About this Research Topic
In the last four decades, several ocean color missions have been launched and produced a wealth of data that has been used to study various aspects of the ocean, including primary productivity, phytoplankton blooms, and changes in ocean chemistry. The study of time series of ocean color remote sensing observations presents key challenges including correction of atmospheric effects, sensor calibration, noise and artifact removal, integration of data from multiple platforms, analysis of long-term trends and patterns, and improved predictions of future ocean conditions.
Addressing these challenges is crucial for advancing our understanding of the ocean and its role in the Earth system. In recent years, the application of machine learning and other data analysis techniques has greatly advanced our understanding of the ocean and allowed for the detection of long-term trends and patterns that would not have been possible with traditional methods. These analyses have provided new insights into the functioning of the ocean and have led to improved predictions of future ocean conditions. Overall, the time series of ocean color remote sensing observations and their analyses represent a powerful tool for understanding and monitoring the ocean, and they continue to play an important role in advancing our knowledge of the ocean and its role in the Earth system.
The scope of this research topic focuses on the use of satellite-based measurements of the optical properties of the ocean to study oceanographic and biological processes over time. It encompasses the correction of data for atmospheric and environmental effects, the removal of noise and artifacts, the calibration of sensors, the integration of data from multiple platforms, and the analysis of long-term trends and patterns in the ocean color data.
1) Machine learning techniques for ocean color applications
2) Integration of data from multiple satellite missions and platforms
3) Analysis of long-term trends and patterns in ocean color data
4) Improved predictions of future ocean conditions based on time series of ocean color remote sensing observations
5) Study of interannual and interdecadal variability in ocean color data
6) Assessment of the impact of climate change on the ocean using ocean color remote sensing observations
7) Time series analysis of insitu observations (including ship, Argo, profilers, buoys, AERONET-OC or similar networks) and model-based results
Keywords: Time Series, Ocean Color, Remote Sensing, Machine Learning, Deep Learning
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