Authors:Lili Jing Lei Yang Wentao Yang Tianhe Xu Fan Gao Yilin Lu Bo Sun Dongkai Yang Xuebao Hong Nazi Wang Hongliang Ruan and José Darrozes

来源出版物：Remote Sensing卷：13期：19 DOI https://doi.org/10.3390/ rs13194013出版年：October 2021

Abstract:This article aims to attempt to increase the number of satellites that can be used for monitoring soil moisture to obtain more precise results using GNSS-IR (Global Navigation Satellite System-Interferometric Reflectometry) technology to estimate soil moisture. We introduce a soil moisture inversion model by using GPS SNR (Signal-to-Noise Ratio) data and propose a novel Robust Kalman Filter soil moisture inversion model based on that. We validate our models on a data set collected at Lamasquère, France. This paper also compares the precision of the Robust Kalman Filter model with the conventional linear regression method and robust regression model in three different scenarios: (1) single-band univariate regression, by using only one observable feature such as frequency, amplitude, or phase; (2) dual-band data fusion univariate regression; and (3) dual-band data fusion multivariate regression. First, the proposed models achieve higher accuracy than the conventional method for single-band univariate regression, especially by using the phase as the input feature. Second, dual-band univariate data fusion achieves higher accuracy than single-band and the result of the Robust Kalman Filter model correlates better to the in situ measurement. Third, multivariate variable fusion improves the accuracy for both models, but the Robust Kalman Filter model achieves better improvement. Overall, the Robust Kalman Filter model shows better results in all the scenarios.

Keywords:GNSS; Signal-to-Noise Ratio; soil moisture; Robust Kalman Filter; data fusion

Citations:Jing, L.; Yang, W.; Yang, L.; Xu, T.; Gao, F.; Lu, Y.; Sun, B.; Yang, D.; Hong, X.; Wang, N.; et al. Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach. Remote Sens. 2021, 13, 4013.