Calibration of Sentinel-1 SAR retrieved wind speed based on BP neural network model

doi:10.3969/j.issn.1001-909X.2024.03.006

Abstract

Abstract:

An accuracy analysis of wind speed data retrieved from Sentinel-1 synthetic aperture radar (SAR) was conducted based on buoy observations from the National Data Buoy Center (NDBC). A back propagation (BP) neural network was utilized to correct the deviation in the SAR-derived wind speeds. Sensitivity experiments were designed for environmental factors, the number of training samples for BP neural network input, and neural network structure parameters. Finally, the SAR wind field data were converted into u and v vector wind data, and the accuracy analysis and correction were performed separately for u and v wind components. The experiment finds that the SAR-derived wind speed is underestimated compared to the buoy data. After calibration using BP neural network, the accuracy of SAR-derived wind speed data is improved, and the absolute value of bias of wind speed decreases from 0.78 m/s to 0.04 m/s, the RMSE of wind speed decreases from 1.98 m/s to 1.77 m/s. The sensitivity experiments suggest that low quality environmental factors input data will decrease the calibration effect of BP neural network, and increasing the sample size of the training set can improve that. The calibration results of converted u and v vector wind field data also show that the BP neural network has good correction effect.

Key words: Sentinel-1, SAR wind speed, NDBC, BP neural network, deviation calibration, sensitivity experiment, scalar wind field, vector wind field

CLC Number:

P732
TP183

NI Hanyue, DONG Changming, LIU Zhenbo, YANG Jingsong, LI Xiaohui, REN Lin. Calibration of Sentinel-1 SAR retrieved wind speed based on BP neural network model[J]. Journal of Marine Sciences, 2024, 42(3): 75-87.

Figures/Tables 12

Fig.1 Diagram of BP neural network structure

Fig.2 Accuracy analysis of SAR-derived wind speed (In fig.b, the black line is a 1∶1 diagonal line, and the red line is the fitted linear function, similarly hereinafter. )

Fig.3 Variation curve of wind speed error statistics with NDBC buoy observed wind speed

Fig.4 Polar coordinate representation of wind field data

Fig.5 Diagram of deviation calibration process

Fig.6 Scatter plot of density distribution of wind speed before and after calibration

Fig.7 Variation curve of wind speed error statistics with NDBC buoy observed wind speed before and after calibration

Fig.8 Polar coordinate representation of SAR wind field test set data before and after calibration

Tab.1 Sensitivity experiment results of environmental factors on wind speed calibration

统计量	同时输入降水数据和海气温差数据	只输入降水数据	只输入海气温差数据
bias/(m·s^-1)	-0.04	-0.05	-0.04
RMSE/(m·s^-1)	1.77	1.79	1.77
STD/(m·s^-1)	1.77	1.79	1.77

Tab.2 Sensitivity experiment results of BP neural network parameters on wind speed calibration

实验模型		bias/(m·s^-1)	RMSE/(m·s^-1)	STD/(m·s^-1)
原BP神经网络模型		-0.04	1.77	1.77
参数调整后的BP 神经网络模型	训练集数量为70%	0.00	1.80	1.80
	训练集数量为50%	0.12	1.79	1.78
	训练批次数量为2 000	-0.05	1.76	1.76
	训练批次数量为2 500	-0.05	1.76	1.75
	隐含层神经元数量为50	-0.04	1.77	1.77
	隐含层神经元数量为150	-0.05	1.77	1.77
	学习率为0.01	-0.05	1.77	1.76
	学习率为0.001	-0.04	1.77	1.77

Fig.9 Scatter plot of density distribution for u and v vector winds

Fig.10 Scatter plot of density distribution for u and v vector winds before and after calibration

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