Submitted to: ASABE Annual International Meeting
Publication Type: Abstract only
Publication Acceptance Date: 7/18/2013
Publication Date: 7/21/2013
Citation: Sudduth, K.A., Kremer, R.J., Veum, K.S., Kitchen, N.R. 2013. Estimating a soil quality index with VNIR Reflectance Spectroscopy. ASABE Annual International Meeting. Available Online. Interpretive Summary:
Technical Abstract: Knowledge of spatial variability in soil quality is important to assess the impact of site-specific management on the soil. Quantification of soil quality commonly involves measurement of multiple indicator properties which are transformed by appropriate weighting functions and then combined into a soil quality index. Measurement of these multiple soil properties at the scale required to discern spatial variation requires considerable time and expense using standard methods, so alternative sensor-based approaches would be useful. The purpose of this research was to evaluate the ability of visible and near infrared (VNIR) diffuse reflectance spectroscopy (DRS) to estimate soil properties that are candidate soil quality indicators, and also to use VNIR-DRS to estimate an overall soil quality index, as determined by the Soil Management Assessment Framework (SMAF). Soil samples were obtained in 2008 from two depths (0-5 and 5-15 cm) at a long-term (since 1991) experimental site in central Missouri where cropping systems were replicated across a typical claypan soil landscape. Laboratory analyses were conducted for indicators of soil quality, including organic and biomass carbon, soil glucosidase enzyme activity, plant available nutrients, and soil texture. VNIR-DRS data were obtained in the laboratory using a spectrometer with a wavelength range of 350 to 2500 nm and calibrations to soil properties were developed with partial least squares regression. Soil quality indexes were determined (1) using SMAF and indicator values derived with standard laboratory methods, (2) using SMAF and indicator values estimated by VNIR-DRS, and (3) by direct estimation using VNIR-DRS. These soil quality indices were then evaluated for their ability to discriminate cropping system differences. Results from this study will be important in helping to determine the feasibility of a sensor-based soil quality index.