Evaluation of a Multivariate Calibration Model for the WET Sensor That Incorporates Apparent Dielectric Permittivity and Bulk Soil Electrical Conductivity

The measurement of apparent dielectric permittivity (ε_s) by low-frequency capacitance sensors and its conversion to the volumetric water content of soil (θ) through a factory calibration is a valuable tool in precision irrigation. Under certain soil conditions, however, ε_s readings are substantially affected by the bulk soil electrical conductivity (EC_b) variability, which is omitted in default calibration, leading to inaccurate θ estimations. This poses a challenge to the reliability of the capacitance sensors that require soil-specific calibrations, considering the EC_b impact to ensure the accuracy in θ measurements. In this work, a multivariate calibration equation (multivariate) incorporating both ε_s and EC_b for the determination of θ by the capacitance WET sensor (Delta-T Devices Ltd., Cambridge, UK) is examined. The experiments were conducted in the laboratory using the WET sensor, which measured θ, ε_s, and EC_b simultaneously over a range of soil types with a predetermined actual volumetric water content value (θ_m) ranging from θ = 0 to saturation, which were obtained by wetting the soils with four water solutions of different electrical conductivities (EC_i). The multivariate model’s performance was evaluated against the univariate CAL and the manufacturer’s (Manuf) calibration methods with the Root Mean Square Error (RMSE). According to the results, the multivariate model provided the most accurate θ estimations, (RMSE ≤ 0.022 m³m⁻³) compared to CAL (RMSE ≤ 0.027 m³m⁻³) and Manuf (RMSE ≤ 0.042 m³m⁻³), across all the examined soils. This study validates the effects of EC_b on θ for the WET and recommends the multivariate approach for improving the capacitance sensors’ accuracy in soil moisture measurements.