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Remote Sensing
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Intelligent Remote Sensing for Wetland Mapping and Monitoring
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Intelligent Remote Sensing for Wetland Mapping and Monitoring

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2595

Special Issue Editors

Dr. Ming Wang

E-Mail Website
Guest Editor
College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China
Interests: wetland remote sensing; wetland mapping and monitoring; intelligent information extraction; time-series change detection; urban wetlands
Dr. Weidong Man

E-Mail
Guest Editor
College of Mining Engineering, North China University of Science and Technology, Tangshan 10081, China
Interests: wetland remote sensing; vegetation parameter retrieval; ecological restoration; eco-environmental remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiao Huang

E-Mail Website
Guest Editor
Department of Environmental Sciences, Emory University, Atlanta, GA, USA
Interests: GeoAI and deep learning; disaster remote sensing; human–environment interaction; computational social sciences
Special Issues, Collections and Topics in MDPI journals
Dr. Mingyue Liu

E-Mail Website
Guest Editor
College of Mining Engineering, North China University of Science and Technology, Tangshan 10081, China
Interests: wetland remote sensing; coastal wetland; vegetation parameter retrieval; land use/land cover change
Dr. Huiying Li

E-Mail Website
Guest Editor
School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China
Interests: wetland remote sensing; coastal wetland; wetland mapping and monitoring; vegetation parameter retrieval
Dr. Hengxing Xiang

E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: wetland remote sensing; wetland functional traits and ecosystem services; environmental remote sensing

Special Issue Information

Dear Colleagues,

Wetlands are among the world’s most valuable and dynamic ecosystems, playing crucial roles in biodiversity conservation, water regulation, and climate mitigation. However, the rapid loss and transformation of wetlands due to climate change and human activities have heightened the need for timely and accurate monitoring. Remote sensing offers a unique opportunity to observe wetlands across extensive spatial and temporal scales. In recent years, the advent of artificial intelligence (AI), deep learning, and advanced computational methods has opened new possibilities for extracting detailed and reliable wetland information from increasingly diverse and complex remote sensing data.

This Special Issue aims to showcase cutting-edge advances in intelligent remote sensing techniques for wetland mapping and monitoring. By promoting the integration of AI-driven methods and advanced time-series analyses, this Special Issue aligns with the journal’s focus on remote sensing science, technology, and applications. We seek contributions that advance both the development of novel methodologies and the implementation of practical solutions, fostering interdisciplinary collaboration among researchers in remote sensing, ecology, computer vision, and environmental science.

We invite original research articles, technical notes, and review papers covering a broad range of topics including, but not limited to, the following:

  1. Deep learning and machine learning approaches for wetland classification and change detection;
  2. Time-series analysis and spatiotemporal modeling for wetland dynamics;
  3. SAR, LiDAR, and novel remote sensing for wetland mapping and monitoring;
  4. Data fusion and integration of multi-source remote sensing for wetlands;
  5. AI-enabled techniques for high-resolution and large-scale wetland mapping;
  6. Remote sensing of wetland functional traits and ecosystem services;
  7. Monitoring of wetland hydrological, biogeochemical, and vegetation parameters;
  8. Automatic delineation and inventory of wetlands through intelligent algorithms;
  9. Assessment of wetland degradation, restoration, and connectivity with remote sensing.

Submissions introducing novel frameworks, demonstrating practical applications, or providing comprehensive reviews are especially encouraged.

Dr. Ming Wang
Dr. Weidong Man
Dr. Xiao Huang
Dr. Mingyue Liu
Dr. Huiying Li
Dr. Hengxing Xiang
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

  • wetland mapping and monitoring
  • intelligent remote sensing
  • deep learning
  • time-series analysis
  • change detection
  • wetland ecosystem services
  • hydrological dynamics

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

Published Papers (4 papers)

