About this Research Topic
Remote sensing technology has revolutionized ocean exploration, allowing researchers to monitor and understand various oceanographic processes and phenomena on a global scale.
Remote sensing, which involves the use of satellites and aircraft to collect data about the planet's surface and atmosphere, enables scientists to monitor oceanic conditions in real-time, track changes in sea level, and analyse marine ecosystems. Deep learning plays a pivotal role in enhancing remote sensing technology, revolutionizing how we collect, interpret, and utilize vast amounts of environmental data. By leveraging deep learning, remote sensing can identify subtle patterns and anomalies that traditional methods might miss, such as climate change impacts.
Together, remote sensing and ocean exploration enhance our ability to protect marine environments, sustain biodiversity, and address global challenges like climate change and sustainable development. These advancements not only deepen our scientific knowledge but also inform policy decisions and conservation efforts, ensuring the health and resilience of the world's oceans for future generations.
This Research Topic compiles the latest research and developments in the application of remote sensing techniques, particularly using deep learning methods, to improve our understanding of the oceans.
We invite original research articles, reviews, and case studies on the following topics:
• Advanced remote sensing techniques for ocean observation, including deep learning-based approaches.
• Integration of satellite, aerial, and in-situ data for oceanographic studies using deep learning.
• Machine learning and artificial intelligence algorithms optimized for ocean remote sensing.
• Hyperspectral and multispectral imaging for marine applications enhanced by deep learning techniques.
• SAR (Synthetic Aperture Radar) applications in oceanography with deep learning models.
• Remote sensing of coastal and estuarine environments using deep learning architectures.
• Monitoring of harmful algal blooms using deep learning algorithms applied to remote sensing data.
• Deep learning-based approaches for remote sensing of coral reefs and marine habitats.
• Applications of deep learning in fisheries management utilizing remote sensing data.
• Deep learning-based assessment of climate change impacts on oceans using remote sensing datasets.
Manuscripts relating to practical applications in ecological monitoring and marine target tracking can be submitted to the Research Topic ‘Remote Sensing Applications in Marine Ecology Monitoring and Target Sensing’ https://www.frontiersin.org/research-topics/64528/remote-sensing-applications-in-marine-ecology-monitoring-and-target-sensing.
Remote sensing, which involves the use of satellites and aircraft to collect data about the planet's surface and atmosphere, enables scientists to monitor oceanic conditions in real-time, track changes in sea level, and analyse marine ecosystems. Deep learning plays a pivotal role in enhancing remote sensing technology, revolutionizing how we collect, interpret, and utilize vast amounts of environmental data. By leveraging deep learning, remote sensing can identify subtle patterns and anomalies that traditional methods might miss, such as climate change impacts.
Together, remote sensing and ocean exploration enhance our ability to protect marine environments, sustain biodiversity, and address global challenges like climate change and sustainable development. These advancements not only deepen our scientific knowledge but also inform policy decisions and conservation efforts, ensuring the health and resilience of the world's oceans for future generations.
This Research Topic compiles the latest research and developments in the application of remote sensing techniques, particularly using deep learning methods, to improve our understanding of the oceans.
We invite original research articles, reviews, and case studies on the following topics:
• Advanced remote sensing techniques for ocean observation, including deep learning-based approaches.
• Integration of satellite, aerial, and in-situ data for oceanographic studies using deep learning.
• Machine learning and artificial intelligence algorithms optimized for ocean remote sensing.
• Hyperspectral and multispectral imaging for marine applications enhanced by deep learning techniques.
• SAR (Synthetic Aperture Radar) applications in oceanography with deep learning models.
• Remote sensing of coastal and estuarine environments using deep learning architectures.
• Monitoring of harmful algal blooms using deep learning algorithms applied to remote sensing data.
• Deep learning-based approaches for remote sensing of coral reefs and marine habitats.
• Applications of deep learning in fisheries management utilizing remote sensing data.
• Deep learning-based assessment of climate change impacts on oceans using remote sensing datasets.
Manuscripts relating to practical applications in ecological monitoring and marine target tracking can be submitted to the Research Topic ‘Remote Sensing Applications in Marine Ecology Monitoring and Target Sensing’ https://www.frontiersin.org/research-topics/64528/remote-sensing-applications-in-marine-ecology-monitoring-and-target-sensing.
Keywords: Remote Sensing, Oceanography, Deep Learning, Climate Change
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
