International Journal of Marine Science, 2025, Vol.15, No.1, 45-52 http://www.aquapublisher.com/index.php/ijms 45 Research Perspective Open Access Mechanisms and Applications of Ocean Remote Sensing: Inversion Algorithms Linhua Zhang, Lingfei Jin Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China Corresponding author: lingfei.jin@jicat.org International Journal of Marine Science, 2025, Vol.15, No.1, doi: 10.5376/ijms.2025.15.0005 Received: 17 Jan., 2025 Accepted: 20 Feb., 2025 Published: 28 Feb., 2025 Copyright © 2025 Zhang and Jin, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhang L.H., and Jin L.F., 2025, Mechanisms and applications of ocean remote sensing: inversion algorithms, International Journal of Marine Science, 15(1): 45-52 (doi: 10.5376/ijms.2025.15.0005) Abstract This study reviews the fundamental mechanisms of ocean remote sensing, with a focus on the interaction between electromagnetic waves and the ocean surface, as well as the importance of atmospheric correction. It systematically analyzes inversion algorithms used to extract oceanic parameters such as chlorophyll concentration, sea surface temperature, and seafloor topography. The study also explores the integrated application of machine learning and artificial intelligence to optimize inversion accuracy and address algorithmic challenges. The findings indicate that advanced inversion algorithms can significantly enhance the accuracy of oceanic parameter extraction, which is crucial for obtaining key data on sea surface temperature, chlorophyll concentration, and seafloor topography. Case studies demonstrate the application of inversion algorithms in monitoring coral reef degradation, tracking marine oil spills, and assessing sea level rise. This study highlights the importance of inversion algorithms in improving the precision of ocean observations, aiming to provide a scientific basis for the optimization of ocean observation technologies and promote higher accuracy and broader-scale ocean monitoring capabilities. Keywords Ocean remote sensing; Inversion algorithms; Sea surface temperature; Chlorophyll concentration; Machine learning 1 Introduction Ocean remote sensing is an important tool for observing and understanding the sea. The ocean is huge and changes quickly. A lot of information is not easy to measure by traditional methods, and remote sensing can help us collect this data more easily. It has many uses in areas such as climate research, marine biology and marine science. Through remote sensing technology, we can obtain information such as the color, temperature, wave height of sea water, and can continuously observe it on a large scale and for a long time. For example, parameters such as chlorophyll a concentration and water transparency are directly related to whether the water quality is good. Remote sensing of these indicators can help us make more accurate ecological and geochemical models (Zhu and Huang, 2021; Zhao et al., 2022; Bisson et al., 2023). Remote sensing technology has developed rapidly in recent years. At first, people mainly used some empirical formulas to calculate, but now there are new methods, such as semi-analytical methods and machine learning algorithms, which can make the data we obtain more accurate and wider (D’Alimonte et al., 2016; Kolluru et al., 2021). Now, researchers have developed some hybrid algorithms to better analyze ocean color data, especially the distribution of absorbent substances. These technologies allow us to see the changes in light in seawater. In addition, machine learning methods such as support vector machines and deep learning models are also used to estimate parameters such as chlorophyll a concentration and seawater sound speed, and the effect is better than traditional methods (Chen et al., 2024). This study reviews the latest advances in these inversion algorithms, including hybrid models and machine learning methods, to see how they perform in ocean parameter inversion. We will also analyze their working mechanisms and practical applications, and discuss how to use new technologies to improve the accuracy and reliability of data. The ultimate goal is to promote the development of more powerful and more general inversion algorithms to make ocean remote sensing more useful and efficient.
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