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Remote Sensing for Monitoring Harmful Algal Blooms (Second Edition)
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Remote Sensing for Monitoring Harmful Algal Blooms (Second Edition)

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1908

Special Issue Editors

Dr. Gongliang Yu

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Guest Editor
CAS Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
Interests: water color remote sensing; harmful algal blooms monitoring; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junsheng Li

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Guest Editor
International Research Center of Big Data for Sustainable Development Goals, Chinese Academy of Sciences, Beijing 100094, China
Interests: water color remote sensing; harmful algal blooms monitoring; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Chen Wang

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Guest Editor
Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of China, Beijing 100094, China
Interests: coastal wetland remote sensing; water environment remote sensing; harmful algal blooms monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Yao Liu

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Guest Editor
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing 100048, China
Interests: hyperspectral remote sensing; water color remote sensing; image classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The continuous development of the social economy and the intensification of human activities in recent years have resulted in the occurrence of eutrophication in many sea areas and inland waterbodies, with the frequent occurrence of harmful algal blooms. Remote sensing has many advantages in terms of observing harmful algal blooms. Satellite remote sensing can monitor the spatial distribution of large-scale harmful algal blooms, UAV remote sensing can realize the high-resolution monitoring of harmful algal blooms under clouds, and the public participation of monitoring harmful algal blooms can be realized through smartphone-based citizen science. In the process of monitoring harmful algal blooms based on remote sensing, there are still some scientific and technical problems that need to be further studied.

This Special Issue aims to present studies covering monitoring methods, temporal and spatial variation rules, environmental impact analysis, and methods for the prediction and early warning of harmful algal blooms based on multisource remote sensing technology. Remote sensing technology includes satellite remote sensing, UAV remote sensing, and smartphone-based citizen science, whereas satellite remote sensing includes optical remote sensing satellites, SAR, and thermal infrared. The methods for the remote sensing of harmful algal blooms include traditional threshold segmentation, decision tree, and deep learning methods. The analysis of temporal and spatial variation rules and factors influencing harmful algal blooms can be oriented to a certain waterbody or a wide range of water areas. In addition to remote sensing data, meteorological and other auxiliary data can be used in environmental impact analysis and the prediction and early warning of harmful algal blooms.

  • Methods for monitoring harmful algal blooms based on satellite remote sensing;
  • Methods for monitoring harmful algal blooms based on UAVs;
  • Methods for monitoring harmful algal blooms based on citizen science;
  • Methods for monitoring harmful algal blooms based on deep learning;
  • Analysis of the temporal and spatial variations in harmful algal blooms;
  • Analysis on factors influencing harmful algal blooms;
  • Analysis of the environmental impact of harmful algal blooms;
  • Early warning and prediction of harmful algal blooms.

Dr. Gongliang Yu
Prof. Dr. Junsheng Li
Dr. Chen Wang
Dr. Yao Liu
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
  • UAVs
  • citizen science
  • harmful algal blooms
  • eutrophication
  • water quality
  • bio-optical properties
  • natural and anthropogenic factors

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Related Special Issue

Published Papers (2 papers)

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Research

24 pages, 6074 KB  
Article
Remote Sensing Inversion of Chlorophyll-a in the East China Sea Based on ALA-BP Neural Network
by Lu Cao, Ying Xiong, Yuntao Wang, Xiangbin Ran, Jiayin Bian, Qiang Fang, Wentao Ma and Huiyu Zheng
Remote Sens. 2026, 18(9), 1415; https://doi.org/10.3390/rs18091415 - 3 May 2026
Viewed by 183
Abstract
Under the combined impacts of climate change and intensified human activities, harmful algal blooms (HABs) have occurred with increasing frequency in China’s coastal waters, posing growing risks to marine ecosystems and regional sustainability. Chlorophyll-a concentration (Chl-a), a key indicator of phytoplankton biomass, plays [...] Read more.
Under the combined impacts of climate change and intensified human activities, harmful algal blooms (HABs) have occurred with increasing frequency in China’s coastal waters, posing growing risks to marine ecosystems and regional sustainability. Chlorophyll-a concentration (Chl-a), a key indicator of phytoplankton biomass, plays a crucial role in HAB monitoring and early warning. This study integrates satellite remote sensing data from 2000 to 2004, 2011 to 2013, and 2023 to 2024 with in situ measurements and environmental variables (e.g., dissolved oxygen) to investigate Chl-a dynamics in the East China Sea. The results indicate pronounced spatiotemporal heterogeneity across the region. Spectral features were represented using band-ratio methods and the BRG model, followed by variable selection based on the Bayesian Information Criterion (BIC) to determine the optimal band combinations for model training. Six mainstream machine learning models were evaluated, and the Backpropagation Neural Network (BP) was selected as the baseline model due to its superior performance. To further improve model robustness and global optimization capability, the Artificial Lemming Algorithm (ALA) was employed to optimize the BP network, resulting in the ALA-BP inversion model. The optimized model achieved correlation coefficients of 0.933 on the test set and 0.940 on the independent validation set, outperforming the other models. The proposed model was further applied to the 2024 algal bloom event in the East China Sea, successfully capturing the spatiotemporal variations of Chl-a. This study provides an effective retrieval framework for Chl-a in optically complex coastal waters and demonstrates its applicability in HAB monitoring. Full article
(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms (Second Edition))
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38 pages, 9751 KB  
Article
Detecting Harmful Algae Blooms (HABs) on the Ohio River Using Landsat and Google Earth Engine
by Douglas Kaiser and John J. Qu
Remote Sens. 2025, 17(24), 4010; https://doi.org/10.3390/rs17244010 - 12 Dec 2025
Viewed by 1107
Abstract
Harmful Algal Blooms (HABs) in large river systems present significant challenges for water quality monitoring, with traditional in-situ sampling methods limited by spatial and temporal coverage. This study evaluates the effectiveness of machine learning techniques applied to Landsat spectral data for detecting and [...] Read more.
Harmful Algal Blooms (HABs) in large river systems present significant challenges for water quality monitoring, with traditional in-situ sampling methods limited by spatial and temporal coverage. This study evaluates the effectiveness of machine learning techniques applied to Landsat spectral data for detecting and quantifying HABs in the Ohio River system, with particular focus on the unprecedented 2015 bloom event. Our methodology combines Google Earth Engine (GEE) for satellite data processing with an ensemble machine learning approach incorporating Support Vector Regression (SVR), Neural Networks (NN), and Extreme Gradient Boosting (XGB). Analysis of Landsat 7 and 8 data revealed that the 2015 HAB event had both broader spatial extent (636.5 river miles) and earlier onset (5–7 days) than detected through conventional monitoring. The ensemble model achieved a correlation coefficient of 0.85 with ground-truth measurements and demonstrated robust performance in detecting varying bloom intensities (R2 = 0.82). Field validation using ORSANCO monitoring stations confirmed the model’s reliability (Nash-Sutcliffe Efficiency = 0.82). The integration of multispectral indices, particularly the Floating Algae Index (FAI) and Normalized Difference Chlorophyll Index (NDCI), enhanced detection accuracy by 23% compared to single-index approaches. The GEE-based framework enables near real-time processing and automated alert generation, making it suitable for operational deployment in water management systems. These findings demonstrate the potential for satellite-based HAB monitoring to complement existing ground-based systems and establish a foundation for improved early warning capabilities in large river systems through the integration of remote sensing and machine learning techniques. Full article
(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms (Second Edition))
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