As a common ocean phenomenon, ISWs appear as bright and dark patterns in optical remote sensing images. Of all remote sensing technologies, optical remote sensing allows for the earliest detection of ISWs. Shand (1953) was the first to use aerial photography to record bright and dark patterns created by ISWs in the Georgia Strait. Apel et al. (1975) first identified ISWs in remote sensing images, making it possible to investigate ISWs using optical remote sensing. In the study of optical remote sensing imaging, the sea surface is usually considered to be composed of tilted facets (Cox & Munk, 1954). The optical sensor receives the sunlight reflected from suitably tilted facets of the water surface. Accordingly, Cox and Munk (1956) proposed the relationship between the radiance and the probability density function of the slope of the sea surface, which laid a theoretical foundation for the study of the optical remote sensing imaging of ISWs. The convergent and divergent modulations of ISWs at the sea surface changed the roughness of the sea surface, and the reflected light altered as the inclination of suitably tilted facets responded to the hydrodynamic changes (Alpers, 1985;Melsheimer & Keong, 2001). Hence, the ISWs appeared as bright and dark patterns in the optical remote sensing images (Liu & D'Sa, 2019). Furthermore, geometric angles play an important role in optical imaging. Jackson and Alpers (2010) introduced the concept of the critical angle, the angle of the sensor relative to the reflected light at which the order of bright and dark patterns will be reversed when exceeded by a sensor. This concept helped explain the differences between images of ISWs taken with and without sunglint. Based on the above studies, the different types of ISWs may produce different patterns (see in Figure 1) in optical remote sensing images (Huang et al., 2012) that

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The propagation speed is one of the important parameters of the internal solitary waves(ISWs). How to obtain the ISWs speed through optical remote sensing images accurately and quickly is an important problem to be solved. In this paper, we simulate ISWs optical remote sensing imaging and obtain an experimental database and build the ISWs speed inversion models based on a single-scene optical remote sensing image by using the least squares method and the support vector machine. The accuracy of the ISW speed inversion models were tested by using MODIS Image and GF-4 image data of the South China Sea. The study results show that: The least squares ISW speed inversion model can give the regression equation, which is more intuitive and has less accuracy in the water depth range from 300 meters to 399 meters, while the support vector machine ISW speed inversion model has high accuracy in the water depth range from 400 meters to 1200 meters and from 83 meters to 299 meters. Therefore, the two kinds of ISW speed inversion models have different advantages, and can be applied to the inversion of the ISW speed in the real ocean.

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