机器学习算法辅助的GPS信噪比观测值土壤湿度反演

doi:10.13474/j.cnki.11-2246.2018.0221

测绘通报 ›› 2018, Vol. 0 ›› Issue (7): 106-111.doi: 10.13474/j.cnki.11-2246.2018.0221

机器学习算法辅助的GPS信噪比观测值土壤湿度反演

丰秋林¹, 郑南山^1,2

1. 中国矿业大学环境与测绘学院, 江苏徐州 221116;
2. 江苏省资源环境信息工程重点实验室, 江苏徐州 221116

收稿日期:2017-09-19 出版日期:2018-07-25 发布日期:2018-08-02
作者简介:丰秋林(1993-),男,硕士生,主要从事GNSS应用研究。E-mail:1169459961@qq.com
基金资助:
国家自然科学基金（51174206）

Retrieving Soil Moisture Using Signal-to-noise Ratio of GPS Signal by Assisted Machine Learning Algorithm

FENG Qiulin¹, ZHENG Nanshan^1,2

1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
2. Jiangsu Key Laboratory of Resources and Environmental Information Engineering, Xuzhou 221116, China

Received:2017-09-19 Online:2018-07-25 Published:2018-08-02

摘要/Abstract

摘要： 利用BP神经网络和支持向量回归机两种机器学习算法，构建基于机器学习算法的GNSS卫星反射信号土壤湿度反演模型，并与线性回归统计模型和实测数据进行对比分析。结果表明：基于机器学习算法的GNSS卫星反射信号土壤湿度反演方法获取的土壤湿度结果与土壤湿度参考值误差较小，反演模型的决定系数分别为0.928 3和0.913 1，均方根误差为0.026 6和0.032 6，线性回归统计模型的决定系数分别为0.553 2和0.859 8，均方差根误差分别为0.093 9和0.041 6。说明利用回归算法定量估测土壤湿度明显优于线性回归统计模型，且基于支持向量回归机的土壤湿度反演模型定量估测土壤湿度优于基于BP神经网络算法的土壤湿度反演模型，证明了该方法的可靠性，为土壤湿度的实时反演研究提供了一种新方法。

关键词: BP神经网络算法, 支持向量回归机, 信噪比, 土壤湿度

Abstract: We construct the soil moisture retrieval model of GNSS satellite reflection signal based on machine learning algorithm with the use of BP neural network and support vector machine,and compared with the linear regression statistical model and the measured data.The results show that the error between soil moisture prediction of retrieval model based on the regression algorithm and reference value of soil moisture is small,the determination coefficient are 0.928 3 and 0.913 1,respectively,the root mean square error are 0.026 6 and 0.032 0,and the determination coefficient of the linear regression statistic model are 0.553 8 and 0.859 2,respectively,the root mean square error are 0.093 9 and 0.041 6.Those demonstrates that the quantitative estimates of soil moisture by using regression algorithm is better than the linear regression model,and the soil moisture retrieval model based on support vector regression is superior to the soil moisture retrieval model based on BP neural network algorithm,which proves the reliability of the method and provides a new method for real-time retrieval of soil moisture.

Key words: BP neural network algorithm, support vector regression machine, signal to noise ratio, soil moisture

中图分类号:

P237

丰秋林, 郑南山. 机器学习算法辅助的GPS信噪比观测值土壤湿度反演[J]. 测绘通报, 2018, 0(7): 106-111.

FENG Qiulin, ZHENG Nanshan. Retrieving Soil Moisture Using Signal-to-noise Ratio of GPS Signal by Assisted Machine Learning Algorithm[J]. 测绘通报, 2018, 0(7): 106-111.

