Inversion of sea surface temperature in South China Sea based on machine learning method

Zhenhao Liu; Hongchang He; Donglin Fan; Ziyi Gong

doi:10.1117/12.3007436

30 August 2023 Inversion of sea surface temperature in South China Sea based on machine learning method

Zhenhao Liu, Hongchang He, Donglin Fan, Ziyi Gong

Author Affiliations +

Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 127970K (2023) https://doi.org/10.1117/12.3007436
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China

Abstract

The traditional models for inverting sea surface temperature (SST) have relatively low accuracy and are unable to predict the SST in cloud-covered areas of remote sensing data. This study focuses on the South China Sea and its surrounding areas. Based on the 2022 Himawari-8 satellite imagery data and iQuam2 measured data, a 1x1 sampling window was selected to construct the inversion dataset. The support vector regression (SVR), artificial neural network (ANN), and LightGBM algorithms in machine learning were employed for SST inversion under two scenarios: clear sky and entire region, based on the presence or absence of cloud cover. The inversion results during the testing phase indicate: (1) The LightGBM algorithm demonstrates higher inversion accuracy for the entire region compared to the clear sky scenario, indicating its ability to effectively mitigate cloud interference; (2) Under the entire region scenario, LightGBM achieves a correlation coefficient of 0.9407, mean absolute error of 0.2335°C, and mean square error of 0.1803°C, outperforming other algorithms; (3) The inversion accuracy of machine learning algorithms is significantly higher than that of Himawari8 Level 2 products. These conclusions highlight that the LightGBM algorithm can overcome the limitations of traditional methods and achieve high-precision SST inversion for the entire region.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenhao Liu, Hongchang He, Donglin Fan, and Ziyi Gong "Inversion of sea surface temperature in South China Sea based on machine learning method", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 127970K (30 August 2023); https://doi.org/10.1117/12.3007436

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KEYWORDS

Machine learning

Artificial neural networks

Windows

Clouds

Education and training

Remote sensing

Data modeling

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