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River and Lake Dynamic Monitoring and Ecological Assessment Based on Remote Sensing

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 6792

Special Issue Editors

Dr. Yingxin Shang

E-Mail Website
Guest Editor
Remote Sensing and Geographic Information Center, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: remote sensing of optical properties and carbon cycle for inland water; water quality monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Zhidan Wen

E-Mail Website
Guest Editor
Remote Sensing and Geographic Information Center, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: inland water carbon cycle; remote sensing of greenhouse gases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Inland water bodies around the world, such as lakes and rivers, play a crucial role in sustaining life, providing human well-being, supporting ecosystems, and ensuring water security for millions of people worldwide. However, in recent years, these valuable resources have experienced increasing pressure under the background of climate change, population growth, urbanization, and industrial activities. The ability to comprehensively monitor and assess the ecological status and dynamics of lakes and rivers from a local to global scale using remote sensing has become a critical challenge for hydrological, ecological, and environmental researchers, managers, and policy makers.

Remote sensing has emerged as a powerful tool in addressing these above mentioned challenges. Remote sensing technologies, encompassing optical, thermal, radar, and lidar sensors aboard satellites and other platforms, offer the capability to acquire frequent, synoptic, and multidimensional data across large geographic areas over a long period with a given revisit frequency. Overall, the obtained water-related information and ecological assessments for lakes and rivers can further support water resource monitoring, assessment, management, and policy making for inland waters.

This Special Issue aims to present studies that address the various uses of remote sensing data and techniques in water quality, water quantity, hydrology monitoring, and ecological assessment for inland waters; it also highlights the recent advancements in the use of remote sensing to assess water cycle processes, with a particular focus on hydrological and water quality parameters.

“River and Lake Dynamic Monitoring and Ecological Assessment Based on Remote Sensing” is one of the typical application scenarios of remote sensing technology. Research in this direction will promote the development and application of remote sensing technology. We encourage submissions on innovative methodologies of data analysis that can handle multimission and multisource remote sensing data for monitoring the spatio-temporal dynamics of extreme hydrological events and/or water quality variations and assessing their impacts on ecosystems.

  • Water cycle, climate, greenhouse gases, and ecosystems;
  • Data-driven hydrologic process learning;
  • Intelligent extraction of water information with remote sensing techniques;
  • Remote sensing inversion models of water quality parameters;
  • Water pollution identification with remote sensing techniques;
  • Novel application of remote sensing techniques in water resources and water environment monitoring;
  • Climate or human-induced spatio-temporal variation of water quality in coastal, estuarine, and inland waters;
  • Applications of artificial intelligence (AI) and/or machine learning approaches;
  • Time series of remote sensing data for long-term analyses;
  • Application of remote sensing techniques for ecological assessment and carbon cycles.

Dr. Yingxin Shang
Dr. Zhidan Wen
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

  • remote sensing
  • spatial and temporal variation
  • optical properties
  • water quality
  • natural and anthropogenic influences
  • hydrological processes
  • ecological risk assessment
  • climate change
  • carbon cycles
  • sustainable water management

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Published Papers (6 papers)

