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Calibration and Assessment of Capacitance-Based Soil Moisture Sensors
The aim of this paper was: (1) to establish soil-specific laboratory calibration equations for two types of volumetric water content sensors (5TE and GS3); and (2) to evaluate their measurement accuracy and precision for estimating the soil moisture contents in sand, based on the established laboratory calibration equations and the corresponding default factory calibration equations provided by the manufacturers. The study revealed that the developed laboratory calibration equations (linear and polynomial) improved the sensors’ measurement accuracy compared to that obtained using their corresponding factory calibration equations. Based on the root mean square error (RMSE), the 5TE sensor exhibited higher accuracy (RMSE=1.15%) with the third-order polynomial equation, followed by the second-order equation (RMSE=1.32%) and a linear regression equation (RMSE=1.63%). Thus, the third-polynomial type equation was considered to be the most suitable calibration model for the 5TE sensors in sand. In contrast, the second-order polynomial calibration equation provided highest accuracy for the GS3 sensors with the lowest RMSE of 0.86%.
Calibration and Assessment of Capacitance-Based Soil Moisture Sensors
The aim of this paper was: (1) to establish soil-specific laboratory calibration equations for two types of volumetric water content sensors (5TE and GS3); and (2) to evaluate their measurement accuracy and precision for estimating the soil moisture contents in sand, based on the established laboratory calibration equations and the corresponding default factory calibration equations provided by the manufacturers. The study revealed that the developed laboratory calibration equations (linear and polynomial) improved the sensors’ measurement accuracy compared to that obtained using their corresponding factory calibration equations. Based on the root mean square error (RMSE), the 5TE sensor exhibited higher accuracy (RMSE=1.15%) with the third-order polynomial equation, followed by the second-order equation (RMSE=1.32%) and a linear regression equation (RMSE=1.63%). Thus, the third-polynomial type equation was considered to be the most suitable calibration model for the 5TE sensors in sand. In contrast, the second-order polynomial calibration equation provided highest accuracy for the GS3 sensors with the lowest RMSE of 0.86%.
Calibration and Assessment of Capacitance-Based Soil Moisture Sensors
Bhuiyan, Mohammad Zahidul I. (author) / Wang, Shanyong (author) / Carter, John (author) / Raka, Tabassum Mahzabeen (author)
Geo-Congress 2020 ; 2020 ; Minneapolis, Minnesota
Geo-Congress 2020 ; 754-766
2020-02-21
Conference paper
Electronic Resource
English
Calibration and Assessment of Capacitance-Based Soil Moisture Sensors
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