基于BP神经网络模型的哨兵SAR反演风速偏差校正

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

海洋学研究 ›› 2024, Vol. 42 ›› Issue (3): 75-87.DOI: 10.3969/j.issn.1001-909X.2024.03.006

基于BP神经网络模型的哨兵SAR反演风速偏差校正

倪晗玥¹^,²(), 董昌明³, 刘振波³, 杨劲松¹^,²^,^*(), 李晓辉², 任林²

1.上海交通大学海洋学院,上海 200240
2.自然资源部第二海洋研究所卫星海洋环境动力学国家重点实验室,浙江杭州 310012
3.南京信息工程大学海洋科学学院,江苏南京 210044

收稿日期:2023-12-26 修回日期:2024-03-26 出版日期:2024-09-15 发布日期:2024-11-25
通讯作者: ^*杨劲松(1969—),男,研究员,主要从事海洋微波遥感与卫星海洋学研究,E-mail:jsyang@sio.org.cn。
作者简介:倪晗玥(2000—),女,江苏省苏州市人,主要从事海洋微波遥感研究,E-mail:nihanyue001126@163.com。
基金资助:
国家自然科学基金项目(42306200);国家自然科学基金项目(42306216);国家重点研发计划项目(2022YFC3103101)

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

NI Hanyue¹^,²(), DONG Changming³, LIU Zhenbo³, YANG Jingsong¹^,²^,^*(), LI Xiaohui², REN Lin²

1. School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, China
2. Second Institute of Oceanography, MNR, State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou 310012, China
3. School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

Received:2023-12-26 Revised:2024-03-26 Online:2024-09-15 Published:2024-11-25

摘要/Abstract

摘要：

该文基于美国国家浮标资料中心(National Data Buoy Center,NDBC) 浮标观测数据对哨兵一号搭载的合成孔径雷达(synthetic aperture radar,SAR) 反演风速数据进行精度分析,并利用BP神经网络 (back propagation neural network) 对SAR反演风速的偏差进行校正;同时针对环境要素、BP神经网络训练输入的样本量以及神经网络结构参数设计了敏感性试验;最后将SAR标量风场数据转换为用u、v矢量表示的风场数据,并对u向风和v向风分别进行了精度分析和校正。实验结果表明:SAR反演风速相较于浮标观测数据出现了低估现象;经过BP神经网络校正后,SAR反演风速数据的精度得到了改善,风速的平均偏差绝对值从0.78 m/s下降到0.04 m/s,均方根误差从1.98 m/s下降到了1.77 m/s;敏感性试验表明输入质量较差的环境要素数据时BP神经网络的校正效果有所下降,而增加训练集样本量能改善校正效果;将标量风场数据转换为u、v矢量风场数据后的校正结果也显示BP神经网络具有较好的校正效果。

关键词: 哨兵一号卫星, SAR风速资料, NDBC浮标, BP神经网络, 偏差校正, 敏感性试验, 标量风场, 矢量风场

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

中图分类号:

P732
TP183

倪晗玥, 董昌明, 刘振波, 杨劲松, 李晓辉, 任林. 基于BP神经网络模型的哨兵SAR反演风速偏差校正[J]. 海洋学研究, 2024, 42(3): 75-87.

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.

图/表 12

图1 BP神经网络结构示意图

Fig.1 Diagram of BP neural network structure

图2 SAR反演风速精度分析 (图b中的黑线为1∶1对角线,红线表示拟合后的一次函数。后文同此。)

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. )

图3 风速误差统计量随NDBC浮标观测风速变化曲线

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

图4 风场数据极坐标表示

Fig.4 Polar coordinate representation of wind field data

图5 偏差校正流程图

Fig.5 Diagram of deviation calibration process

图6 校正前、后风速的密度分布散点图

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

图7 校正前、后风速误差统计量随NDBC浮标观测风速变化曲线

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

图8 校正前、后SAR反演风场测试集数据极坐标图

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

表1 环境要素对风速校正的敏感性试验结果

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

表2 BP神经网络参数对风速校正的敏感性试验结果

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

图9 u、v向风密度分布散点图

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

图10 u、v向风校正前、后密度分布散点图

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

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基于BP神经网络模型的哨兵SAR反演风速偏差校正

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

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