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Research

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29 pages, 3645 KiB  
Article
Estimations of Dynamic Water Depth and Volume of Global Lakes Using Machine Learning
by Yunzhe Lv, Li Jia, Massimo Menenti, Chaolei Zheng, Jing Lu, Min Jiang, Qiting Chen and Yiqing Zhang
Remote Sens. 2025, 17(6), 1052; https://doi.org/10.3390/rs17061052 - 17 Mar 2025
Viewed by 521
Abstract
Water volume, a fundamental characteristic of lakes, serves as a crucial indicator for understanding regional climate, ecological systems, and hydrological processes. However, limitations in existing estimation methods and datasets for water depth, such as the insufficient observation of small and medium-sized lakes and [...] Read more.
Water volume, a fundamental characteristic of lakes, serves as a crucial indicator for understanding regional climate, ecological systems, and hydrological processes. However, limitations in existing estimation methods and datasets for water depth, such as the insufficient observation of small and medium-sized lakes and unclear temporal information, have hindered a comprehensive understanding of global lake water volumes. To address these challenges, this study develops a machine learning (ML)-based approach to estimate the dynamic water depths of global lakes. By incorporating various lake features and employing multiple innovative water depth extraction methods, we generated an extensive water depth dataset to train the model. Validation results demonstrate the model’s high accuracy, with the bias of −0.08 m, a MAE of 1.09 m, an RMSE of 4.78 m, and an R2 of 0.95. The proposed method provides dynamic monthly estimates of global lake water depths and volumes in 2000~2020. This study offers a cost-effective and efficient solution for estimating global lake water dynamics, providing reliable data to support the monitoring, analysis, and management of regional and global lake systems. Full article
(This article belongs to the Special Issue River and Lake Dynamic Monitoring and Ecological Assessment Based on Remote Sensing)
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17 pages, 4935 KiB  
Article
Temperature Is a Key Factor Affecting Total Phosphorus and Total Nitrogen Concentrations in Northeastern Lakes Based on Sentinel-2 Images and Machine Learning Methods
by Haoming Qin, Chong Fang, Ge Liu, Kaishan Song, Zhuoshi Li, Sijia Li, Hui Tao and Zhaojiang Yan
Remote Sens. 2025, 17(2), 267; https://doi.org/10.3390/rs17020267 - 13 Jan 2025
Viewed by 857
Abstract
Nitrogen and phosphorus are limiting nutrients in freshwater ecosystems, and the remote estimation of total phosphorus (TP) and total nitrogen (TN) in eutrophic waters is of great significance. This study utilized machine learning algorithms based on Sentinel-2 satellite imagery for remote estimation of [...] Read more.
Nitrogen and phosphorus are limiting nutrients in freshwater ecosystems, and the remote estimation of total phosphorus (TP) and total nitrogen (TN) in eutrophic waters is of great significance. This study utilized machine learning algorithms based on Sentinel-2 satellite imagery for remote estimation of TP and TN concentrations in Lake Xingkai, Chagan and Songhua. Results indicate that random forest (RF) and XGBoost regression algorithms perform better. The performance of the GBDT algorithm was slightly lower than that of the RF and XGBoost regression algorithms, the BP algorithm had overfitting, and the SVR algorithm had poor fitting performance. Results showed that the TN concentration inversion model based on the RF algorithm had the highest accuracy (R2 = 0.98, RMSE = 0.09, MAPE = 19.74%). The Extreme Gradient Boosting (XGB) model also performed well, though slightly less accurately than RF (R2 = 0.97, RMSE = 0.14, MAPE = 20.67%). For TP concentration, the XGB model’s performance (R2 = 0.82, RMSE = 0.08, MAPE = 24.89%) was comparable to that of the RF model (R2 = 0.82, RMSE = 0.07, MAPE = 29.55%). The RF algorithm was applied to all cloud-free Sentinel-2 satellite images of these typical lakes in northeastern China during the non-glacial period from 2017 to 2023, generating spatiotemporal distribution maps of TP and TN concentrations. Between 2017 and 2023, TP concentrations in Lake Xingkai, Chagan and Songhua showed increasing, decreasing, and initially decreasing then increasing patterns, respectively. A positive correlation between temperature and TP concentration was observed, as higher temperatures enhance biological activity. In contrast, a negative correlation was found with TN concentration, as higher temperatures promote phytoplankton growth and reproduction. This study not only offers a new method for monitoring eutrophication in lakes but also provides valuable support for sustainable water resource management and ecological protection goals. Full article
