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Monitoring and Assessment of Ecohydrological Evolution of River–Lake–Wetland Systems Based on Remote Sensing Big Data

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3913

Special Issue Editors

Dr. Yanfeng Wu

E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
Interests: Wetland ecohydrology processes; hydrological simulation; functional assessment; hydrological regulation; climate change
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hsiang-te Kung

E-Mail Website
Guest Editor
Department of Earth Sciences, University of Memphis, Memphis, TN 38152, USA
Interests: remote sensing of water resources; remote sensing and ecohydrology; remote sensing and environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

River–lake–wetland systems play a crucial role in maintaining ecological balance, providing biodiversity habitats, and supporting various ecosystem services. These complex ecosystems are highly sensitive to both natural and anthropogenic changes, such as climate variability, land use alterations, and water management practices. Understanding the ecohydrological evolution of these systems is essential for effective environmental management and sustainable development. Recent advancements in remote sensing technologies and big data analytics have provided unprecedented capabilities to monitor and assess these dynamic systems at various spatial and temporal scales. This Special Issue aims to leverage these technological advancements to enhance our understanding of the ecohydrological processes and changes in river–lake–wetland systems.

This Special Issue seeks to bring together cutting-edge research that utilizes remote sensing big data to monitor and assess the ecohydrological evolution of river–lake–wetland systems. It aligns with the scope of Remote Sensing by focusing on the application of advanced remote sensing techniques and big data analytics to address critical environmental challenges. The goal is to provide a comprehensive overview of the current state of the art in this field, identify knowledge gaps, and propose innovative solutions for sustainable management and conservation of these vital ecosystems. By doing so, this Special Issue will contribute to the broader scientific community by offering valuable insights and practical tools for policymakers, environmental scientists, and practitioners.

This issue will contribute to the field of Remote Sensing by showcasing novel approaches for handling and analyzing large volumes of data from diverse sources. It will highlight how these techniques can be applied to complex ecohydrological systems, potentially leading to new methodologies and tools that can be used in other environmental monitoring contexts.

We invite submissions that cover a wide range of topics related to the ecohydrological evolution of river–lake–wetland systems using remote sensing big data. Potential themes include but are not limited to the following:

  • Land Cover and Land Use Change Detection;
  • Water Quality Monitoring;
  • Hydrological Modeling and Assessment;
  • Ecological Health and Biodiversity;
  • Impact of Climate Change and Human Activities;
  • Machine Learning and Data Analytics;
  • Case Studies and Regional Assessments.

We welcome a variety of article types, including original research articles, review papers, short communications, and technical notes. Submissions should present novel methodologies, innovative applications, or significant advancements in the field of remote sensing for ecohydrological studies.

Dr. Yanfeng Wu
Prof. Dr. Hsiang-te Kung
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

  • remote sensing big data
  • ecohydrological evolution
  • river–lake–coastal wetland systems
  • ecological monitoring
  • hydrological assessment
  • machine learning
  • data analytics

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Published Papers (4 papers)

