Editor-in-Chief: Xinliang Tian
Shanghai Jiao Tong University, China
Frequency: Continuously
Submission to First Decision: 10 days
Submission to Acceptance: 60 days
Accept to Publish: 15 days
© 2026 Bon View Publishing Pte Ltd.
Aims and Scope
The Remote Sensing for Sustainable Oceans (RSSO) aims to publish high-quality research that drives innovation in remote sensing applications for marine and coastal environments and has a direct impact on ocean sustainability. The journal welcomes original research articles and review articles in the following areas:
- Advancements in Marine Remote Sensing Technology: Including the development and application of new algorithms, tools, and methods.
- Oceanographic Data Acquisition and Processing: Involving the acquisition, processing, and analysis of oceanographic data.
- Coastal and Marine Environment Monitoring: Monitoring of coastal changes, marine pollution, and ecological monitoring.
- Polar Glacier Remote Sensing: Remote sensing monitoring of polar glaciers and sea ice and their response to global climate change.
- Coral Reef Ecosystem Studies: Remote sensing assessment and conservation of coral reef health.
- Marine Biodiversity Assessment: Marine biodiversity assessment using remote sensing technology.
- Satellite-Based Ocean Monitoring: Satellite monitoring of sea surface temperature, salinity, currents, etc.
- Marine Health Monitoring: Remote sensing assessment of marine health, including ocean acidification and hypoxia.
- Sustainable Marine Ecology: Research on how remote sensing technologies support the sustainability of marine ecosystems.
- Coastal Fisheries and Shipping Industries: Applications of remote sensing technology in fishery resource management and shipping safety.
- Sea Surface Temperature Mapping: Remote sensing monitoring of sea surface temperature and its impact on marine ecosystems.
- Artificial Intelligence in Marine Remote Sensing: The application of AI technology in marine remote sensing, including image recognition and data processing.
- Machine Learning Applications in Oceanography: The application of machine learning technology in oceanography, including pattern recognition and predictive modeling.
- Big Data Analytics for Marine Science: The application of big data analysis in ocean science, particularly in the processing and interpretation of remote sensing data.
- Oceanographic Modeling and Simulation: Oceanographic modeling and simulation studies integrated with remote sensing data, including the impacts of climate change on the oceans.
