Enhancing Observational Capabilities of Marine Environmental Dynamics Through Machine Learning and Multi-Source Remote Sensing Data Fusion
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 38
Special Issue Editors
Interests: marine remote sensing data reconstruction; ocean parameter retrieval; multi-source ocean data fusion
Interests: marine big data and earth system science; geospatial artificial intelligence (GeoAI); spatial modelling and spatiotemporal prediction
Special Issues, Collections and Topics in MDPI journals
Interests: marine spatiotemporal data mining theory and methods; monitoring and assessment of ocean sustainable development goals; digital twin ocean
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Marine environmental dynamics are fundamental to understanding climate regulation, ocean processes, and ecosystem functioning. Remote sensing has significantly advanced large-scale ocean observation by providing continuous and synoptic measurements. However, single-sensor observations are often limited in their ability to capture complex ocean processes due to constraints in spatial resolution, temporal coverage, and sensitivity to different physical properties. Recent developments in multi-source remote sensing, including optical, thermal, and microwave sensors, have enabled more comprehensive observation of marine environments. At the same time, machine learning techniques offer powerful tools for extracting nonlinear relationships, integrating heterogeneous datasets, and improving the accuracy of ocean parameter retrieval and prediction. These advances lead to new opportunities to enhance the observational capability of marine environmental dynamics, though challenges remain in data fusion, scale consistency, and model interpretability.
This Special Issue aims to explore innovative methods that enhance the observation and understanding of marine environmental dynamics by integrating multi-source remote sensing data and machine learning approaches. It will promote interdisciplinary studies combining oceanography, remote sensing, and data science. These topics align closely with the scope of Remote Sensing, focusing on advanced data acquisition, processing, and analysis techniques for Earth observation. This Special Issue emphasizes data fusion, intelligent retrieval, and spatiotemporal modelling of marine environments, as well as the integration of satellite, in situ, and model datasets to improve observational accuracy and efficiency.
Articles may address, but are not limited to, the following topics:
- Multi-source remote sensing data fusion for ocean observation;
- Machine learning and deep learning for marine parameter retrieval;
- Spatiotemporal analysis and modelling of ocean dynamics;
- Cross-scale observation of marine processes;
- Integration of satellite, in situ, and numerical model data;
- Uncertainty analysis and model interpretability;
- Monitoring of marine ecosystems and environmental change.
Dr. Bo Ping
Dr. Sensen Wu
Prof. Dr. Cunjin Xue
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
- marine environmental dynamics
- multi-source oceanic data fusion
- spatiotemporal process analysis
- ocean monitoring
- ocean parameter retrieval
- data-driven modelling
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