Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma

Abstract

Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at the site with lower levels of salinity and clay.

Performance of Soil Moisture Sensors in Florida Sandy Soils

“…The GS1 sensor also had low RMSE (0.026 m 3 m −3 ) ( Table 3). This value was smaller than the RMSE value of 0.048 reported by Datta et al [6] for the GS1 in a fine sandy loam soil. These authors reported that TDR315, CS655, and GS1 sensors performed better in a soil with lower salinity and lower clay content.…”

“…Many researchers have also reported overestimation of θv by the CS655 sensor [56][57][58]. Similar results from Datta et al [6] showed that all sensors (TDR315, CS655, and GS1) overestimated θv in a low clay content soil located in central Oklahoma. Adeyemi et al [52] found that TDR315 and GS1 underestimated θv in sandy loam soil.…”

“…Soil moisture sensors (SMS) are a recent innovation with potential to estimate soil volumetric water content (θv) and electrical conductivity in real time [4][5][6]. Those sensors provide an accurate estimate of moisture content by measuring the dielectric constant of the soil or relative permittivity (ε) in response to water content, determined by the time in which an electromagnetic pulse travels in the soil [7,8].…”

Soil Structure and Texture Effects on the Precision of Soil Water Content Measurements with a Capacitance- Based Electromagnetic Sensor

“…Data from GS-1 soil moisture sensors in situ at the location of the soil sampling sites were collected for an uncertainty analysis for use in irrigation scheduling. Previously, Datta et al (2018) suggested that GS-1 sensors presented acceptable accuracies for managing irrigation at sites with low salinity and low clay content based on the reported root mean square errors. In the current study, the capacitance-based GS-1 sensors were installed and monitored for the 2018 growing season at depths of 0.15, 0.46, and 0.76 m. The temporal trends ( fig.…”

“…The performance of EM soil water sensors under various soil conditions has been investigated extensively (Geesing et al, 2004;Mittelbach et al, 2012;Singh et al, 2018;Varble and Chávez, 2011;Vaz et al, 2013), and some studies have proposed correcting for non-water influences on  v by developing soil-specific calibrations. For soil moisture sensors based on capacitance and frequency domain technology, the sensor response over a large  v range has been captured in the laboratory (Adeyemi et al, 2016;Goswami et al, 2019;Ojo et al, 2015;Provenzano et al, 2016;Santhosh et al, 2017) and in the field (Datta et al, 2018;Huang et al, 2017;Lea-Cox et al, 2018;Ojo et al, 2014;Rudnick et al, 2015;Sui, 2017).…”