Soil moisture estimation based on triple-frequency multipath error

Zhang, Xianyun; Nie, Shihai; Zhang, Chunkang; Zhang, Jun; Cai, Hong

doi:10.1080/01431161.2021.1933246

Cited by 2 publications

(9 citation statements)

References 30 publications

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“…The direct and the reflected signals entering the receiver antenna interfere with each other, which causes the observation value to deviate from the actual value and produce the multipath error. According to the roughness of the reflective surface of the ground, the two main types of reflection are diffuse reflection and specular reflection [22,24]. For convenience, we assume that multipath error is only caused by specular reflections.…”

Section: Gnss Multipath Error Principlementioning

confidence: 99%

“…The PBO SMC consisted of the site averaged SMC on a daily timescale, and the median SMC value of all satellite tracks for each day was used [22]. (https://data.unavco.org/ archive/gnss/products/) (accessed on 15 September 2021).…”

Section: Data Sourcesmentioning

confidence: 99%

Section: Choice Of Elevation Anglementioning

confidence: 99%

“…Given the lack of measured soil moisture data, we selected the soil moisture value from the P041 station estimated with the new GPS L2C carrier SNR as the reference value. The soil moisture value of the PBO site is based on a value per day and is the median value estimated by all satellites [22]. As soil moisture changes more during the day than that at night and more GPS satellites are visible during the day than at night, to simplify the calculation, generally, only four and eight observation periods are selected at night and day, respectively.…”

Section: Choice Of Elevation Anglementioning

confidence: 99%

“…Zhang et al proposed two new SMC estimation methods: triple-frequency carrier phase (TRFCP) and triple-frequency pseudorange (TRFP). The experimental results showed that the phase delay estimated by the two methods strongly correlates with Plate boundary observation (PBO) SMC [22]. Shen et al proposed a BDS Medium Earth Orbit (MEO) and Inclined Geosynchronous Satellite Orbit (IGSO) satellite multisatellite soil moisture retrieval method based on SNR observations.…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of Soil Moisture Content Based on Multisatellite Dual-Frequency Combination Multipath Errors

Nie

Wang

et al. 2022

Remote Sensing

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Global navigation satellite system interferometric reflectometry (GNSS-IR) is a new type of microwave remote sensing technology that can measure soil moisture content (SMC). GNSS-IR soil moisture retrieval methods based on the satellite signal-to-noise ratio (SNR) and triple-frequency signal combination have the following shortcomings: SNR does not always exist in the original GNSS file, and the number of triple-frequency signal observation satellites is small, resulting in GNSS-IR soil moisture observation time resolution being low. Based on the above problems, in this study, we constructed a soil moisture inversion method based on multisatellite dual-frequency combined multipath error is proposed: the multipath error calculation model of dual-frequency carrier phase (L4 Ionosphere Free, L4_IF) and dual-frequency pseudorange (DFP) without ionospheric effect is constructed. We selected the data of the five epochs before and after the time point of the effective satellite period to construct the multipath error model and error equation, and we solved the delay phase for soil moisture retrieval. We verified the method using Plate Boundary Observatory (PBO) P041 site data. The results showed that the Pearson correlation coefficients (R) of L4_IF and DFP methods at P041 station are 0.97 and 0.91, respectively. To better verify the results' reliability and the proposed method's effectiveness, the soil moisture data of the MFLE station about 210 m away from P041 station are used as the verification data in this paper. The results showed that the delay phase solved by multipath error and soil moisture strongly correlate. Pearson correlation coefficients (R) of L4_IF and DFP methods at MFLE station are 0.93 and 0.86, respectively. In order to improve the inversion accuracy of GNSS-IR soil moisture, this paper constructs the prediction model of soil moisture by using the linear regression (ULR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), and evaluates the accuracy of each model. The results showed that the soil moisture retrieval method based on multisatellite dual-frequency combined multipath error can replace the traditional retrieval method and effectively improve the time resolution of GNSS-IR soil moisture estimation. To perform highly dynamic monitoring of soil moisture, higher retrieval accuracy can only be obtained with a small epoch multipath error.

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Section: Gnss Multipath Error Principlementioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

Section: Choice Of Elevation Anglementioning

confidence: 99%

Section: Choice Of Elevation Anglementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retrieval of Soil Moisture Content Based on Multisatellite Dual-Frequency Combination Multipath Errors

Nie

Wang

et al. 2022

Remote Sensing

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Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers

Zhang,

Ren,

Liang

et al. 2023

Sensors

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Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range.

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Soil moisture estimation based on triple-frequency multipath error

Cited by 2 publications

References 30 publications

Retrieval of Soil Moisture Content Based on Multisatellite Dual-Frequency Combination Multipath Errors

Retrieval of Soil Moisture Content Based on Multisatellite Dual-Frequency Combination Multipath Errors

Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers

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