Inversion of sea surface salinity in Bay of Bengal based on Catboost algorithm

Ziyi Gong; Hongchang He; Donglin Fan; Jingran Sheng; Xinyue Zhang

doi:10.1117/12.3007408

30 August 2023 Inversion of sea surface salinity in Bay of Bengal based on Catboost algorithm

Ziyi Gong, Hongchang He, Donglin Fan, Jingran Sheng, Xinyue Zhang

Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 1279718 (2023) https://doi.org/10.1117/12.3007408
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China

Abstract

In view of the complexity and low accuracy of traditional sea surface salinity inversion models, this study took the Bay of Bengal sea area (82°~91°E, 9°~15°N) as the research area, and used the L-band sounding SMOS Level2 satellite product data, Argo measured data and characteristic data. Catboost algorithm was used to construct the remote sensing inversion model of sea surface salinity, and grid search algorithm was used to optimize the model parameters, so as to improve the accuracy of SMOS satellite products. The validation results show that the root mean square error (RMSE) of the Catboost model's test set inversion of SSS is 0.2929psu, and the mean absolute error (MAE) is 0.1885psu. Comparison and analysis using measured data show that the Catboost model's inversion of SSS has a high degree of consistency with Argo data. The validation also analyzed the RMSE errors between the model's inversion results and the Argo measured results for each month in 2021, revealing the potential relationship between climate and meteorological variables in the Bay of Bengal and changes in salinity.

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

Citation Download Citation

Ziyi Gong, Hongchang He, Donglin Fan, Jingran Sheng, and Xinyue Zhang "Inversion of sea surface salinity in Bay of Bengal based on Catboost algorithm", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 1279718 (30 August 2023); https://doi.org/10.1117/12.3007408

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KEYWORDS

Data modeling

Satellites

Error analysis

Climatology

Machine learning

Climate change

Mathematical optimization

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