Temperature and texture dependent dielectric model for moist soils at 1.4 GHz - IRD - Institut de recherche pour le développement Access content directly
Journal Articles IEEE Geoscience and Remote Sensing Letters Year : 2013

Temperature and texture dependent dielectric model for moist soils at 1.4 GHz

Abstract

In this paper, a monofrequent dielectric model for moist soils taking into account dependences on the temperature and texture is proposed, in the case of electromagnetic frequency equal to 1.4 GHz. The proposed model is deduced from a more general model proposed by Mironov and Fomin (2009) that provides estimations of the complex dielectric constant (CDC) of moist soils as a function of frequency, temperature, moisture and texture of soils. The latter employs the physical laws of Debye, Clausius- Mossotii, and the law of ion conductance to calculate the CDC of water solutions in the soil. The parameters of the respective physical laws were determined by using the CDCs of moist soils measured by Curtis et al. (1995) for a wide ensemble of soil textures (clay content from 0 to 76%), moistures (from drying at 105°C to nearly saturation), temperatures (10 - 40°C), and frequencies (0.3 - 26.5 GHz). This model has standard deviations of calculated CDCs from the measured values equal to 1.9 and 1.3 for the real and imaginary parts of CDC, respectively. In the model proposed in this paper, the respective standard deviations were decreased to the values of 0.87 and 0.26. In addition, the equations to calculate the complex dielectric constant as a function of moisture, temperature, and texture were represented in a simple form of the refractive mixing dielectric model, which is commonly used in the algorithms of radiometric and radar remote sensing to retrieve moisture in the soil.
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Dates and versions

ird-00828324 , version 1 (31-05-2013)

Identifiers

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V. Mironov, Yann H. Kerr, J.-P. Wigneron, L. Kosolapova, F. Demontoux. Temperature and texture dependent dielectric model for moist soils at 1.4 GHz. IEEE Geoscience and Remote Sensing Letters, 2013, 10 (3), pp.419-423. ⟨10.1109/LGRS.2012.2207878⟩. ⟨ird-00828324⟩
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