参考文献

[1] ROBOCK A,VINNIKOV K Y,SRINIVASAN G,et al.The Global Soil Moisture Data Bank[J].Bulletin of the American Meteorological Society,2000,81(6):1281-1300.
[2] 敖敏思,胡友健,刘亚东,等.GPS信噪比观测值的土壤湿度变化趋势反演[J].测绘科学技术学报,2012,29(2):66-96.
[3] SCHMUGGE T,JACKSON T J.Mapping Surface Soil Moisture with Microwave Radiometers[J].Meteorology and Atmospheric Physics,1994,54(1-4):213-223.
[4] HEGARAT-MASCLE S L,ZRIBI M,ALEM F,et al.Soil Moisture Estimation from ERS/SAR Data:Toward an Operational Methodology[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(12):2647-2658.
[5] 刘经南,邵连军,张训械.GNSS-R研究进展及其关键技术[J].武汉大学学报(信息科学版),2007,32(11):955-960.
[6] 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] BILICH A,LARSON K M,AXELRAD P.Modeling GPS Phase Multipath with SNR:Case Study from the Salar de Uyuni,Boliva[J].Journal of Geophysical Research Solid Earth,2008,113(B4):317-323.
[8] BILICH A,LARSON K M.Mapping the GPS Multipath Environment Using the Signal-to-noise Ratio (SNR)[J].Radio Science,2007,42(2):3442-3446.
[9] RODRIGUEZ-ALVAREZ N,CAMPS A,VALL-LLOSSERA M,et al.Land Geophysical Parameters Retrieval Using the Interference Pattern GNSS-R Technique[J].IEEE Transactions on Geoscience and Remote Sensing,2010,49(1):71-84.
[10] LARSON K M,BRAUN J J,SMALL E E,et al.GPS Multipath and Its Relation to Near-surface Soil Moisture Content[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2010,3(1):91-99.
[11] LARSON K M,SMALL E E,GUTMANN E D,et al.Use of GPS Receivers as a Soil Moisture Network for Water Cycle Studies[J].Geophysical Research Letters,2008,35(24):851-854.
[12] LARSON K M,SMALL E E,GUTMANN E,et al.Using GPS Multipath to Measure Soil Moisture Fluctuations:Initial Results[J].GPS Solutions,2008,12(3):173-177.
[13] 敖敏思,朱建军,胡友健,等.利用SNR观测值进行GPS土壤湿度监测[J].武汉大学学报(信息科学版),2015,40(1):117-120.
[14] 马成前,王庆喜.基于局部及全局误差的BP神经网络研究[J].武汉理工大学学报,2009(20):99-101.
[15] 谭立萍,马颖异,陈永生,等.三层模型的BP网络在GNSS高程拟合中的应用研究[J].测绘通报,2015(8):56-59.
[16] 黄杰,王光辉,杨化超,等.结合偏最小二乘法和支持向量机的遥感影像变化检测[J].测绘通报,2016(7):35-38.
[17] 杨磊,吴秋兰,张波,等.SVRM辅助的北斗GEO卫星反射信号土壤湿度反演方法[J/OL].北京航空航天大学学报,2016,42(6):1134-1141.

机器学习算法辅助的GPS信噪比观测值土壤湿度反演

Retrieving Soil Moisture Using Signal-to-noise Ratio of GPS Signal by Assisted Machine Learning Algorithm

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 12

编辑推荐

Metrics

本文评价

[1]	贾秀丽. 高斯过程回归辅助下的GPS干涉反射积雪深度估测[J]. 测绘通报, 2022, 0(7): 78-82.
[2]	张玉, 贾遂民. 噪声调节主成分变换下的遥感图像薄云去除[J]. 测绘通报, 2021, 0(3): 33-37.
[3]	迪里胡玛尔·阿汗木江, 玉素甫江·如素力. 新疆焉耆盆地土壤湿度时空分布特征[J]. 测绘通报, 2021, 0(11): 36-41.
[4]	陈堃, 沈飞, 曹新运, 朱逸凡. 基于深度置信网络的GNSS-IR土壤湿度反演[J]. 测绘通报, 2020, 0(9): 100-105.
[5]	王庆余, 杜宁, 王莉, 张小东, 吴磊, 熬逍. 变分模态分解及能量熵在地心运动降噪中的应用[J]. 测绘通报, 2020, 0(8): 59-64.
[6]	朱小灵, 王剑辉, 谢荣安. 基于GPS-IR的地表土壤湿度估算方法[J]. 测绘通报, 2020, 0(6): 128-133.
[7]	王瑞芳. 利用EMD进行GNSS-MR潮位监测研究[J]. 测绘通报, 2020, 0(10): 114-117.
[8]	王晓, 郭军, 朱邦彦, 王爱学, 秦天天. 一种侧扫声呐图像去噪及均衡化综合处理方法[J]. 测绘通报, 2018, 0(8): 51-55.
[9]	李晓光, 程鹏飞, 成英燕, 万军. GNSS数据质量分析[J]. 测绘通报, 2017, 0(3): 1-4,8.
[10]	王小洁, 闵锦忠, 薛丰昌, 王伟. MODIS数据支持下的土壤湿度模型构建——以河南商丘地区为例[J]. 测绘通报, 2016, 0(12): 48-50,73.
[11]	许良备应忍冬陈新刘佩林赵毅. 北斗非GEO卫星信号码并行捕获[J]. 测绘通报, 2014, 0(9): 12-15.
[12]	宋学忠徐爱功杨东凯马小东. GNSS反射信号在土壤湿度测量中的应用[J]. 测绘通报, 2013, 0(11): 61-64.