International Journal of Marine Science, 2025, Vol.15, No.1, 45-52 http://www.aquapublisher.com/index.php/ijms 46 2 Fundamental Mechanisms of Ocean Remote Sensing 2.1 Electromagnetic spectrum utilization in ocean observations Ocean remote sensing mainly relies on electromagnetic spectrum to collect data. Simply put, it is to use different bands of light to "see" the ocean. Different wavelengths can see different information. For example: the near-infrared (NIR) band is more suitable for observing clear sea water; the short-wave infrared (SWIR) band is more suitable for observing turbid waters (Liu et al., 2019). These bands are important when performing atmospheric correction, which means that when processing images, they can help us "filter" air interference to data. Choosing the right wavelength is very important, especially in some places with complex water quality. If you choose the wrong one, the data will be inaccurate (Wang et al., 2023). 2.2 Interaction between ocean surface and electromagnetic waves Remote sensing depends on the interaction between electromagnetic waves and the sea surface. When the light hits the sea surface, part of it will be reflected back. Through these reflected lights (also known as remote sensing reflectivity Rrs), we can know what is happening in the water. Scientists use some algorithms (such as the semi-analytical algorithm SAA) to convert Rrs data into "optical properties" in seawater. These characteristics can tell us whether there are phytoplankton, debris, colored substances, etc. in the water (Kolluru et al., 2021). This method is particularly important for monitoring seawater color and chlorophyll concentration (He et al., 2024). If the data is inaccurate, it is easy to judge the wrong water quality. 2.3 Atmospheric correction and its role in accurate data retrieval Atmospheric correction (AC) is a critical preprocessing step in ocean remote sensing because it eliminates atmospheric effects in satellite data to obtain accurate ocean information. Various AC algorithms have been developed to deal with challenges posed by different atmospheric conditions. For example, the Intelligent Polarized AC (IPAC) algorithm significantly improves the accuracy of marine color products by processing multi-angle, multi-spectral and polarized data. Similarly, the use of models such as ACOLITE and SeaDAS has been shown to enhance retrieval of remote sensing reflectivity in coastal waters, and general AC methods often fail (Ilori et al., 2019). The development of advanced AC algorithms based on neural networks and other advanced AC algorithms has further improved data accuracy, especially in high-latitude areas where the solar zenith angle is large (Li et al., 2022). 3 Inversion Algorithms in Ocean Remote Sensing 3.1 Principles of ocean parameter inversion 3.1.1 Optical inversion for chlorophyll concentration Remote sensing technology can be used to estimate how much chlorophyll a is in water, which is very useful for understanding water quality. The commonly used method now is to analyze the optical properties of water based on remote sensing reflectivity data. These methods can determine whether there are many phytoplankton in the water, because phytoplankton contains chlorophyll. In the past, people used traditional algorithms, such as the ocean color index. But now, scientists are starting to use machine learning models, like Support Vector Machine (SVM) and XGBoost, which are more accurate when processing MODIS/Aqua satellite data. Some people have combined semi-analytical algorithms (SAA) and absorption decomposition algorithms (ADA) to develop a hybrid algorithm. This method can better analyze absorbent substances like chlorophyll in water (Kolluru et al., 2021). 3.1.2 Microwave inversion for sea surface temperature and salinity To understand the temperature and salinity of the ocean, scientists generally use microwave remote sensing. This type of approach combines multiple remote sensing data, coupled with machine learning models, to estimate temperature, salinity, and the sound velocity profiles associated with them. In order to improve efficiency, they will also use a method called empirical orthogonal function (EOF) to first "simplify" the data, so that the calculation is faster and the results are more accurate (Feng et al., 2024). 3.1.3 Acoustic inversion for seafloor topography and subsurface features To know what the seabed looks like, you can use acoustic inversion technology. This type of technology will
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