About this Research Topic
Ocean color remote sensing is an essential component in understanding oceanic and biological dynamics, leveraging satellite measurements to analyze optical properties of ocean waters. Over the past several decades, advancements in this technology have enabled researchers to capture and interpret changes across various temporal scales which are consequential for monitoring ocean dynamics and productivity effectively. Despite these advancements, the field faces ongoing challenges such as atmospheric corrections, sensor calibration, and data integration, heightening the need for improved techniques and methodologies within the research community.
This Research Topic aims to enhance the interpretation of time series ocean color remote sensing data and improve the prediction models concerning future ocean conditions. By tackling prevailing challenges like data noise, atmospheric interference, and multi-platform data inconsistencies, the goal is to refine data accuracy and utility. The integration of machine learning and advanced data analysis techniques stands at the forefront of this initiative, poised to revolutionize the understanding of ocean long-term trends and contribute significantly to oceanographic science.
To gather further insights in ocean color remote sensing applied over extended periods, we welcome articles addressing, but not limited to, the following themes:
• Machine learning techniques for ocean color applications
• Integration of data from multiple satellite missions and platforms
• Analysis of long-term trends and patterns in ocean color data
• Improved predictions of future ocean conditions based on time series observations
• Study of interannual and interdecadal variability in ocean color data
• Assessment of the impact of climate change on ocean dynamics through remote sensing
• Comparative studies using in-situ observations and remote sensing datasets
This structured exploration will hopefully bring new advancements in understanding the ongoing and predictable changes in our oceans facilitated by remote sensing technology.
This Research Topic aims to enhance the interpretation of time series ocean color remote sensing data and improve the prediction models concerning future ocean conditions. By tackling prevailing challenges like data noise, atmospheric interference, and multi-platform data inconsistencies, the goal is to refine data accuracy and utility. The integration of machine learning and advanced data analysis techniques stands at the forefront of this initiative, poised to revolutionize the understanding of ocean long-term trends and contribute significantly to oceanographic science.
To gather further insights in ocean color remote sensing applied over extended periods, we welcome articles addressing, but not limited to, the following themes:
• Machine learning techniques for ocean color applications
• Integration of data from multiple satellite missions and platforms
• Analysis of long-term trends and patterns in ocean color data
• Improved predictions of future ocean conditions based on time series observations
• Study of interannual and interdecadal variability in ocean color data
• Assessment of the impact of climate change on ocean dynamics through remote sensing
• Comparative studies using in-situ observations and remote sensing datasets
This structured exploration will hopefully bring new advancements in understanding the ongoing and predictable changes in our oceans facilitated by remote sensing technology.
Keywords: Time Series, Ocean Color, Remote Sensing, Machine Learning, Deep Learning
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.
