Inversion of sea surface ocean current in South China Sea, based on machine learning method

Bozhi Pan; Hongchang He; Donglin Fan

doi:10.1117/12.3024044

21 February 2024 Inversion of sea surface ocean current in South China Sea, based on machine learning method

Bozhi Pan, Hongchang He, Donglin Fan

Proceedings Volume 12988, Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023); 129880E (2024) https://doi.org/10.1117/12.3024044
Event: Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023), 2023, Xi’an, China

Abstract

This paper addresses the problems of traditional surface current inversion methods, including low model accuracy and insufficient spatial and temporal resolution of the corresponding data, especially the challenges in processing remote sensing image data bearing cloud cover and performing large-scale time series inversion. In this study, several machine learning methods, including Random Forest, LightGBM, and Deep Neural Network, based on Himawari-8 satellite data and AVIS measurements, are employed to realize surface currents' inversion in the South China Sea. The experimental results fully demonstrate that using Himawari-8 full-band feature data for the inversion of sea surface currents even under cloud coverage is feasible. Moreover, the accuracy of the LightGBM model is better than other algorithms, with a correlation coefficient of 0.8856 and a root-mean-square error (RMSE) of 6.15 cm/s. These conclusions clearly show that the LightGBM algorithm is able to overcome the limitations of the traditional methods and realize the inversion of surface currents for the whole region.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Bozhi Pan, Hongchang He, and Donglin Fan "Inversion of sea surface ocean current in South China Sea, based on machine learning method", Proc. SPIE 12988, Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023), 129880E (21 February 2024); https://doi.org/10.1117/12.3024044

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KEYWORDS

Satellites

Machine learning

Education and training

Solar radiation models

Data modeling

Random forests

Remote sensing

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