Estimating a Soil Quality Index with Visible Near-Infrared Reflectance Spectroscopy.

Sensor-based approaches to assessment and quantification of soil quality are important to facilitate cost-effective, site-specific soil management. The objective of this research was to evaluate the ability of visible, near-infrared (VNIR) diffuse reflectance spectroscopy to estimate multiple soil quality indicators (SQIs) and Soil Management Assessment Framework (SMAF) scores. A total of 234 soil samples from two depths (0-5 and 5-15 cm) were obtained in 2008 from 17 agricultural management systems located in and around the Central Mississippi River Basin Long Term Agroecosystem Research (CMRB LTAR) site in the Central Claypan Region of Missouri, USA. The VNIR spectra were obtained on oven-dried and field–moist soil, and calibration models were developed with partial least squares (PLS) regression. Models were evaluated using the coefficient of determination (R²), residual prediction deviation (RPD), and the ratio of prediction error to interquartile range (RPIQ). The most reliable estimation results were achieved using oven-dry soil for organic C, β-glucosidase, total N, the biological SMAF score, the organic C score, and the β-glucosidase score (R² ≥ 0.76, RPD ≥ 2.0, RPIQ ≥ 3.2). Using field-moist soils, the most reliable estimation results were achieved for organic C and the organic C score (R² ≥ 0.80, RPD ≥ 2.1, RPIQ ≥ 3.6). Incorporating the bulk density score and P score as auxiliary variables with the VNIR spectra improved estimation of the overall SMAF soil quality score for oven-dry soil (R² = 0.76, RPD = 2.0, RPIQ = 3.1) and field-moist soil (R² = 0.75, RPD = 1.9, RPIQ = 2.8). These results demonstrate the robustness of VNIR estimation of biological SQIs, and illustrate the potential for rapid, in-field quantification of soil quality by fusing VNIR sensors with auxiliary data obtained from complementary sensors or supplemental analyses.