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Remote Sensing
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Artificial Intelligence for Coastal Remote Sensing: Dataset and Application
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Artificial Intelligence for Coastal Remote Sensing: Dataset and Application

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 789

Special Issue Editors

Dr. Chunpeng Chen

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Guest Editor
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200050, China
Interests: coastal remote sensing; coastal wetlands; deep learning; coastal geomorphology
Special Issues, Collections and Topics in MDPI journals
Dr. Panpan Xu

E-Mail Website
Guest Editor
Department of Marine GeoSciences, Ocean University of China, Qingdao 266100, China
Interests: global aquatic land cover; coastal wetlands; remote sensing; LULC change
Dr. Pengfei Tang

E-Mail Website
Guest Editor
Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea
Interests: tidal flat mapping; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coastal zones are vital yet vulnerable ecosystems facing unprecedented pressure from climate change and human activities. Leveraging the power of artificial intelligence (AI) alongside remote sensing presents a revolutionary opportunity to enhance the precision, efficiency, and scalability of coastal monitoring and management. This Special Issue seeks high-quality research advancing the application of AI techniques to interpret remote sensing data (satellite, aerial, UAV, SAR, LiDAR, hyperspectral) for understanding and safeguarding coastal environments.

We invite original contributions focusing on AI-driven extraction, mapping, and change detection of key coastal elements (such as salt marshes, mangroves, aquaculture ponds, tidal flats, shorelines, river deltas, and coastal wetlands) and emerging infrastructure (like offshore wind turbines and coastal photovoltaics), as well as those assessing coastal responses to global climate change and sea level rise. We particularly encourage research on novel AI algorithms optimized for coastal challenges in remote sensing (e.g., handling tidal influences, complex spectral signatures, and sparse data), the creation of benchmark datasets for training and validation, robust time-series analysis of coastal dynamics, and solutions enhancing the scalability and automation of coastal intelligence.

This Special Issue aims to showcase cutting-edge methodologies and provide a valuable resource for researchers and practitioners dedicated to sustainable coastal zone management through advanced geospatial AI.

Dr. Chunpeng Chen
Dr. Panpan Xu
Dr. Pengfei Tang
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

  • artificial intelligence
  • automated mapping
  • coastal wetlands
  • river deltas
  • multi-source data fusion
  • earth observation big data

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Published Papers (1 paper)

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Research

26 pages, 24920 KB  
Article
An Interpretable Transformer-Based Framework for Monitoring Dissolved Inorganic Nitrogen and Phosphorus in Jiangsu–Zhejiang–Shanghai Offshore
by Yushan Jiang, Zigeng Song, Wang Man, Xianqiang He, Qin Nie, Zongmei Li, Xiaofeng Du and Xinchang Zhang
Remote Sens. 2026, 18(1), 154; https://doi.org/10.3390/rs18010154 - 3 Jan 2026
Viewed by 426
Abstract
Anthropogenic increases in nitrogen and phosphorus inputs have intensified coastal water pollution, leading to economic losses and even threats to human health. Dissolved Inorganic Nitrogen (DIN) and Dissolved Inorganic Phosphorus (DIP), as key indicators of water quality, are essential for formulating environmental protection [...] Read more.
Anthropogenic increases in nitrogen and phosphorus inputs have intensified coastal water pollution, leading to economic losses and even threats to human health. Dissolved Inorganic Nitrogen (DIN) and Dissolved Inorganic Phosphorus (DIP), as key indicators of water quality, are essential for formulating environmental protection strategies. While deep learning has advanced the retrieval of these nutrients in coastal waters, existing models remain constrained by limited accuracy, insufficient interpretability, and poor regional transferability. To address these issues, we developed a Transformer-based model for retrieving DIN and DIP in the Jiangsu-Zhejiang-Shanghai (JZS) Offshore, integrating satellite observations with reanalysis data. Our model outperformed previous studies in this region, achieving high retrieval accuracy for DIN (R2 = 0.88, RMSE = 0.16 mg/L, and MAPE = 33.69%) and DIP (R2 = 0.85, RMSE = 0.007 mg/L, and MAPE = 31.59%) with strong interpretability. Based on this model, we generated a long-term (2005–2024) dataset, revealing clear seasonality and spatial patterns of DIN and DIP. Specifically, the concentrations have a distinct seasonal cycle with winter minima and autumn maxima, as well as estuarine-to-offshore decreasing gradient. Water quality assessment further showed that the areal extent of medium-to-high eutrophic waters increased by 3.94 × 102 km2/yr (2005–2016) but decreased by 4.45 × 102 km2/yr (2016–2024). Overall, the proposed Transformer-based framework provided a robust, accurate, and interpretable tool for nitrogen and phosphorus nutrient retrieval, supporting sustainable management of marine water quality in the JZS coastal ecosystems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Coastal Remote Sensing: Dataset and Application)
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