Advances in Machine Learning and Multi-Source Remote Sensing for Monitoring Marine Aquatic Environments
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 348
Special Issue Editors
Interests: ocean color; marine science; ocean carbon cycle; air–sea carbon flux; remote sensing-based ocean biogeochemistry; oceanic LiDAR
Interests: ocean color; marine science; LiDAR and remote sensing; LiDAR simulation; oceanic detection; oceanic LiDAR
Special Issue Information
Dear Colleagues,
Coastal and marine aquatic environments are critical to global biogeochemical cycles, ecosystem services, and human well-being, yet they face increasing pressures from nutrient enrichment, pollution, sedimentation, and climatalogical extremes such as marine heatwaves. These stressors lead to degradation, biodiversity loss, harmful algal blooms (HABs), and disruptions to carbon cycling. Remote sensing technologies—ranging from spaceborne and airborne sensors to in situ platforms—have advanced rapidly, enabling unprecedented monitoring of water quality, ecosystem dynamics, sediment transport, and carbon fluxes. Coupling these observations with modeling and field data is essential to enabling sustainable management and policy support.
This Special Issue, “Advances in Machine Learning and Multi-Source Remote Sensing for Monitoring Marine Aquatic Environments”, highlights recent progress in the use of cutting-edge technologies to improve detection, quantification, and forecasting of aquatic processes. Machine learning offers novel solutions for analyzing large, heterogeneous datasets; capturing complex non-linear patterns; and enhancing model accuracy and transferability. Combined with multi-source remote sensing, it provides robust frameworks for environmental monitoring, early warning, and ecosystem management under global change.
We invite authors to contribute original research, methodological developments, case studies, or reviews on (but not limited to) the following themes:
- Machine learning algorithms and frameworks for aquatic monitoring;
- Multi-sensor data fusion integrating satellite, airborne, and in situ observations;
- AI-driven detection and forecasting of harmful algal blooms;
- Deep learning applications in aquatic image classification and anomaly detection;
- Monitoring water quality, primary productivity, and carbon fluxes.
We hope that this Special Issue will provide a timely overview of how machine learning and multi-source remote sensing are reshaping the monitoring of aquatic environments.
Dr. Siqi Zhang
Dr. Zhenhua Zhang
Dr. Mengyu Li
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
- coastal remote sensing
- aquatic environments
- sediment transport and flux
- water pollution
- harmful algal blooms (HABs)
- ocean color
- carbon flux
- marine ecosystems
- biogeochemical monitoring
- big data
- machine learning
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