Quality control and improvement of GNSS-IR soil moisture robust inversion model

Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) technology can realize continuous and dynamic monitoring of soil moisture at areas. The GNSS signal is highly susceptible to the influence of external factors, causing anomalous terms in the process of characteristic parameters extraction. In this study, an integrated outlier detection method is proposed. This method first detects outlier based on Inter-Quartile Range, Grubbs test and Hampel filter outlier detection methods for the characteristic parameters data, and derives the outlier location by complementing the detection results with each other and corrected by rainfall data. Then, four kinds of outlier repair methods, namely, moving average, locally weighted primary linear regression, locally weighted quadratic linear regression, and robust locally weighted quadratic linear regression, were used to repair and filter out the optimal data according to the location of the outlier. Finally, three machine learning methods, namely, Bagging Tree, Support Vector Machine (SVM), and Gaussian Process Regression (GPR), were combined to build a soil water content inversion model and evaluate the accuracy. The results show that the proposed comprehensive outlier detection method in this study can accurately detect the location of outlier. The correlation between the characteristic parameters and in-situ soil moisture can be effectively improved after remediation treatment. The outlier repair helped to improve the inversion accuracy of the soil moisture inversion model, and the R2 of the soil moisture inversion model increased by 13.21 % to 27.08 % (mean 18.25 %), the RMSE decreased by 12.97 % to 23.61 % (mean 18.16 %). The comprehensive outlier detection and repair method proposed in this study can provide reference for the quality control of input data before the establishment of GNSSIR model, and effectively improve the inversion accuracy of GNSS-IR soil moisture inversion model. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.