Imaging the electrical conductivity of the soil profile and its relationships to soil water patterns and drainage characteristics

Soil water content (theta) measurement is vital for accurate irrigation scheduling. Electromagnetic induction surveys can be used to map spatial variability of theta when other soil properties are uniform. However, depth-specific theta variations, essential for precision irrigation management, have been less investigated using this method. A quasi-2-dimensional inversion model, capable of inverting apparent soil electrical conductivity (ECa) data to calculate estimates of true electrical conductivity (sigma) down the entire soil profile, was developed using ECa data collected by a multi-coil Dualem-421S sensor. The optimal relationships between sigma and volumetric water content (theta v) were established using all coil arrays of the Dualem-421S, a damping factor of 0.04, an initial model of 35 mSm(-1), and with ten iterations (R-2 = 0.70, bias = 0.00 cm(3)cm(-3), RMSE = 0.04 cm(3)cm(-3)). These relationships were then used to derive soil profile images of these properties, and as expected, theta v and sigma follow similar trends down the soil profile. The derived soil profile images for theta v have potential use for irrigation scheduling to two ECa-derived soil management zones under a variable rate irrigation system at this case study site. They reflect the intrinsic soil differences that occur between texture, texture transitions and drainage characteristics. The method can also be used to guide placement of soil moisture sensors for in-season monitoring of spatio-temporal variations of theta v. This soil imaging method showed good potential for predicting 2D depth profiles of soil texture, moisture and drainage characteristics, and supporting soil, plant and irrigation management.

Automated summary

This study developed a quasi-2-dimensional inversion model based on electromagnetic induction surveys to derive true electrical conductivity and volumetric water content of the soil profile, establishing relationships between the two properties. The method was applied for irrigation scheduling and monitoring spatio-temporal variations of soil moisture, showing potential for predicting 2D depth profiles of soil characteristics and supporting soil management.