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Research

22 pages, 5716 KB  
Article
Machine-Learning-Based Historical Reconstruction of Soil Organic Carbon Dynamics in Coastal Tidal Flats: Quantifying the Spatiotemporal Impacts of Reclamation
by Caiyao Kou, Yongbin Zhang, Weidong Man, Fuping Li, Chunyan Lu, Qingwen Zhang and Mingyue Liu
Remote Sens. 2026, 18(7), 978; https://doi.org/10.3390/rs18070978 - 25 Mar 2026
Viewed by 371
Abstract
Coastal tidal flat soil organic carbon (SOC) is significantly affected by reclamation activities. However, the limited availability of historical SOC data constrains the reconstruction of past SOC. SOC data were integrated in current time-point and remote sensing data during the last two decades [...] Read more.
Coastal tidal flat soil organic carbon (SOC) is significantly affected by reclamation activities. However, the limited availability of historical SOC data constrains the reconstruction of past SOC. SOC data were integrated in current time-point and remote sensing data during the last two decades by applying machine learning (ML) methods such as random forest (RF), boosted regression trees (BRT), and extreme gradient boosting (XGBoost) to map the spatiotemporal distribution of tidal flat reclamation and the spatial distribution of SOC content in the western coastal region of the Bohai Rim over the last two decades and to explore how the period and type of reclamation affect SOC content. The results show that: (1) The area of tidal flats decreased by 61.92% from 2000 to 2020 due to reclamation activities. (2) Among the ML methods, the XGBoost model demonstrated the best performance (R2 = 0.71, MAE = 0.93 g/kg, RMSE = 1.32 g/kg, d-Willmott = 0.98), with the modified normalized difference water index (MNDWI) being the most important predictor variable. (3) The SOC content of tidal flats decreased from 4.11 g/kg in 2000 to 3.33 g/kg in 2020, a reduction of 18.98%. (4) The reclamation of tidal flats into marshes, forest lands, grasslands, farmlands, and bare lands led to an increasing trend in SOC content, with the greatest increase observed in regions converted to farmlands. This study provides data support for the control of reclamation activities, creation of tidal flat conservation policies, and strategic decision-making for climate change mitigation. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing for Wetland Mapping and Monitoring)
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17 pages, 33308 KB  
Article
Mapping of Threatened Vereda Wetlands in the Brazilian Midwest Using a Domain-Specific U-Net
by Jeaneth Machicao, Alexandre Augusto Barbosa, Leandro O. Salles, Peter Mann Toledo, Pedro Luiz P. Corrêa, Luiz Flamarion B. Oliveira, Rosane Garcia Collevatti, Eduardo Barroso de Souza and Jean Pierre H. B. Ometto
Remote Sens. 2026, 18(5), 791; https://doi.org/10.3390/rs18050791 - 5 Mar 2026
Viewed by 419
Abstract
The palm swamp landscapes, particularly the Vereda wetlands and their associated swamp gallery forests (VED.SGF), comprise essential yet threatened ecosystems within the Brazilian Cerrado. In addition to supporting significant portions of biodiversity, they provide critical ecosystem services such as storing and filtering excess [...] Read more.
The palm swamp landscapes, particularly the Vereda wetlands and their associated swamp gallery forests (VED.SGF), comprise essential yet threatened ecosystems within the Brazilian Cerrado. In addition to supporting significant portions of biodiversity, they provide critical ecosystem services such as storing and filtering excess rainwater and serving as major carbon reservoirs in organic soils. These wetlands are directly linked to the drainage systems of the headwaters of the main Cerrado river basins, which together account for about two-thirds of Brazil’s hydrographic basins. Mapping and managing VED.SGF ecosystems through remote sensing present major challenges addressed in this first study. Their narrow, dendritic, and complex tabular spatial pattern, often elongated along watersheds on scales of hundreds of kilometers, suffering distortions due to human impact, and the limited amount of annotated data make segmentation particularly challenging. Existing deep learning (DL) methods, typically pre-trained on natural images, struggle to capture the spectral and spatial intricacies of these ecosystems. This study introduces a trained-from-scratch U-Net model supported by field-based experimental procedures to ensure high-quality wetland annotations. The resulting dataset covers approximately 7300 km2 in western Bahia and provides domain-specific weights tailored to remote sensing applications. Using high-resolution (4.6 m) RGB mosaics, the model was trained, validated, and tested to establish a reproducible and scalable pipeline. The proposed method achieved robust results in an independent test area of 8040 km2, with a mean IoU of 0.728, F1-score of 0.843, and Cohen’s Kappa of 0.837. These results demonstrate consistent performance and strong generalization to new areas, establishing a scientifically reliable baseline that situates the model competitively within the current state of the art. By releasing both the model weights and annotated dataset, this study provides valuable resources to advance future research on mapping and monitoring these unique and strategic wetland ecosystems. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing for Wetland Mapping and Monitoring)
