On the laboratory calibration of dielectric permittivity models for agricultural soils: Effect of systematic porosity variation. Issue 1 (12th January 2021)

• Record Type: Journal Article
• Authors: Pan, Xicai; Han, Yudi; Chun, Kwok Pan; Zhang, Jiabao; Ma, Donghao; Gao, Hongkai
• Is Part Of: Vadose zone journal. Volume 20:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-12
• Subjects: Soil science -- Periodicals; Zone of aeration -- Periodicals; Groundwater flow -- Periodicals; Groundwater flow; Zone of aeration; Periodicals; Electronic journals; 631.4
• DOI: 10.1002/vzj2.20096
• Languages: English
• ISSNs: 1539-1663

Abstract

Abstract: Dielectric techniques are fundamental methods for measuring soil water content, and they commonly rely on the conventional laboratory calibration of the dielectric permittivity models between a dielectric constant and water content. As a non‐negligible factor, porosity has been constructed differently in some models as a calibration constant, but the systematic porosity variations during the laboratory model calibration and field applications are not yet well addressed. Based on time‐domain reflectometer laboratory calibration experiments, this study investigated this issue using three preestablished dielectric permittivity models: the Purdue calibration equation (American Society for Testing and Materials model [ASTM]), the complex refractive index model (CRIM), and a piecewise CRIM model (CRIMP). Results demonstrate that a generalized porosity constant used in the calibration would bring in additional structural bias compared with the calibration using variable porosities, and its magnitude varies with the model structure. The deviation of the generalized porosity constant can further amplify the structural bias of ASTM and CRIM for soils with low clay content, but it is insensitive for the soils with high clay content due to the overwhelming role of model structure error. Only the model CRIMP with a "perfect" model structure can effectively cope with the systematic porosity variation and keep a stable built‐in capability for estimating calibration constants from readily available soil data. These findings highlight ignoring porosity variation should not be taken for granted for calibrating and applying the preestablished models.