A Study of GNSS-IR Soil Moisture Inversion Algorithms Integrating Robust Estimation with Machine Learning
A Study of GNSS-IR Soil Moisture Inversion Algorithms Integrating Robust Estimation with Machine Learning
复制标题
稳健估计与机器学习相结合的GNSS-IR土壤水分反演算法研究
DOI：
10.3390/su15086919
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发表时间：
2023-04
期刊：
影响因子：
3.9
通讯作者：
Rui-Qiang Ding;Nanshan Zheng;Hao Zhang;Hua Zhang;F. Lang;Wei Ban
中科院分区：
文献类型：
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作者：
Rui-Qiang Ding;Nanshan Zheng;Hao Zhang;Hua Zhang;F. Lang;Wei Ban
土壤水分监测在农业、水资源管理、灾害预防等方面有着广泛的应用，对可持续发展具有重要意义。全球导航卫星系统（GNSS）干涉反射测量技术（GNSS-IR）为土壤水分监测提供了一种补充方法。然而，由于信噪比（SNR）测量的质量和复杂的表面环境，多径干扰信号度量（幅度，频率和相位）中不可避免的离群值被用作建模变量来反演GNSS-IR土壤湿度。此外，单变量模型拟合效果差，泛化能力弱，难以全面分析各因素之间的关系。本文采用最小协方差行列式（MCD）抗差估计和机器学习算法。MCD鲁棒估计可以消除多径信号度量和机器学习算法（包括反向传播神经网络（BPNN）、高斯过程回归（GPR）和随机森林（RF））的异常值，并且可以综合建立使用多径干扰信号度量的非线性GNSS-IR土壤水分反演模型。此外，还对三种机器学习算法的建模参数选择以及单星和全星反演结果进行了研究，使算法具有更强的通用性。结果表明，与MCD多元回归模型相比，机器学习模型的相关系数（R）提高了4.3~86.6%，均方根误差（RMSE）降低了2.8~ 30%。具有80个决策树和1个节点的RF模型显示出最明显的改进。利用所有卫星数据的整体模型比单卫星模型具有更好的泛化能力，但会导致精度损失。
Soil moisture monitoring is widely used in agriculture, water resource management, and disaster prevention, which is of great significance for sustainability. The global navigation satellite system interferometric reflectometry (GNSS-IR) technology provides a supplementary method for soil moisture monitoring. However, due to the quality of the signal-to-noise ratio (SNR) measurements and the complex surface environment, inevitable outliers in multipath interference signal metrics (amplitude, frequency, and phase) were used as modeling variables to inverse GNSS-IR soil moisture. Besides, it is hard to use the univariate model to comprehensively analyze the relationship between the various factors, due to the poor fitting effect and weak generalization ability of the model. In this paper, the minimum covariance determinant (MCD) robust estimation and machine learning algorithms are adopted. The MCD robust estimation can eliminate outliers of the multipath signal metrics and machine learning algorithms, including the back propagation neural network (BPNN), Gaussian process regression (GPR), and random forest (RF), and can comprehensively establish nonlinear GNSS-IR soil moisture inversion models using multipath interference signal metrics. Moreover, the study of the modeling parameter selection for the three machine learning algorithms and the inversion results for single satellite and all satellites are also carried out to make the algorithms more generalizable. The results show that the correlation coefficients (R) and the root mean square error (RMSE) of the machine learning models for all satellite tracks are increased by 4.3~86.6% and reduced by 2.8~30%, respectively, compared with the MCD multiple regression model. The RF model with 80 decision trees and 1 node shows the clearest improvement. The total model using all satellite data has more generalization ability than the single satellite model but causes some loss of accuracy.