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23 pages, 9600 KB  
Article
Vertical Monitoring of Chlorophyll-a and Phycocyanin Concentrations High-Latitude Inland Lakes Using Sentinel-3 OLCI
by Jinpeng Shen, Zhidan Wen, Kaishan Song, Hui Tao, Shizhuo Liu, Zhaojiang Yan, Chong Fang and Lili Lyu
Remote Sens. 2026, 18(1), 139; https://doi.org/10.3390/rs18010139 - 31 Dec 2025
Cited by 1 | Viewed by 686
Abstract
Massive phytoplankton blooms threaten lake ecosystems, causing significant ecological and socio-economic damage. While remote sensing is vital for monitoring, the vertical stratification of algae influences light propagation and distorts remote sensing reflectance signals. This effect is particularly understudied in high-latitude lakes, leaving a [...] Read more.
Massive phytoplankton blooms threaten lake ecosystems, causing significant ecological and socio-economic damage. While remote sensing is vital for monitoring, the vertical stratification of algae influences light propagation and distorts remote sensing reflectance signals. This effect is particularly understudied in high-latitude lakes, leaving a gap in understanding phytoplankton biomass patterns. To address this, our study investigated three high-latitude water bodies: Lake Hulun, Fengman Reservoir, and Lake Khanka. We collected water samples from three depths based on total and euphotic zone depth and developed layer-specific inversion models for chlorophyll-a (Chal) and phycocyanin (PC) using a random forest algorithm. These models demonstrated strong performance and were applied to Sentinel-3 OLCI imagery from 2016–2024. Our results show that Chla generally decreases exponentially with depth, whereas PC exhibits a Gaussian-like vertical distribution with a pronounced subsurface maximum at approximately 1 m. In addition, a significant positive correlation between Chla and PC was observed in surface waters. In Lake Khanka, the northern basin exhibited a significant interannual increase in phytoplankton biomass. At 3 m, PC correlated negatively with turbidity and responded strongly to cyanobacterial blooms, while organic suspended matter correlated positively with Chla. This work establishes a robust framework for multilayer water quality monitoring in high-latitude lakes, providing critical insights for eutrophication management and cyanobacterial bloom early warning. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing for Wetland Mapping and Monitoring)
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24 pages, 4561 KB  
Article
Four-Decade CDOM Dynamics in Amur River Basin Lakes from Landsat and Machine Learning
by Ye Wang, Pengfei Han, Chi Zhang, Zhuohang Xin, Lu Zhang, Xixin Lu and Jinkun Huang
Remote Sens. 2026, 18(1), 125; https://doi.org/10.3390/rs18010125 - 29 Dec 2025
Cited by 1 | Viewed by 639
Abstract
Lakes in the Amur River Basin (ARB) are increasingly influenced by climate variability and human activities, yet long-term basin-scale patterns of colored dissolved organic matter (CDOM) remain unclear. In this study, we developed a support vector regression (SVR) model to retrieve lake CDOM [...] Read more.
Lakes in the Amur River Basin (ARB) are increasingly influenced by climate variability and human activities, yet long-term basin-scale patterns of colored dissolved organic matter (CDOM) remain unclear. In this study, we developed a support vector regression (SVR) model to retrieve lake CDOM from Landsat 5/7/8 imagery and generated a 40-year (1984–2023) CDOM dataset for 69 large lakes. The model provides a reliable tool for multi-decadal, large-area water quality monitoring considering its robust performance (R2 = 0.88, rRMSE = 22.4%, MAE = 2.63 m−1). Trend analysis revealed a significant rise in CDOM since 1999, particularly across the Mongolian Plateau and Northeast China Plain. Among the 69 lakes, 27 exhibited increasing CDOM, while 4 showed declines, highlighting pronounced regional variability. Variance partitioning indicated that human activities, especially irrigation and grazing, account for ~30% of CDOM variation, exceeding the contribution of any single climatic driver, whereas temperature represents the dominant climate driver (12.8%). Shallow systems were more sensitive to external disturbances, while deep lakes responded more strongly to thermal conditions. This study delivers the first long-term satellite-based CDOM assessment in the ARB and underscores the combined impacts of climate change and land-use pressures on lake optical dynamics. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing for Wetland Mapping and Monitoring)
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