Retrieval of Significant Wave Height in Coastal Seas of China from GaoFen-3 Satellites Based on Deep Learning

Sun, Fengjia; Li, Xing; Li, Xiao-Ming; Ren, Yongzheng; Wu, Ke

doi:10.3390/rs18060966

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Retrieval of Significant Wave Height in Coastal Seas of China from GaoFen-3 Satellites Based on Deep Learning

by

Fengjia Sun

¹,

Xing Li

¹,

Xiao-Ming Li

^1,2,*

,

Yongzheng Ren

³ and

Ke Wu

³

¹

Hainan Aerospace Technology Innovation Center, Wenchang 571399, China

²

Aerospace Information Technology University, Jinan 250200, China

³

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

^*

Author to whom correspondence should be addressed.

Remote Sens. 2026, 18(6), 966; https://doi.org/10.3390/rs18060966

Submission received: 6 February 2026 / Revised: 17 March 2026 / Accepted: 19 March 2026 / Published: 23 March 2026

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation—4th Edition)

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Abstract

The acquisition of significant wave height (SWH) in coastal seas is significantly important to human activities. The Gaofen-3 (GF-3) satellites, comprising GF-3, GF-3B and GF-3C, are independently developed operational SAR of China, capable of providing high-precision, high-resolution, multi-polarization coastal ocean wave observations. In order to obtain SWH in coastal seas, the retrieval of SWH using Quad-Polarization Stripmap (QPS) mode data from GF-3 satellites based on the deep learning method is implemented in this study. Furthermore, to obtain more SWH data, the polarization ratio model was applied to the Fine Stripmap (FS) mode data and Ultra Fine Stripmap (UFS) mode data to extend the model application. Comparisons with ECMWF Reanalysis v5 (ERA5) wave heights show that the QPS mode SWH retrieval achieves a root mean square error (RMSE) of 0.33 m. For the FS mode, the RMSE is 0.44 m (vs. ERA5) and 0.52 m (vs. altimeter). For the UFS mode, the RMSE is 0.39 m (vs. ERA5). Evaluation results indicate the feasibility of the proposed method for coastal SWH retrieval.

Keywords: Gaofen-3 satellites; significant wave height; retrieval; deep learning

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MDPI and ACS Style

Sun, F.; Li, X.; Li, X.-M.; Ren, Y.; Wu, K. Retrieval of Significant Wave Height in Coastal Seas of China from GaoFen-3 Satellites Based on Deep Learning. Remote Sens. 2026, 18, 966. https://doi.org/10.3390/rs18060966

AMA Style

Sun F, Li X, Li X-M, Ren Y, Wu K. Retrieval of Significant Wave Height in Coastal Seas of China from GaoFen-3 Satellites Based on Deep Learning. Remote Sensing. 2026; 18(6):966. https://doi.org/10.3390/rs18060966

Chicago/Turabian Style

Sun, Fengjia, Xing Li, Xiao-Ming Li, Yongzheng Ren, and Ke Wu. 2026. "Retrieval of Significant Wave Height in Coastal Seas of China from GaoFen-3 Satellites Based on Deep Learning" Remote Sensing 18, no. 6: 966. https://doi.org/10.3390/rs18060966

APA Style

Sun, F., Li, X., Li, X.-M., Ren, Y., & Wu, K. (2026). Retrieval of Significant Wave Height in Coastal Seas of China from GaoFen-3 Satellites Based on Deep Learning. Remote Sensing, 18(6), 966. https://doi.org/10.3390/rs18060966

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Retrieval of Significant Wave Height in Coastal Seas of China from GaoFen-3 Satellites Based on Deep Learning

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