Robust regression soil moisture retrieval method for GNSS-IR multi-star fusion

doi:10.13474/j.cnki.11-2246.2024.0312

Abstract

Abstract: Global navigation satellite system interferometry (GNSS-IR) can extract effective information such as soil moisture and sea surface height by analyzing interference information between direct and reflected satellite signals. In order to reduce the weight of outliers by using robust regression method, this paper proposes to reduce or offset the influence of anomalous observation data on soil moisture inversion. In order to verify the application range of the model, multi-star fusion experiments are carried out on the basis of robust regression, which effectively improve the accuracy of soil moisture inversion. The results show that compare with the traditional linear regression method, the RMSE and MAE of the proposed method are reduced by 8.38% and 8.91% on average for a single satellite, and by 15.18% and 16.42% on average for two satellites,by 21.00% and 22.97% on average for three satellites, by 26.25% and 28.71% on average for four satellites.

Key words: GNSS-IR, soil moisture, linear regression, robust regression, multi-satellites fusion

CLC Number:

P228

WANG Shitai, YANG Kexin, YIN Min, MA Yue, JIANG Wei, LIU Xu, WEI Jialin. Robust regression soil moisture retrieval method for GNSS-IR multi-star fusion[J]. Bulletin of Surveying and Mapping, 2024, 0(3): 69-74.

References

[1] 毕京锐.基于GPS与BDS反射信号的土壤湿度反演研究[D].淮南:安徽理工大学,2022.
[2] 丰秋林,郑南山,刘晨,等.BP神经网络辅助的GNSS反射信号土壤湿度反演[J].测绘科学,2018,43(8):157-162.
[3] 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.
[4] 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):L24405.
[5] 严颂华,张训械. 基于GNSS-R信号的土壤湿度反演研究[J]. 电波科学学报,2010,25(1):8-13.
[6] 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.
[7] 汉牟田,张波,杨东凯,等. 利用GNSS干涉信号振荡幅度反演土壤湿度[J]. 测绘学报,2016,45(11):1293-1300.
[8] 梁月吉,任超,黄仪邦,等. 多星融合的土壤湿度滚动式估算模型[J]. 遥感学报,2019,23(4):648-660.
[9] 陈堃,沈飞,曹新运,等. 基于深度置信网络的GNSS-IR土壤湿度反演[J]. 测绘通报,2020(9):100-105.
[10] 孙波,张弛,尹世超,等. 基于PSO-RF的GNSS-IR土壤湿度反演方法研究[J]. 无线电工程,2021,51(10):1080-1085.
[11] 王式太,姜新伟,殷敏,等. GA辅助NLS的GNSS-IR土壤湿度反演方法[J]. 大地测量与地球动力学,2023,43(2):180-185.
[12] 段睿,张波,汉牟田,等.SVRM方法的单天线GNSS-R土壤湿度反演[J].导航定位学报,2018,6(1):34-39.
[13] 张皓. 机器学习辅助的GNSS-R土壤湿度反演精细化研究[D]. 徐州:中国矿业大学,2021.
[14] 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(7):106-111.
[15] BILICH A,LARSON K M. Mapping the GPS multipath environment using the signal-to-noise ratio (SNR)[J]. Radio Science,2007,42(6):RS6003.
[16] 程义军,孙海燕,程海斌.抗差卡尔曼滤波及其在动态水准网平差中的应用[J].测绘工程,2004,13(4):55-57.