The Influence of Optical Imaging Features and Stratification Parameters on the Inversion of ISW Amplitudes

The amplitude of internal solitary waves (ISWs) is a crucial parameter characterizing their properties. Leveraging machine learning and optical remote sensing images for ISW amplitude inversion has proven highly efficient. However, determining the best input features in the inversion model is often overlooked. This study addresses the feature selection problem in ISW amplitude inversion using a random forest (RF) method. The peak-to-peak distance and relative grayscale differences significantly influence ISW amplitude inversion. When solely using imaging features for ISW amplitude inversion, more significant errors are observed for low-amplitude ISWs due to their weak modulation. In amplitude inversion, selecting the dimensionless ISW amplitude for the output is necessary because it better represents the amplitude magnitude. We find that adding stratification parameters improves the inversion effect, especially the depth ratio. Thus, the impact of physical mechanisms on ISW amplitude inversion is pivotal, and incorporating more hydrological parameters as inputs would lead to further improvements in ISW amplitude inversion.