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Evaluation of Soil Moisture Sensing Technologies in Silt Loam and Loamy Sand Soils: Assessment of Performance, Temperature Sensitivity, and Site- and Sensor-Specific Calibration Functions
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Transactions of the ASABE. 64(4): 1123-1139. (doi: 10.13031/trans.14112) @2021
Authors: Kiran Sharma, Suat Irmak, Meetpal S. Kukal, Mehmet C. Vuran, Amit J. Jhala, Xin Qiao
Keywords: Capacitance, Irrigation, Sensors, Site-specific calibration, Soil moisture, TDR, Time-domain reflectometry.
Nine soil moisture sensors were evaluated in two soil types under different installation orientations. Sensor-specific and soil-specific calibration functions were developed and validated. Sensor performance improved substantially (31% to 89%) after calibration. On average, sensor performance was 67% better in loamy sand than in silt loam soil.
Nine soil moisture sensors were evaluated in two soil types under different installation orientations.
Sensor-specific and soil-specific calibration functions were developed and validated.
Sensor performance improved substantially (31% to 89%) after calibration.
On average, sensor performance was 67% better in loamy sand than in silt loam soil.
Abstract. Reliable soil moisture information is vital for optimal irrigation management, farm-level agronomic decision-making, hydrologic studies, and cropping systems modeling. A wide range of soil moisture sensing technologies is commercially available, but their performance must be evaluated for diverse conditions of use. In this research, we investigated nine soil moisture sensors based on time-domain reflectometry, capacitance, and electrical resistance principles in production field conditions with two installation orientations, i.e., vertical (V) and horizontal (H), in two soils (silt loam and loamy sand) and two growing seasons (2017 and 2018). Performance parameters deduced from the 2017 datasets revealed that sensor type and soil type significantly affected the soil moisture sensor performance under factory calibration (F.C.); however, sensor installation orientation did not. Thus, the sensors were only evaluated based on their performance in horizontal orientation in both soils. Precision and accuracy were considered targets to assist in appropriate sensor selection. To improve sensor accuracy, site-specific calibration (S.S.C.) functions were developed and validated using independent datasets from 2018. Considering mean bias error (MBE), all sensors overestimated volumetric soil water content (θv) in both soils, with the exception of TEROS 21 (MPS-6) in silt loam and JD probe in loamy sand. On average, sensor performance was 67% better in loamy sand than in silt loam. Overall, the sensors showed higher precision in silt loam (R2 = 0.53 to 0.93) than in loamy sand (R2 = 0.25 to 0.82). Substantial post-S.S.C. improvement (32% to 89%) was observed in all sensors‘ performance relative to F.C. in silt loam. In loamy sand, while most sensors performed reasonably well with F.C., considerable improvements (28% to 85%) were observed with S.S.C. Significant differences (p < 0.05) were observed in sensors‘ sensitivity to soil temperature (Tsoil), which ranged from 14°C to 23°C in silt loam and from 14°C to 25°C loamy sand during the experiments. The CS655, 10HS, 5TE, and TEROS 21 (MPS-6) sensors showed significant (p < 0.05) sensitivity to Tsoil fluctuations, with Tsoil explaining a maximum of 17% of the variance observed in sensor performance. No statistically significant (p > 0.05) sensitivity was detected for any of the sensors in loamy sand. TEROS 21 (MPS-6) had the highest sensitivity to Tsoil with a slope of -4.25. In contrast, while statistically significant (p < 0.05), 5TE was the least sensitive to Tsoil variability with a slope of 1.81. The information, data, and analyses presented here can be instrumental for informed sensor selection and use in decision-making in production fields with similar soil textures and soil water regimes.(Download PDF) (Export to EndNotes)