Validation of the soil dielectric spectroscopic models with input parameters based on soil composition

In this paper, a comparative analysis of dielectric spectrum predictions for moist soils in the microwave band was carried out, regarding a well known and prevalent semiempirical dielectric model proposed in [1]-[3], on the one hand, and recently developed generalized refractive mixing dielectric mo...

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description In this paper, a comparative analysis of dielectric spectrum predictions for moist soils in the microwave band was carried out, regarding a well known and prevalent semiempirical dielectric model proposed in [1]-[3], on the one hand, and recently developed generalized refractive mixing dielectric model [5], on the other hand. The analysis is based on the measured dielectric data borrowed from [4], in which a set of soils measured includes all of grain-size distributions that are observed in nature, with measurements being performed over the range from 40 MHz to 17 GHz. In the case of the soil measured in [4] that has an intermediate position in terms of its texture parameters, input data for the semiempirical model were attained, using the texture and soil mineralogy data available in [4], and dielectric spectra predictions both for the dielectric constant and loss factor were calculated. Simultaneously, with the use of the dielectric data of [4], input parameters for the generalized refractive mixing dielectric model were derived, and dielectric predictions for the same moistures were calculated. Comparative analysis based on the measured data together with both predictions showed the semiempirical dielectric mixing model to generate dielectric constants and loss factors that have a noticeable bias relative to the measured ones, correlation coefficients being on the order of 0.93. At the same time, the GRMDM predictions appeared to correlate with the measured values with noticeably better accuracy both in the frequency and moisture domains. As a result, the ability to make accurate predictions for dielectric spectra with the use of the SDMM was shown to be doubted, regarding the moist soils, which falls out of the scope of soils measured and fitted to develop the SDMM in [1]-[3]. On the contrary, the GRMDM [5] has proved to be able to make predictions for the dielectric spectra of moist soils with the same error as that of initial dielectric measurements.
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The analysis is based on the measured dielectric data borrowed from [4], in which a set of soils measured includes all of grain-size distributions that are observed in nature, with measurements being performed over the range from 40 MHz to 17 GHz. In the case of the soil measured in [4] that has an intermediate position in terms of its texture parameters, input data for the semiempirical model were attained, using the texture and soil mineralogy data available in [4], and dielectric spectra predictions both for the dielectric constant and loss factor were calculated. Simultaneously, with the use of the dielectric data of [4], input parameters for the generalized refractive mixing dielectric model were derived, and dielectric predictions for the same moistures were calculated. Comparative analysis based on the measured data together with both predictions showed the semiempirical dielectric mixing model to generate dielectric constants and loss factors that have a noticeable bias relative to the measured ones, correlation coefficients being on the order of 0.93. At the same time, the GRMDM predictions appeared to correlate with the measured values with noticeably better accuracy both in the frequency and moisture domains. As a result, the ability to make accurate predictions for dielectric spectra with the use of the SDMM was shown to be doubted, regarding the moist soils, which falls out of the scope of soils measured and fitted to develop the SDMM in [1]-[3]. 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The analysis is based on the measured dielectric data borrowed from [4], in which a set of soils measured includes all of grain-size distributions that are observed in nature, with measurements being performed over the range from 40 MHz to 17 GHz. In the case of the soil measured in [4] that has an intermediate position in terms of its texture parameters, input data for the semiempirical model were attained, using the texture and soil mineralogy data available in [4], and dielectric spectra predictions both for the dielectric constant and loss factor were calculated. Simultaneously, with the use of the dielectric data of [4], input parameters for the generalized refractive mixing dielectric model were derived, and dielectric predictions for the same moistures were calculated. Comparative analysis based on the measured data together with both predictions showed the semiempirical dielectric mixing model to generate dielectric constants and loss factors that have a noticeable bias relative to the measured ones, correlation coefficients being on the order of 0.93. At the same time, the GRMDM predictions appeared to correlate with the measured values with noticeably better accuracy both in the frequency and moisture domains. As a result, the ability to make accurate predictions for dielectric spectra with the use of the SDMM was shown to be doubted, regarding the moist soils, which falls out of the scope of soils measured and fitted to develop the SDMM in [1]-[3]. 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The analysis is based on the measured dielectric data borrowed from [4], in which a set of soils measured includes all of grain-size distributions that are observed in nature, with measurements being performed over the range from 40 MHz to 17 GHz. In the case of the soil measured in [4] that has an intermediate position in terms of its texture parameters, input data for the semiempirical model were attained, using the texture and soil mineralogy data available in [4], and dielectric spectra predictions both for the dielectric constant and loss factor were calculated. Simultaneously, with the use of the dielectric data of [4], input parameters for the generalized refractive mixing dielectric model were derived, and dielectric predictions for the same moistures were calculated. Comparative analysis based on the measured data together with both predictions showed the semiempirical dielectric mixing model to generate dielectric constants and loss factors that have a noticeable bias relative to the measured ones, correlation coefficients being on the order of 0.93. At the same time, the GRMDM predictions appeared to correlate with the measured values with noticeably better accuracy both in the frequency and moisture domains. As a result, the ability to make accurate predictions for dielectric spectra with the use of the SDMM was shown to be doubted, regarding the moist soils, which falls out of the scope of soils measured and fitted to develop the SDMM in [1]-[3]. On the contrary, the GRMDM [5] has proved to be able to make predictions for the dielectric spectra of moist soils with the same error as that of initial dielectric measurements.</abstract><pub>IEEE</pub><doi>10.1109/IGARSS.2007.4422905</doi><tpages>5</tpages></addata></record>
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subjects Dielectric constant
Dielectric loss measurement
Dielectric losses
Dielectric measurements
dielectric properties
Electrochemical impedance spectroscopy
generalized refractive mixing dielectric model
Loss measurement
Microwave bands
moist soi
Moisture measurement
Predictive models
semiempirical dielectric model
Soil measurements
title Validation of the soil dielectric spectroscopic models with input parameters based on soil composition
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