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.

中文翻译：

---

关于农业土壤介电常数模型的实验室校准：系统孔隙率变化的影响

介电技术是测量土壤含水量的基本方法，它们通常依赖于介电常数和含水量之间介电常数模型的常规实验室校准。作为不可忽略的因素，孔隙率在某些模型中作为校准常数的构造有所不同，但实验室模型校准和现场应用过程中的系统孔隙率变化尚未得到很好的解决。基于时域反射仪实验室校准实验，本研究使用三个预先建立的介电常数模型研究了这一问题：普渡校准方程（美国材料试验学会模型[ASTM]），复折射率模型（CRIM）和分段CRIM模型（CRIMP）。结果表明，与使用可变孔隙度的校准相比，用于校准的广义孔隙度常数会带来额外的结构偏差，并且其大小会随模型结构而变化。广义孔隙率常数的偏差可以进一步放大ASTM和CRIM在低粘土含量土壤中的结构偏差，但是由于模型结构误差的压倒性作用，它对于高粘土含量土壤不敏感。只有具有“完美”模型结构的CRIMP模型才能有效地应对系统的孔隙度变化，并保持稳定的内置功能，可根据现有的土壤数据估算校准常数。这些发现突显了忽略孔隙率变化不应该被视为校准和应用预先建立的模型的原因。