Empirical two-point α-mixing model for calibrating the ECH 2O EC-5 soil moisture sensor in sands

Abstract

Recently improved ECH2O soil moisture sensors have received significant attention in many field and laboratory applications. Focusing on the EC-5 sensor, a simple and robust calibration method is proposed. The sensor-to-sensor variability in the readings (analog-to-digital converter (ADC) counts) among 30 EC-5 sensors was relatively small but not negligible. A large number of ADC counts were taken under various volumetric water contents (θ) using four test sands. The proposed two-point α-mixing model, as well as linear and quadratic models, was fitted to the ADC - θ data. Unlike for conventional TDR measurements, the effect of sensor characteristics is lumped into the empirical parameter α in the two-point a-mixing model. The value of α was fitted to be 2.5, yielding a nearly identical calibration curve to the quadratic model. Errors in θ associated with the sensor-to-sensor variability for the two-point α-mixing model were ±0.005 cm3 cm-3 for dry sand and ±0.028 cm3 cm-3 for saturated sand. In the validation experiments, the highest accuracy in water content estimation was achieved when sensor-specific ADCdry and ADCsat were used in the two-point α-mixing model. Assuming that α = 2.5 is valid for most mineral soils, the two-point α-mixing model only requires the measurement of two extreme ADC counts in dry and saturated soils. Sensor-specific ADC dry and ADCsat counts are readily measured in most cases. Therefore, the two-point α-mixing model (with α = 2.5) can be considered as a quick, easy, and robust method for calibrating the ECH 2O EC-5 sensor. Although further investigation is needed, the two-point α-mixing model may also be applied to calibrating other sensors.