2019Unpublished venueRequires access

Preliminary Applicability Analysis of Soil Dielectric Constant Model of the Different Soil Texture Condition

Yayong Sun, Jianwei Ma, Jie Peng, Huang Shifeng, Yang Kun, Zhu Peng, He Zhu

Open publisher page 4 citations

Abstract

The soil dielectric models used to describe the relationship between soil permittivity and soil water content, mainly include Mironov model, Dobson model, Wang and Schmugge model and Hallikainen model. As a key input parameter of four dielectric constant models, soil texture directly affects the simulation ability. In order to analyze the adaptability of the dielectric constant model under different soil texture conditions, two evaluation indexes of the root mean square error (RSME) and linear correlation coefficient (R) were used to evaluate the accuracy. The results show that the four models have great differences in the simulation accuracy of loamy sand, sandy loam, clay loam, sandy clay, silty clay and clay soil. The Mironov model has the highest simulation accuracy, the Haillikainen model is second, the Dobson and the Wang model are the worst. It is further shown that the Mironov dielectric constant model has the best overall applicability for soil moisture inversion for L-band microwave remote sensing, but, with the increase of the content of silt in the soil, for example the soil with abnormally high silt content in silt loam, silt clay loam and silt clay, the Dobson model was the best choice.

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What this paper is about

The soil dielectric models used to describe the relationship between soil permittivity and soil water content, mainly include Mironov model, Dobson model, Wang and Schmugge model and Hallikainen model. As a key input parameter of four dielectric constant models, soil texture directly affects the simulation ability. In order to analyze the adaptability of the dielectric constant model under different soil texture conditions, two evaluation indexes of the root mean square error (RSME) and linear correlation coefficient (R) were used to evaluate the accuracy. The results show that the four models have great differences in the simulation accuracy of loamy sand, sandy loam, clay loam, sandy clay, silty clay and clay soil. The Mironov model has the highest simulation accuracy, the Haillikainen model is second, the Dobson and the Wang model are the worst. It is further shown that the Mironov dielectric constant model has the best overall applicability for soil moisture inversion for L-band microwave remote sensing, but, with the increase of the content of silt in the soil, for example the soil with abnormally high silt content in silt loam, silt clay loam and silt clay, the Dobson model was the best choice.

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Available abstract

The soil dielectric models used to describe the relationship between soil permittivity and soil water content, mainly include Mironov model, Dobson model, Wang and Schmugge model and Hallikainen model. As a key input parameter of four dielectric constant models, soil texture directly affects the simulation ability. In order to analyze the adaptability of the dielectric constant model under different soil texture conditions, two evaluation indexes of the root mean square error (RSME) and linear correlation coefficient (R) were used to evaluate the accuracy. The results show that the four models have great differences in the simulation accuracy of loamy sand, sandy loam, clay loam, sandy clay, silty clay and clay soil. The Mironov model has the highest simulation accuracy, the Haillikainen model is second, the Dobson and the Wang model are the worst. It is further shown that the Mironov dielectric constant model has the best overall applicability for soil moisture inversion for L-band microwave remote sensing, but, with the increase of the content of silt in the soil, for example the soil with abnormally high silt content in silt loam, silt clay loam and silt clay, the Dobson model was the best choice.

Key concepts: Dielectric, Soil texture, Constant (computer programming), Texture (cosmology), Soil science, Environmental science, Geotechnical engineering, Computer science

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