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Research

21 pages, 9604 KB  
Article
Long-Term Sediment Accretion Rates of Floodplains Using Remote Sensing Waterline Extraction Method: A Case Study of Poyang Lake, China
by Yinghao Zhang, Xiao Zhang, Na Zhang, Jie Xu, Shengyang Hui and Xijun Lai
Remote Sens. 2026, 18(7), 1044; https://doi.org/10.3390/rs18071044 - 31 Mar 2026
Viewed by 371
Abstract
With a typical floodplain in Poyang Lake selected as the study area, this paper employed the remote sensing Waterline Extraction Method (WEM) to invert its topographic changes based on 264 Landsat images from 1987 to 2024. The research systematically revealed the spatiotemporal variations [...] Read more.
With a typical floodplain in Poyang Lake selected as the study area, this paper employed the remote sensing Waterline Extraction Method (WEM) to invert its topographic changes based on 264 Landsat images from 1987 to 2024. The research systematically revealed the spatiotemporal variations in sediment accretion rates over the past 40 years and their influencing factors. By comparing different WEMs, the object-based method was identified as the most suitable for this study area. Accuracy validation of the topographic inversion showed that when using no fewer than 13 images, the average elevation error rate remained below 7.0%, indicating good reliability. The period from 1987 to 2024 was divided into 15 sub-periods, and digital elevation models of the floodplain were reconstructed for each. Results indicated that: (1) natural floodplain unaffected by sand mining experienced continuous accretion, with an average rate of approximately 3.1 ± 0.7 cm yr−1 (surface elevation change) between 1987 and 2024; (2) in areas impacted by sand mining, the sediment accretion rate after mining (about 1.7 ± 0.8 cm yr−1) was lower than that before mining (about 2.6 ± 2.7 cm yr−1), likely due to the loss of vegetation cover reducing sediment retention capacity; (3) different vegetation types notably influenced accretion rates, with mixed Carex–T. lutarioriparia communities showing a consistently higher rate (about 3.5 ± 0.9 cm yr−1) than pure Carex communities (about 1.7 ± 0.7 cm yr−1), primarily attributable to differences in plant morphology, root architecture, and inundation tolerance. Further analysis revealed that riverine sediment supply was the fundamental material source for floodplain accretion. The phased decline in sediment discharge from the Ganjiang and Xiushui rivers since 1996 generally corresponds to the decreasing trend in sediment accretion rates observed after 2004. Full article
(This article belongs to the Special Issue Monitoring and Assessment of Ecohydrological Evolution of River–Lake–Wetland Systems Based on Remote Sensing Big Data)
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21 pages, 4820 KB  
Article
Determination of Suitable Ecological Intervals for Arid Terminal Lakes via Multi-Source Remote Sensing: A “Morphometry–Security–Efficiency” Framework Applied to Ebinur Lake
by Jing Liu, Aihua Long, Mingjiang Deng, Qiang An, Ji Zhang, Qing Luo and Rui Sun
Remote Sens. 2026, 18(5), 771; https://doi.org/10.3390/rs18050771 - 3 Mar 2026
Viewed by 400
Abstract
Terminal lakes in arid regions face severe degradation due to the dual pressures of climate change and anthropogenic water consumption. Traditional single-threshold methods for defining ecological water requirements often fail to balance ecosystem stability with water scarcity. To address this, this study constructs [...] Read more.
Terminal lakes in arid regions face severe degradation due to the dual pressures of climate change and anthropogenic water consumption. Traditional single-threshold methods for defining ecological water requirements often fail to balance ecosystem stability with water scarcity. To address this, this study constructs a comprehensive framework coupling “Morphometric Stability–Ecological Security Reliability–Resource Use Efficiency” to delineate the suitable ecological interval for Ebinur Lake, the largest saltwater lake in Xinjiang. Using multi-source remote sensing data (Landsat, Sentinel, ICESat, CryoSat), we reconstruct the long-term hydrological dynamics from 2001 to 2023. Results indicate a significant shrinking trend in the lake area, driven primarily by reduced inflow. We jointly consider the lake morphometric breakpoint, the ecological security baseline, and the lower bound of ecosystem service water use efficiency (ESWUE) to determine a minimum suitable ecological area of 500 km2; the regulation upper limit is set at 740 km2 based on the marginal peak of ESWUE. However, monitoring data reveal that the lake falls below the minimum 500 km2 baseline in approximately 40% of months, highlighting a severe ecological deficit risk. Furthermore, ESWUE analysis shows a peak in April (10 CNY/m3), suggesting that, under current climate conditions, a “Spring Surplus and Autumn Deficit” regulation strategy—advancing the replenishment window to the spring windy season—can maximize dust suppression benefits at a lower evaporative cost. This study provides a theoretical basis and methodological paradigm that will contribute to the sustainable management of shrinking terminal lakes globally. Full article
(This article belongs to the Special Issue Monitoring and Assessment of Ecohydrological Evolution of River–Lake–Wetland Systems Based on Remote Sensing Big Data)
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19 pages, 4564 KB  
