Abstract
Electromagnetic sensors are widely used to monitor soil water content (θ); however, site-specific calibrations are necessary for accurate measurements. This study compares regression models used for calibration of soil moisture sensors and investigates the relation between soil attributes and the adjusted parameters of the specific calibration equations. Undisturbed soil samples were collected in the A and B horizons of two Ultisols and two Inceptisols from the Mantiqueira Range in Southeastern Brazil. After saturation, the Theta Probe ML2X was used to obtain the soil dielectric constant (ε). Several readings were made, ranging from saturation to oven-dry. After each reading, the samples were weighted to calculate θ (m3 m–3). Fourteen regression models (linear, linearized, and nonlinear) were adjusted to the calibration data and checked for their residue distribution. Only the exponential model with three parameters met the regression assumptions regarding residue distribution. The stepwise regression was used to obtain multiple linear equations to estimate the adjusted parameters of the calibration model from soil attributes, with silt and clay contents providing the best relations. Both the specific and the general calibrations performed well, with RMSE values of 0.02 and 0.03 m3 m–3, respectively. Manufacturer calibration and equations from the literature were much less accurate, reinforcing the need to develop specific calibrations.
Highlights
Assessing soil water content (θ) at various spatial and temporal scales is important for a wide range of applications, such as water dynamics and hydrological modeling (Bertoldi et al, 2014; Zhou et al, 2018), management of water resources (Dobriyal et al, 2012), and irrigation planning (Hillel, 2013)
This study compares regression models used for calibration of soil moisture sensors and investigates the relation between soil attributes and the adjusted parameters of the specific calibration equations
The normal distribution is a description of the randomness of these errors and this assumption was more met because often only minor deviations were observed in the Q-Q plots
Summary
Assessing soil water content (θ) at various spatial and temporal scales is important for a wide range of applications, such as water dynamics and hydrological modeling (Bertoldi et al, 2014; Zhou et al, 2018), management of water resources (Dobriyal et al, 2012), and irrigation planning (Hillel, 2013). Topp et al (1980) established the theoretical and practical basis for determining θ from the soil dielectric constant (ε), providing an equation for sensor calibration. This equation works for a wide range of soils, it may be unsuitable for soils with low bulk density (Regalado et al, 2003; Silva et al, 2012) and with high contents of clay (Kargas et al, 2013), organic matter (Shibchurn et al, 2005), and Fe and Al oxides (Kaiser et al, 2010). There are no calibrations for tropical mountainous soils, which usually have a shallow solum, a deep weathering profile, and high silt and organic matter contents
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