基于树模型机器学习方法的GNSS-R海面风速反演

骆黎明; 白伟华; 孙越强; 夏俊明

doi:10.11728/cjss2020.04.595

基于树模型机器学习方法的GNSS-R海面风速反演

doi: 10.11728/cjss2020.04.595

骆黎明^1,2,3,
白伟华^1,2,3,
孙越强^1,2,3,
夏俊明^1,3

1. 中国科学院国家空间科学中心北京 100190;
2. 中国科学院大学北京 100049;
3. 北京市天基空间环境探测重点实验室北京 100190

基金项目:

国家重点研发计划重点专项项目（2017YB0502800，2017YFB0502802），国家自然科学基金项目（41405039，41775034，41405040，41505030，41606206），中国科学院空间先导专项项目（XDA15007501）和中国科学院科研装备研制项目（YZ201129）共同资助

详细信息

作者简介:
骆黎明,E-mail:limingluo6@163.com

中图分类号: P753;TN967
计量
- 文章访问数: 813
- HTML全文浏览量: 136
- PDF下载量: 99
- 被引次数: 0
出版历程
- 收稿日期: 2018-12-05
- 修回日期: 2020-05-25
- 刊出日期: 2020-07-15

GNSS-R Sea Surface Wind Speed Inversion Based on Tree Model Machine Learning Method

LUO Liming^1,2,3,
BAI Weihua^1,2,3,
SUN Yueqiang^1,2,3,
XIA Junming^1,3

1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190;
2 University of Chinese Academy of Sciences, Beijing 100049;
3 Beijing Key Laboratory of Space Environment Explorations, Beijing 100190

摘要

摘要: GNSS-R是基于GNSS卫星反射信号的一种新技术.GNSS-R技术可以运用到海面风场反演中，传统的GNSS-R技术反演海面风场主要有波形匹配和经验函数两种方法，风速反演精度约为2m·s^-1.波形匹配方法耗时多，计算量大；经验函数方法通常只使用少量物理观测量，会造成信息浪费，损失一定的反演精度.为了提高海面风速的反演精度，引入机器学习领域常用的树模型算法决策树、随机森林、GBDT等对海面风速进行预测.利用GNSS-R与ECMWF数据构成训练集和验证集，训练集用于模型学习，验证集用于检验模型的反演效果.实验结果显示，决策树和随机森林预测误差约为0.6m·s^-1，GBDT等算法的预测误差约为2m·s^-1，满足风速反演要求.与GNSS-R传统反演方法相比，机器学习树模型算法效果更好，在验证集上表现稳定且误差较小.因此，可以将机器学习树模型算法运用到海面风速反演中.
- GNSS-R /
- 海面风速 /
- 反演 /
- 机器学习
Abstract: GNSS-R is a new technique based on GNSS satellite reflection signals, and it can be applied to the inversion of sea surface wind field. The traditional GNSS-R technology inversion of sea surface wind field mainly has waveform matching and experience function. The waveform matching method is time-consuming and computationally intensive; the empirical function method often uses only a small amount of physical observations, which causes waste of additional information and loss of certain inversion precision. The accuracy of the traditional method of wind speed inversion is about 2m·s^-1. In order to improve the inversion accuracy of sea surface wind speed, the tree model algorithm decision tree, random forest and GBDT commonly used in the field of machine learning are introduced to predict the sea surface wind speed. The training set and the verification set are constructed by using GNSS-R and ECMWF data. The training set is used for model learning, and the verification set is used to test the inversion effect of the model. The prediction error of decision tree and random forest is about 0.6m·s^-1, and the prediction error of GBDT and other algorithms is about 2m·s^-1, which meets the requirements of wind speed inversion. Compared with the traditional GNSS-R inversion method, the machine learning tree model algorithm performs better and has stable performance and less error on the verification set. Therefore, the machine learning tree model algorithm can be applied to the sea surface wind speed inversion.
- GNSS-R /
- Sea surface wind speed /
- Inversion /
- Machine learning

HTML全文

参考文献(16)

[1]	ZHANG Lei, SHI Hanqing, LONG Zhiyong, et al. A review of methods for retrieving sea surface wind field from spaceborne synthetic aperture radar images[J]. Marine Sci. Bull., 2012, 31(6):713-720(张雷, 石汉青, 龙智勇, 等. 星载合成孔径雷达图像反演海面风场方法综述[J]. 海洋通报, 2012, 31(6):713-720)
[2]	LÜ Fan, XIU Chundi, WANG Feng, et al. Simulation analysis of GNSS-R sea surface wind field inversion model[J]. J. Naut. Navig., 2018, 6(3):87-91, 97(吕帆, 修春娣, 王峰, 等. GNSS-R海面风场反演模型仿真分析[J]. 导航定位学报, 2018, 6(3):87-91, 97)
[3]	MARTIN N M. A Passive Reflectometry and Interferometry System (PARIS) application to ocean altimetry[J]. Esa J., 1993, 17(4):331-355
[4]	YANG D K, ZHANG Y Q, LU Y, et al. GPS reflections for sea surface wind speed measurement[J]. IEEE Geosci. Remote Sens. Lett., 2008, 5(4):569-572
[5]	SHEN L C, JUANG J C, TSAI C L, et al. Stream soil moisture estimation by reflected GPS signals with ground truth measurements[J]. IEEE Trans. Instr. Meas., 2009, 58(3):730-737
[6]	WAN Wei, CHEN Xiuwan, PENG Xuefeng, et al. Progress and prospect of GNSS remote sensing research and application[J]. J. Remote Sens., 2016, 20(5):858-874(万玮, 陈秀万, 彭学峰, 等. GNSS遥感研究与应用进展和展望[J]. 遥感学报, 2016, 20(5):858-874)
[7]	YU K, RIZOS C, BURRAGE D, et al. An overview of GNSS remote sensing[J]. Eurasip J. Adv. Signal Proc., 2014, 2014(1):134
[8]	BAI Weihua. GNSS-R Marine Remote Sensing Technology Research[D]. Beijing:Graduate School of Chinese Academy of Sciences (白伟华. GNSS-R海洋遥感技术研究[D]. 北京:中国科学院研究生院, 2008)
[9]	YANG Dongkai, ZHANG Qishan. Foundation and Practice of GNSS Reflected Signal Processing[M]. Beijing:Publishing House of Electronics Industry, 2012(杨东凯, 张其善. GNSS反射信号处理基础与实践[M]. 北京:电子工业出版社, 2012)
[10]	BREIMAN L. Bagging predictors[J]. Mach. Learning, 1996, 24:123-140
[11]	FREUND Y. Boosting a weak learning algorithm by majority[J]. Inf. Comp., 1995, 121(2):256-285
[12]	FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. J. Comp. Sys. Sci., 1997, 55(1):119-139
[13]	FRIEDMAN J H. Greedy function approximation:a gradient boosting machine[J]. Ann. Stat., 2001, 29(5):1189-1232
[14]	CHEN Tianqi, CARLOs Guestrin. XGBoost:a scalable tree boosting system[C]//Proceeding KDD'16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2016:785-794
[15]	KE G, MENG Q, FINELY T, et al. LightGBM:A Highly Efficient Gradient Boosting Decision Tree[R]. 31st Conference on Neural Information Processing Systems. California:Neural Information Processing Systems, 2017
[16]	SENI G, ELDER J F. Ensemble methods in data mining:improving accuracy through combining predictors[J]. Synt. Lect. Data Mining Know. Disc., 2010, 2(1):1-126