s high precision in identifying high-risk waters. Finally, the proposed framework was applied to northern Zhejiang Province to assess seven potential black and odorous water bodies, successfully identifying four as high-risk and one as low-risk. This study validates satellite and UAV synergistic remote sensing for the hierarchical risk management of black and odorous water bodies. Full article

(This article belongs to the Special Issue Monitoring Surface Water Dynamics and Quality Using Modern Remote Sensing)

► Show Figures

Figure 1

27 pages, 24065 KB

Open AccessArticle

Enhancing Chlorophyll-a Estimation in Optically Complex Waters Using ZY-1 02E Hyperspectral Imagery: An Integrated Approach Combining Optical Classification and Multi-Index Blending Models

by Congxiang Yan, Xin Fu, Hailiang Gao, Wen Dong, Zhen Liu and Zhenghe Xu

Remote Sens. 2025, 17(23), 3795; https://doi.org/10.3390/rs17233795 - 22 Nov 2025

Cited by 3 | Viewed by 843

Abstract

Chlorophyll-a (Chl-a) concentration is a key parameter for assessing the degree of eutrophication and the algal bloom risk in water bodies. Accurate and robust monitoring of Chl-a is crucial for effective water quality management of inland and coastal optically complex Case-II waters. This study proposes a stratified integrated framework that combines optical water type (OWT) classification and multi-index blending models and evaluates the capability of ZY-1 02E hyperspectral imagery in the retrieval of Chl-a concentration in Case-II waters. This research is based on ZY-1 02E hyperspectral remote sensing images and ground synchronous measurement data from four typical water bodies in China (Dongpu Reservoir, Nanyi Lake, Tangdao Bay, and Moon-lake Reservoir). Using Fuzzy C-Means (FCM) clustering combined with spectral feature analysis, three different OWTs were identified, and the bands sensitive to Chl-a for each water type were recognized. Subsequently, the most suitable semi-empirical indices (BR, TBI) were selected, and a new suspended matter correction index (SMCI) was constructed by integrating spectral bands and TSM data specifically for high-turbidity waters to facilitate the retrieval of Chl-a concentration. The RMSE and MAPE of the model constructed based on the unclassified dataset were 3.1586 μg·L⁻¹ and 30.82%, respectively. When the stratified ensemble method based on optical water type classification was employed, the overall RMSE and MAPE were reduced to 1.5832 μg·L⁻¹ and 16.36%. The results demonstrate that this hierarchical ensemble framework significantly improved the retrieval accuracy of Chl-a concentration. An uncertainty assessment of the Chl-a retrieval model for highly turbid waters incorporating SMCI was conducted using the Monte Carlo method, revealing a mean coefficient of variation of 0.0567 and a coverage rate of 95.65% for the 95% confidence interval, indicating high predictive stability and reliability of the model. This study emphasizes the importance of the integrated framework strategy that combines OWTs classification and multi-index blending models for accurate and robust remote sensing estimation of Chl-a concentration under optically complex environmental conditions. It confirms the application potential of ZY-1 02E hyperspectral data in monitoring Chl-a in inland and near-coastal waters at medium and small scales. Full article

(This article belongs to the Special Issue Monitoring Surface Water Dynamics and Quality Using Modern Remote Sensing)

► Show Figures

Journal Menu

Journal Browser

Monitoring Surface Water Dynamics and Quality Using Modern Remote Sensing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (2 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI