Deep learning retrieval method for global ocean significant wave height by integrating spaceborne GNSS-R data and multivariable parameters

doi:10.11947/j.AGCS.2024.20230050

Abstract

Abstract:

Global navigation satellite system-reflectometry (GNSS-R), as an emerging observation method, has recently been applied to the retrieval of significant wave height (SWH). Existing studies typically use extracting features from delay Doppler maps (DDMs) to construct empirical geophysical model functions (GMFs) for SWH retrieval. However, using multiple variable parameters as model inputs poses significant challenges. Therefore, this article proposes a deep learning network model (named GloWH-Net) that integrates spaceborne GNSS-R data and multivariate parameters to invert global sea surface SWH. To verify the performance of the proposed model, ERA5, Wavewatch Ⅲ (WW3), and AVISO SWH data were used as reference data for extensive testing to evaluate the SWH retrieval performance of the GloWH-Net model and previous models (i.e. empirical and machine learning models). The results showed that when ERA5, WW3, and AVISO SWH were used as reference data respectively, the root mean square error (RMSE) of the proposed GloWH-Net model for retrieving SWH were 0.330 m, 0.393 m, and 0.433 m, respectively, the correlation coefficients (CC) were 0.91, 0.89, and 0.84, respectively. Compared with the empirical combination model based on the minimum variance estimator (MVE), the RMSE of SWH retrieval is reduced by 53.45%, 48.06%, and 40.63%, respectively; Compared to bagging tree (BT) machine learning model, the RMSE of SWH retrieval decreased by 21.92%, 18.72%, and 4.47%, respectively. This indicates that the deep learning method proposed in this article has significant advantages in retrieving global sea surface SWH.

Key words: global navigation satellite system-reflectometry, delay Doppler maps, ocean significant wave height, empirical model, deep learning model

CLC Number:

P237

Jinwei BU, Kegen YU, Qiulan WANG, Linghui LI, Xinyu LIU, Xiaoqing ZUO, Jun CHANG. Deep learning retrieval method for global ocean significant wave height by integrating spaceborne GNSS-R data and multivariable parameters[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(7): 1321-1335.

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模型	DDMs	与DDM相关	与接收机相关	与几何相关	其他辅助参数
BT/SVM	BRCS，effectivescattering area	DDMA，LES，TES，LEWS，TEWS，SNR	gps_eirp，sp_rx_gain	sp_inc_angle，sp_lat，sp_lon，RCG	风速、风向、水深、降雨量
ANN	—
GloWH-Net	BRCS，effectivescattering area

模型	拟合参数			拟合性能指标
模型	a	b	c	RMSE/m	R²
经验模型（DDMA方法）	0.953 3	-0.204 9	-0.094 5	0.712	0.25
经验模型（LEWS方法）	0.857 0	-0.228 7	-0.013 9	0.709	0.26

模型	RMSE/m	Bias/m	CC	MAPE/（%）
经验模型（DDMA方法）	0.715	0.004	0.49	31.18
经验模型（LEWS方法）	0.709	0.001	0.51	30.78
经验模型（DDMA+LEWS方法）	0.709	0.002	0.51	30.85
BT	0.423	-0.013	0.85	15.14
SVM	0.564	0.047	0.72	22.00
ANN	0.455	0.001	0.83	16.75
GloWH-Net	0.330	0.032	0.91	12.19

模型	RMSE/m	Bias/m	CC	MAPE/（%）
经验模型（DDMA方法）	0.757	0.050	0.48	32.02
经验模型（LEWS方法）	0.757	-0.031	0.48	33.44
经验模型（DDMA+LEWS方法）	0.757	0.046	0.48	32.09
BT	0.483	0.033	0.83	16.65
SVM	0.625	0.097	0.69	23.17
ANN	0.502	0.039	0.81	17.98
GloWH-Net	0.393	0.073	0.89	14.29

模型	RMSE/m	Bias/m	CC	MAPE/（%）
经验模型（DDMA方法）	0.707	-0.015	0.46	30.19
经验模型（LEWS方法）	0.801	-0.371	0.47	39.25
经验模型（DDMA+LEWS方法）	0.730	-0.193	0.47	33.93
BT	0.453	-0.021	0.82	16.51
SVM	0.551	0.042	0.72	21.75
ANN	0.469	-0.012	0.81	17.36
GloWH-Net	0.433	0.019	0.84	15.16