Article
Estimating and Mapping Aboveground Biomass of Vegetation in Typical Lake Flooding Wetland Based on MODIS and Landsat Images Fusion
by Xianghu Li, Yaling Lin, Zhenhe Lv, Yani Song and Xing Huang
Remote Sens. 2025, 17(22), 3754; https://doi.org/10.3390/rs17223754 - 18 Nov 2025
Cited by 1 | Viewed by 1012
Abstract
Aboveground biomass (AGB) is a key indicator reflecting the metabolic mechanisms of wetland plants. This study simulated the AGB of multi-community in Poyang Lake (PYL) wetland based on long-term high-resolution (30 m, 8 d) NDVI fused from MODIS and Landsat images and analyzed [...] Read more.
Aboveground biomass (AGB) is a key indicator reflecting the metabolic mechanisms of wetland plants. This study simulated the AGB of multi-community in Poyang Lake (PYL) wetland based on long-term high-resolution (30 m, 8 d) NDVI fused from MODIS and Landsat images and analyzed the spatial distribution of AGB of different wetland plants and their relationships with wetland surface elevation. Comparative analysis showed that the cubic polynomial regression model performed the best in describing the quantitative relationship between AGB and NDVI, with the R2 of 0.83 for fitting data, the Root Mean Square Error (RMSE) of 51.8 g/m2, and prediction accuracy (G) of 71.7% for validation data. The results showed that the maximum AGB of Carex cinerascens (Cc) and Phragmites australis-Triarrhena lutarioriparia (P-T) communities during the spring growth period reached 1352 g/m2 and 1529 g/m2, respectively. The total AGB value of the Polygonum hydropiper-Phalaris arundinacea (P-P) community was the lowest from June to August, due to the flooding of PYL. Trend analysis found that the AGB of the Cc and P-P communities presented increasing trends during 2001–2020. In spatial terms, the Southern and Western areas had the largest AGB, with an average of 1340 g/m2 and 1283 g/m2, respectively, while the AGB in the Northern lake area was the lowest. Additionally, more than 78% of the total vegetation AGB was distributed in areas with elevations of 11.0–15.0 m (total AGB values of up to 332.7–376.3 × 107 kg). The changes in water level and the timing of soil exposure in PYL dominated the spatiotemporal patterns of wetland vegetation AGB. Full article
(This article belongs to the Special Issue Monitoring and Assessment of Ecohydrological Evolution of River–Lake–Wetland Systems Based on Remote Sensing Big Data)
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41 pages, 4705 KB  
Article
Full-Cycle Evaluation of Multi-Source Precipitation Products for Hydrological Applications in the Magat River Basin, Philippines
by Jerome G. Gacu, Sameh Ahmed Kantoush and Binh Quang Nguyen
Remote Sens. 2025, 17(19), 3375; https://doi.org/10.3390/rs17193375 - 7 Oct 2025
Cited by 1 | Viewed by 1550
Abstract
Satellite Precipitation Products (SPPs) play a crucial role in hydrological modeling, particularly in data-scarce and climate-sensitive basins such as the Magat River Basin (MRB), Philippines—one of Southeast Asia’s most typhoon-prone and infrastructure-critical watersheds. This study presents the first full-cycle evaluation of nine widely [...] Read more.
Satellite Precipitation Products (SPPs) play a crucial role in hydrological modeling, particularly in data-scarce and climate-sensitive basins such as the Magat River Basin (MRB), Philippines—one of Southeast Asia’s most typhoon-prone and infrastructure-critical watersheds. This study presents the first full-cycle evaluation of nine widely used multi-source precipitation products (2000–2024), integrating raw validation against rain gauge observations, bias correction using quantile mapping, and post-correction re-ranking through an Entropy Weight Method–TOPSIS multi-criteria decision analysis (MCDA). Before correction, SM2RAIN-ASCAT demonstrated the strongest statistical performance, while CHIRPS and ClimGridPh-RR exhibited robust detection skills and spatial consistency. Following bias correction, substantial improvements were observed across all products, with CHIRPS markedly reducing systematic errors and ClimGridPh-RR showing enhanced correlation and volume reliability. Biases were decreased significantly, highlighting the effectiveness of quantile mapping in improving both seasonal and annual precipitation estimates. Beyond conventional validation, this framework explicitly aligns SPP evaluation with four critical hydrological applications: flood detection, drought monitoring, sediment yield modeling, and water balance estimation. The analysis revealed that SM2RAIN-ASCAT is most suitable for monitoring seasonal drought and dry periods, CHIRPS excels in detecting high-intensity and erosive rainfall events, and ClimGridPh-RR offers the most consistent long-term volume-based estimates. By integrating validation, correction, and application-specific ranking, this study provides a replicable blueprint for operational SPP assessment in monsoon-dominated, data-limited basins. The findings underscore the importance of tailoring product selection to hydrological purposes, supporting improved flood early warning, drought preparedness, sediment management, and water resources governance under intensifying climatic extremes. Full article
(This article belongs to the Special Issue Monitoring and Assessment of Ecohydrological Evolution of River–Lake–Wetland Systems Based on Remote Sensing Big Data)
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