Retrieval of Internal Solitary Wave Parameters and Analysis of Their Spatial Variability in the Northern South China Sea Based on Continuous Satellite Imagery
The remote sensing inversion of internal solitary waves (ISWs) enables the retrieval of ISW parameters and facilitates the analysis of their spatial variability. In this study, we utilize continuous optical imagery from the FY-4B satellite to extract real-time ISW propagation speeds throughout their...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/13/2159 |
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| Summary: | The remote sensing inversion of internal solitary waves (ISWs) enables the retrieval of ISW parameters and facilitates the analysis of their spatial variability. In this study, we utilize continuous optical imagery from the FY-4B satellite to extract real-time ISW propagation speeds throughout their evolution from generation to shoaling. ISW parameters are retrieved in the northern South China Sea based on the quantitative relationship between sea surface current divergence and ISW surface features in optical imagery. The inversion method employs a fully nonlinear equation with continuous stratification to account for the strongly nonlinear nature of ISWs and uses the propagation speed extracted from continuous imagery as a constraint to determine a unique solution. The results show that as ISWs propagate from deep to shallow waters in the northern South China Sea, their statistically averaged amplitude initially increases and then decreases, while their propagation speed continuously decreases with decreasing depth. The inversion results are consistent with previous in situ observations. Furthermore, a three-day consecutive remote sensing tracking analysis of the same ISW revealed that the spatial variation in its parameters aligned well with the abovementioned statistical results. The findings provide an effective inversion approach and supporting datasets for extensive ISW monitoring. |
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| ISSN: | 2072-4292 |