International Journal of Marine Science, 2025, Vol.15, No.1, 45-52 http://www.aquapublisher.com/index.php/ijms 50 6.3 Assessing polar ice melt and sea level rise Inversion algorithms can also be used in polar regions to help us understand ice melting and sea level rise trends. By analyzing ocean color data, scientists can better judge the changes in ice. If some auxiliary observation data are added, such as field measurements, the results will be more accurate (Bisson et al., 2023). There are also researches that combine deep learning technology with remote sensing data. This approach tracks changes in icebergs and sea ice more accurately, helping us analyze seasonal changes and long-term trends (Li et al., 2020). These studies are very valuable in predicting future climate and sea level changes. 7 Future Directions in Ocean Remote Sensing and Inversion Algorithms 7.1 Enhancing algorithm precision through data assimilation Now, data assimilation is a new way to improve the accuracy of ocean remote sensing algorithms. Simply put, putting data from different sources together will have better results. For example, we can combine information such as seawater temperature, salinity, and sea surface height in satellite data to invert the temperature and salinity distribution underwater (Zhao et al., 2024). There is also a technology called neural network observation operator, which can also integrate complex data such as underwater acoustic propagation. Research has found that this approach can greatly improve the accuracy of forecasting ocean states (Storto et al., 2021). At the same time, combining semi-analytical algorithms and absorption decomposition algorithms can also reduce errors and make the inversion result more reliable (Kolluru et al., 2021). 7.2 Integration of quantum remote sensing technologies Quantum remote sensing is a "new direction" in current remote sensing technology. It uses quantum technology to improve sensitivity and resolution, that is, it can see more clearly and measure more accurately. If this technology is used in ocean remote sensing, it may make the inversion algorithm more accurate, and it can also help us see more and understand more. Although this aspect is still developing, the prospects are worth looking forward to. 7.3 Expanding global collaboration for comprehensive ocean monitoring Cooperation among countries is key to more comprehensively monitoring the global ocean. Through cooperation, different countries can share data, technology and experience and develop stronger models together. A research team has used machine learning to develop a global chlorophyll a concentration inversion model, which is inseparable from large international databases like the SeaWiFS dataset (Chen et al., 2024). Cooperation can also help develop a more stable sound speed inversion framework, use a variety of remote sensing data, and combine it with AI methods, which can improve monitoring capabilities for large-scale sea areas (Feng et al., 2024). With these international cooperation projects, scientists can better understand the motion patterns of the ocean, while making the application of remote sensing more accurate and useful. Acknowledgments Thanks Mr F. Zhou from the Institute of Life Science of Jiyang College of Zhejiang A&F University for his reading and revising suggestion. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Alevizos E., Oikonomou D., Argyriou A., and Alexakis D., 2022, Fusion of drone-based RGB and multi-spectral imagery for shallow water bathymetry inversion, Remote Sensing, 14: 1127. https://doi.org/10.3390/rs14051127 Amani M., Moghimi A., Mirmazloumi S.M., Ranjgar B., Ghorbanian A., Ojaghi S., Ebrahimy H., Naboureh A., Nazari M., Mahdavi S., Moghaddam S., Asiyabi R., Ahmadi S., Mehravar S., Mohseni F., and Jin S., 2022, Ocean remote sensing techniques and applications: a review, Water, 14(21): 3400. https://doi.org/10.3390/w14213400 Bisson K., Werdell P., Chase A., Kramer S., Cael B., Boss E., McKinna L., and Behrenfeld M., 2023, Informing ocean color inversion products by seeding with ancillary observations, Optics Express, 31(24): 40557-40572. https://doi.org/10.1364/oe.503496
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