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Title: Estimation of soil physical properties from sensor-based soil strength and apparent electrical conductivity

item CHO, YONG-JIN - University Of Missouri
item Sudduth, Kenneth - Ken
item CHUNG, SUN-OK - University Of Missouri

Submitted to: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 6/9/2014
Publication Date: 7/13/2014
Citation: Cho, Y., Sudduth, K.A., Chung, S. 2014. Estimation of soil physical properties from sensor-based soil strength and apparent electrical conductivity. ASABE Annual International Meeting. Paper No. 141914006. [Available Online].

Interpretive Summary: Quantification of soil physical properties such as bulk density, water content, and texture fractions is traditionally accomplished by collection of soil samples in the field and subsequent laboratory analysis. This process is inefficient and may be impractical when measurements are needed at many locations across a field or landscape, for example to develop precision agriculture management plans, to quantify spatial variability in soil quality, or to drive spatially-explicit process-based models. Mobile proximal sensors that can collect spatially dense data could be a potential solution to this problem. The purpose of this research was to examine the use of data from soil electrical conductivity (ECa) and soil strength sensors to estimate bulk density, soil water content, and soil clay fraction. Data used included three different ECa measurements and two different soil strength measurements for three field sites in central Missouri. Using correlation and regression analysis, we found that soil clay at discrete soil depths was well-estimated by combinations of the three ECa variables, depending on the depth in question. Sensor-based soil water content estimates were of variable accuracy. Bulk density estimates were not good unless laboratory-measured water content was included as an explanatory variable along with sensor data. Fusion of data from ECa and soil strength sensors showed potential for estimating soil physical properties, but inclusion of a soil water content sensor would be required for better results. These results will benefit researchers and practitioners interested in applying sensors to improve the efficiency of quantifying spatially-variable soil physical properties.

Technical Abstract: Quantification of soil physical properties has traditionally been through soil sampling and laboratory analyses, which is time-, cost-, and labor-consuming, making it difficult to obtain the spatially-dense data required for precision agriculture. Soil strength and apparent electrical conductivity (ECa) sensor measurements are significantly affected by soil texture, bulk density (BD), and water content (WC). The objective of this study was to explore the potential to estimate soil texture, BD, and WC using sensor-based soil strength and ECa data obtained from sites with varying soil physical properties. Data collected from three research sites in Missouri included on-the-go horizontal soil strength at five depths up to 50 cm on a 10-cm interval, cone index at the same depths, ECa measured by EM38 and Veris devices, and depth-dependent soil properties including soil texture, BD, and WC. Accuracy of the regression models for WC estimation varied, with better results when adding soil strength data to ECa data and when including depth as a variable. Regressions estimating BD generally included ECa, and also included soil strength at some depths and sites. BD estimates were improved considerably by adding WC to the model, suggesting the need for a WC sensor. Soil clay content (as a component of texture) was well-estimated by ECa variables and the accuracy was improved in some cases by adding soil strength and depth variables. This study showed the potential of fusing data from multiple mobile proximal sensors to estimate major soil physical properties.