Python software to transform GPS SNR wave phases to volumetric water content

Martín, Angel; Julián, Ana Belén Anquela; Ibáñez, Sara; Baixauli, Carlos; Clavero, Sara

doi:10.1007/s10291-021-01190-3

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The observation data of the PBO network's P043 station from 18 January 2016 to 30 September 2016 were selected for the experiments (The rising satellite tracks are used, since the interferogram pattern can differ for rising and setting tracks of the same satellite) [32]. We use the first 75% of data as the training set and the last 25% of data as the test set to model the GNSS-IR soil moisture inversion model.…”

Section: Study Area and Data Resourcementioning

confidence: 99%

A Study of GNSS-IR Soil Moisture Inversion Algorithms Integrating Robust Estimation with Machine Learning

et al. 2023

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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.

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Section: Study Area and Data Resourcementioning

confidence: 99%

A Study of GNSS-IR Soil Moisture Inversion Algorithms Integrating Robust Estimation with Machine Learning

et al. 2023

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Estimation of relative permittivity for measuring soil texture-dependent water content by GNSS-IR

Kobayashi,

Aoki,

Sato

et al. 2024

GPS Solut

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Techniques for measuring the soil moisture content (SMC) using global navigation satellite system-interferometric reflectometry (GNSS-IR) with a positioning antenna have been reported. However, conventional methods are limited to evaluating the relative change in volumetric water content in a dry range for certain soil textures. In this study, we proposed a method to measure relative permittivity using GNSS-IR and evaluated its applicability at two sites with different soil textures and moisture content. The true multipath penetration depth was obtained from the tangent dielectric suitable for the soil textures, and the apparent penetration depth affected by the relative permittivity of the soil was calculated from the signal-to-noise ratio measured by GNSS. The relative permittivity of the soil was obtained from the ratio of these values and compared with the relative permittivity of the SMC sensor. As a result, we could measure soil permittivity according to soil textures from dry to wet conditions from GNSS-IR, except when the true multipath wave penetration depth was less than 1.5 cm, at which only surface reflection occurred. Sandy soils with a low dielectric loss tangent and wet areas with small changes in the depth of penetration of electromagnetic waves are particularly suitable environments for this method.

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Python software to transform GPS SNR wave phases to volumetric water content

Cited by 2 publications

References 17 publications

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

Estimation of relative permittivity for measuring soil texture-dependent water content by GNSS-IR

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