(This article belongs to the Special Issue River and Lake Dynamic Monitoring and Ecological Assessment Based on Remote Sensing)
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19 pages, 6086 KiB  
Article
Remote Sensing Estimation of CDOM for Songhua River of China: Distributions and Implications
by Pengju Feng, Kaishan Song, Zhidan Wen, Hui Tao, Xiangfei Yu and Yingxin Shang
Remote Sens. 2024, 16(23), 4608; https://doi.org/10.3390/rs16234608 - 8 Dec 2024
Viewed by 1075
Abstract
Rivers are crucial pathways for transporting organic carbon from land to ocean, playing a vital role in the global carbon cycle. Dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) are major components of dissolved organic matter and have significant impacts on [...] Read more.
Rivers are crucial pathways for transporting organic carbon from land to ocean, playing a vital role in the global carbon cycle. Dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) are major components of dissolved organic matter and have significant impacts on maintaining the stability of river ecosystems and driving the global carbon cycle. In this study, the in situ samples of aCDOM(355) and DOC collected along the main stream of the Songhua River were matched with Sentinel-2 imagery. Multiple linear regression and five machine learning models were used to analyze the data. Among these models, XGBoost demonstrated a superior, highly stable performance on the validation set (R2 = 0.85, RMSE = 0.71 m−1). The multiple linear regression results revealed a strong correlation between CDOM and DOC (R2 = 0.73), indicating that CDOM can be used to indirectly estimate DOC concentrations. Significant seasonal variations in the CDOM distribution in the Songhua River were observed: aCDOM(355) in spring (6.23 m−1) was higher than that in summer (5.3 m−1) and autumn (4.74 m−1). The aCDOM(355) values in major urban areas along the Songhua River were generally higher than those in non-urban areas. Using the predicted DOC values and annual flow data at the sites, the annual DOC flux in Harbin was calculated to be approximately 0.2275 Tg C/Yr. Additionally, the spatial variation in annual CDOM was influenced by both natural changes in the watershed and human activities. These findings are pivotal for a deeper understanding of the role of river systems in the global carbon cycle. Full article
(This article belongs to the Special Issue River and Lake Dynamic Monitoring and Ecological Assessment Based on Remote Sensing)
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21 pages, 15249 KiB  
Article
Variations of Lake Ice Phenology Derived from MODIS LST Products and the Influencing Factors in Northeast China
by Xiaoguang Shi, Jian Cheng, Qian Yang, Hongxing Li, Xiaohua Hao and Chunxu Wang
Remote Sens. 2024, 16(21), 4025; https://doi.org/10.3390/rs16214025 - 30 Oct 2024
Viewed by 1010
Abstract
Lake ice phenology serves as a sensitive indicator of climate change in the lake-rich Northeast China. In this study, the freeze-up date (FUD), break-up date (BUD), and ice cover duration (ICD) of 31 lakes were extracted from a time series of the land [...] Read more.
Lake ice phenology serves as a sensitive indicator of climate change in the lake-rich Northeast China. In this study, the freeze-up date (FUD), break-up date (BUD), and ice cover duration (ICD) of 31 lakes were extracted from a time series of the land water surface temperature (LWST) derived from the combined MOD11A1 and MYD11A1 products for the hydrological years 2001 to 2021. Our analysis showed a high correlation between the ice phenology measures derived by our study and those provided by hydrological records (R2 of 0.89) and public datasets (R2 > 0.7). There was a notable coherence in lake ice phenology in Northeast China, with a trend in later freeze-up (0.21 days/year) and earlier break-up (0.19 days/year) dates, resulting in shorter ice cover duration (0.50 days/year). The lake ice phenology of freshwater lakes exhibited a faster rate of change compared to saltwater lakes during the period from HY2001 to HY2020. We used redundancy analysis and correlation analysis to study the relationships between the LWST and lake ice phenology with various influencing factors, including lake properties, local climate factors, and atmospheric circulation. Solar radiation, latitude, and air temperature were found to be the primary factors. The FUD was more closely related to lake characteristics, while the BUD was linked to local climate factors. The large-scale oscillations were found to influence the changes in lake ice phenology via the coupled influence of air temperature and precipitation. The Antarctic Oscillation and North Atlantic Oscillation correlate more with LWST in winter, and the Arctic Oscillation correlates more with the ICD. Full article
(This article belongs to the Special Issue River and Lake Dynamic Monitoring and Ecological Assessment Based on Remote Sensing)
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Review

