In situ, diffuse reflectance spectroscopy (DRS) profile soil sensors have the potential to provide both rapid and high-resolution prediction of multiple soil properties for precision agriculture, soil health assessment, and other applications related to environmental protection and agronomic sustainability. However, the effects of soil moisture, other environmental factors, and artefacts of the in-field spectral data collection process often hamper the utility of in situ DRS data. Various processing and modeling techniques have been developed to overcome these challenges, including external parameter orthogonalization (EPO) transformation of the spectra. In addition, Bayesian modeling approaches may improve prediction over traditional partial least squares (PLS) regression. The objectives of this study were to predict soil organic carbon (SOC), total nitrogen (TN), and texture fractions using a large, regional dataset of in situ profile DRS spectra and compare the performance of (1) traditional PLS analysis, (2) PLS on EPO-transformed spectra (PLS-EPO), (3) PLS-EPO with the Bayesian Lasso (PLS-EPO-BL), and (4) covariate-assisted PLS-EPO-BL models. In this study, soil cores and in situ profile DRS spectrometer scans were obtained to ~1 m depth from 22 fields across Missouri and Indiana, USA. In the laboratory, soil cores were split by horizon, air-dried, and sieved (<2 mm) for a total of 708 samples. Soil properties were measured and DRS spectra were collected on these air-dried soil samples. The data were randomly split into training (n = 308), testing (n = 200), and EPO calibration (n = 200) sets, and soil textural class was used as the categorical covariate in the Bayesian models. Model performance was evaluated using the root mean square error of prediction (RMSEP). For the prediction of soil properties using a model trained on dry spectra and tested on field moist spectra, the PLS-EPO transformation dramatically improved model performance relative to PLS alone, reducing RMSEP by 66% and 53% for SOC and TN, respectively, and by 76%, 91%, and 87% for clay, silt, and sand, respectively. The addition of the Bayesian Lasso further reduced RMSEP by 4–11% across soil properties, and the categorical covariate reduced RMSEP by another 2–9%. Overall, this study illustrates the strength of the combination of EPO spectral transformation paired with Bayesian modeling techniques to overcome environmental factors and in-field data collection artefacts when using in situ DRS data, and highlights the potential for in-field DRS spectroscopy as a tool for rapid, high-resolution prediction of soil properties.

The soil health concept has evolved over the past several decades, recognizing that the response of dynamic soil properties to management is dependent on site‐specific factors. The Soil Health Assessment Protocol and Evaluation (SHAPE) tool provides scores and benchmark values by forming soil peer groups based on site‐specific, climate‐edaphic characteristics. Data for model development were compiled from the Cornell Soil Health Laboratory and the Kellogg Soil Survey Laboratory databases. The SHAPEv1.0 interpretation curves produce scores between 0% and 100% for measured laboratory values that reflect the quantile within a population conditional cumulative distribution function along with measures of uncertainty. The original SHAPE tool was developed for soil organic carbon and has been expanded to include two wet aggregate stability methods, permanganate oxidizable organic carbon, autoclaved citrate extractable protein, and 4‐day microbial respiration. In addition, SHAPE provides site‐specific benchmark values at user‐defined percentiles within a given soil peer group. The difference between the actual measured value and the selected benchmark value represents the soil health opportunity gap. Case studies in Missouri and Texas show that the SHAPE scoring curves are sensitive to land‐use and management practices across multiple soil types and provide a regionally relevant interpretation of key soil health indicators.