Linhua Zhang , Lingfei Jin

Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
International Journal of Marine Science, 2025, Vol. 15, No. 1   
Received: 17 Jan., 2025    Accepted: 20 Feb., 2025    Published: 28 Feb., 2025

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.

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.

Ocean remote sensing; Inversion algorithms; Sea surface temperature; Chlorophyll concentration; Machine learning