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27 pages, 34152 KiB  
Review
Retrieving Inland Water Quality Parameters via Satellite Remote Sensing: Sensor Evaluation, Atmospheric Correction, and Machine Learning Approaches
by Mohsen Ansari, Anders Knudby, Meisam Amani and Michael Sawada
Remote Sens. 2025, 17(10), 1734; https://doi.org/10.3390/rs17101734 - 15 May 2025
Viewed by 157
Abstract
Satellite remote sensing provides a cost-effective and large-scale alternative to traditional methods for retrieving water quality parameters for inland waters. Effective water quality parameter retrieval via optical satellite remote sensing requires three key components: (1) a sensor whose measurements are sensitive to variations [...] Read more.
Satellite remote sensing provides a cost-effective and large-scale alternative to traditional methods for retrieving water quality parameters for inland waters. Effective water quality parameter retrieval via optical satellite remote sensing requires three key components: (1) a sensor whose measurements are sensitive to variations in water quality; (2) accurate atmospheric correction to eliminate the effect of absorption and scattering in the atmosphere and retrieve the water-leaving radiance/reflectance; and (3) a bio-optical model used to estimate water quality from the optical signal. This study provides a literature review and an evaluation of these three components. First, a review of decommissioned, active, and upcoming satellite sensors is presented, highlighting their advantages and limitations, and a ranking method is introduced to assess their suitability for retrieving chlorophyll-a, colored dissolved organic matter, and non-algal particles in inland waters. This ranking can aid in selecting appropriate sensors for future studies. Second, the strengths and weaknesses of atmospheric correction algorithms used over inland waters are examined. The results show that no atmospheric correction algorithm performed consistently across all conditions. However, understanding their strengths and weaknesses allows users to select the most suitable algorithm for a specific use case. Third, the challenges, limitations, and recent advances of machine learning use in bio-optical models for inland water quality parameter retrieval are discussed. Machine learning models have limitations, including low generalizability, low dimensionality, spatial/temporal autocorrelation, and information leakage. These issues highlight the importance of locally trained models, rigorous cross-validation methods, and integrating auxiliary data to enhance dimensionality. Finally, recommendations for promising research directions are provided. Full article
(This article belongs to the Special Issue River and Lake Dynamic Monitoring and Ecological Assessment Based on Remote Sensing)
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25 pages, 1644 KiB  
Review
The Application of Remote Sensing Technology in Inland Water Quality Monitoring and Water Environment Science: Recent Progress and Perspectives
by Lei Chen, Leizhen Liu, Shasha Liu, Zhenyu Shi and Chunhong Shi
Remote Sens. 2025, 17(4), 667; https://doi.org/10.3390/rs17040667 - 16 Feb 2025
Viewed by 2456
Abstract
Due to its long-term and high-frequency observation capabilities, remote sensing is widely recognized as an indispensable and preferred technology for large-scale and cross-regional water quality monitoring. This paper comprehensively reviews the recent progress of remote sensing for water environment monitoring, predominantly focusing on [...] Read more.
Due to its long-term and high-frequency observation capabilities, remote sensing is widely recognized as an indispensable and preferred technology for large-scale and cross-regional water quality monitoring. This paper comprehensively reviews the recent progress of remote sensing for water environment monitoring, predominantly focusing on remote sensing data sources, inversion indices, and inversion models. Specifically, we summarize the inversion methods for commonly monitored water quality parameters, including optically active constituents (such as chlorophyll-a, colored dissolved organic matter, total suspended solids, and water clarity) and non-optically active constituents (including total nitrogen, total phosphorus, and chemical oxygen demand). Furthermore, the applications of remote sensing in the field of environmental sciences such as spatiotemporal evolution and driver factor analysis of water quality, carbon budget research, and pollution source identification are also systematically reviewed. Finally, we propose that atmospheric correction algorithm improvement, multi-source data fusion, and high-precision large-scale inversion algorithms should be further developed to reduce the current dependence on empirical observation algorithms in remote sensing and overcome the limitations imposed by temporal and spatial scales and that more inversion models for non-optically active parameters should be explored to realize accurate remote sensing monitoring of these components in the future. This review not only enhances our understanding of the critical role of remote sensing in inland water quality monitoring but also provides a scientific basis for water environment management. Full article
(This article belongs to the Special Issue River and Lake Dynamic Monitoring and Ecological Assessment Based on Remote Sensing)
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