无线电工程2024，Vol.54Issue(4)：954-961,8.DOI:10.3969/j.issn.1003-3106.2024.04.019
基于小波变换和DNN算法的GNSS-IR 土壤湿度反演
GNSS-IR Soil Moisture Retrieval Based on Wavelet Transform and Deep Neural Network
摘要
Abstract
To effectively improve the accuracy of Global Navigation Satellite System Interferometric Reflectometry(GNSS-IR)soil moisture retrieval,a soil moisture retrieval method combining digital signal analysis and Deep Neural Network(DNN)is proposed.This method utilizes Wavelet Transform(WT)instead of the traditional polynomial fitting method to reduce noise,thereby effectively enhancing the extraction accuracy of the reflected signal.The Hilbert Transform(HT)is employed to obtain the average instantaneous properties of the observation signal,including the average instantaneous amplitude,average instantaneous frequency,and average instantaneous phase for each observation period.The Deep Neural Network(DNN)algorithm is used to establish a nonlinear mapping relationship between these three attributes and soil moisture,enabling the inversion of soil moisture.The model is established and evaluated using GNSS observation data collected in 2015 and 2016 at the PBO P037 station near Chattaffy County,Colorado,USA.The results demonstrate a Root Mean Square Error(RMSE)of 0.009 5 cm3/cm3 for this method,which represents a significant improvement compared to the traditional linear regression model.Consequently,the proposed approach effectively enhances the accuracy of GNSS-IR soil moisture retrieval.关键词
全球导航卫星系统干涉反射/土壤湿度反演/小波变换/深度神经网络Key words
GNSS-IR/soil moisture inversion/WT/DNN分类
天文与地球科学引用本文复制引用
张杰,刘小芳,姚蕊..基于小波变换和DNN算法的GNSS-IR 土壤湿度反演[J].无线电工程,2024,54(4):954-961,8.基金项目
高层次创新人才培养专项资助(B12402005) （B12402005）
四川轻化工大学人才引进项目(2021RC16) （2021RC16）
教育部高等教育司产学合作协同育人项目(202101038016)Funded by High Level Innovative Talents Training Special Project(B12402005) （202101038016）
Sichuan University of Science and Engi-neering for Talent Introduction Project(2021RC16) （2021RC16）
University-Industry Cooperation Collaborative Education Project of the Higher Education De-partment of the Ministry of Education(202101038016) （202101038016）
