About this Research Topic
Exploring and understanding ocean ecology and the global carbon cycle is pivotal for addressing climate change and ensuring sustainable ocean management. Remote sensing from airborne and space borne sensors have revolutionized our ability to monitor and study these vast and dynamic systems from distances. The latest advancements in ocean color and LiDAR remote sensing, along with the emergence of BGC-Argo technology, offer unprecedented opportunities for more accurate and detailed observation and analysis of oceanic processes.
Ocean color remote sensing has long been a cornerstone for assessing phytoplankton biomass, primary productivity, and other critical oceanographic variables. Recent innovations in this field, especially the launch of PACE and HY-1E satellites, have enhanced sensor capabilities, data resolution, and algorithmic approaches, thereby improving our understanding of ocean ecosystems. Meanwhile, LiDAR (Light Detection and Ranging) remote sensing technology has expanded its reach from terrestrial and atmospheric applications to marine environments, providing unique insights into ocean surface and subsurface structures. LiDAR remote sensing presents a renewed opportunity to overcome long-standing limitations associated with ocean color data.
The integration of these technologies with BGC-Argo floats, which measure biogeochemical parameters such as oxygen, nitrate, and pH, represents a significant advancement in our observational capabilities. This multi-faceted approach, combined with innovative data fusion techniques and the application of artificial intelligence, holds great promise for advancing our understanding of ocean biology and the carbon cycle.
The goal of this collection is to compile comprehensive reviews and original research articles that showcase the most recent developments and applications in remote sensing technologies for ocean ecology and the carbon cycle. By bringing together leading experts and cutting-edge research, this research topic aims to provide a platform for disseminating significant advancements and fostering collaborations that will drive the future of oceanographic remote sensing and its applications in ecological and carbon cycle studies.
Potential topics include but are not limited to the following:
• Comprehensive reviews of ocean color or LiDAR remote sensing techniques.
• Innovations in ocean color remote sensing technology, including in situ optics.
• Recent advancements in LiDAR remote sensing technology.
• Recent advancements in BGC-Argo technology.
• Novel algorithms for the analysis of ocean color or LiDAR remote sensing data and novel findings.
• Utilization of space-borne LiDAR in ocean biology and carbon studies (e.g., CALIPSO, ICESat-2).
• Integrative data fusion approaches combining ocean color, LiDAR, Bgc-Argo, and ocean numerical modeling, among others.
• Application and integration of AI in remote sensing and its implications.
• Exploring the new frontiers opened by PACE, HY1-E and CALIGOLA projects, as well as other operational mission.
Ocean color remote sensing has long been a cornerstone for assessing phytoplankton biomass, primary productivity, and other critical oceanographic variables. Recent innovations in this field, especially the launch of PACE and HY-1E satellites, have enhanced sensor capabilities, data resolution, and algorithmic approaches, thereby improving our understanding of ocean ecosystems. Meanwhile, LiDAR (Light Detection and Ranging) remote sensing technology has expanded its reach from terrestrial and atmospheric applications to marine environments, providing unique insights into ocean surface and subsurface structures. LiDAR remote sensing presents a renewed opportunity to overcome long-standing limitations associated with ocean color data.
The integration of these technologies with BGC-Argo floats, which measure biogeochemical parameters such as oxygen, nitrate, and pH, represents a significant advancement in our observational capabilities. This multi-faceted approach, combined with innovative data fusion techniques and the application of artificial intelligence, holds great promise for advancing our understanding of ocean biology and the carbon cycle.
The goal of this collection is to compile comprehensive reviews and original research articles that showcase the most recent developments and applications in remote sensing technologies for ocean ecology and the carbon cycle. By bringing together leading experts and cutting-edge research, this research topic aims to provide a platform for disseminating significant advancements and fostering collaborations that will drive the future of oceanographic remote sensing and its applications in ecological and carbon cycle studies.
Potential topics include but are not limited to the following:
• Comprehensive reviews of ocean color or LiDAR remote sensing techniques.
• Innovations in ocean color remote sensing technology, including in situ optics.
• Recent advancements in LiDAR remote sensing technology.
• Recent advancements in BGC-Argo technology.
• Novel algorithms for the analysis of ocean color or LiDAR remote sensing data and novel findings.
• Utilization of space-borne LiDAR in ocean biology and carbon studies (e.g., CALIPSO, ICESat-2).
• Integrative data fusion approaches combining ocean color, LiDAR, Bgc-Argo, and ocean numerical modeling, among others.
• Application and integration of AI in remote sensing and its implications.
• Exploring the new frontiers opened by PACE, HY1-E and CALIGOLA projects, as well as other operational mission.
Keywords: Ocean color, Ocean optics, LiDAR, BGC-Argo, AI, Machine learning, PACE, HY1-E
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