Soil is a fundamental natural resource which people rely on for the production of food, fiber, and energy. Given the importance of soils, there is a need for regular monitoring to detect changes in its status so as to implement appropriate management in the event of degradation. Application of ultra-violet (UV), visible (VIS) and near-infrared (NIR) spectroscopy for prediction of soil properties offer a cost and time effective approach for evaluation of soil structural quality. The main objective of this study was to evaluate the ability of reflectance spectroscopy in the UV, VIS and NIR ranges to predict several soil properties simultaneously. Soil samples (n=210 in 15cm depth from surface) were used for simultaneous estimation of pH, electrical conductivity (EC), air-dry gravimetric water content, organic carbon (OC), total nitrogen (TN), free iron, clay, sand, and silt contents, cation exchange capacity (CEC), and exchangeable calcium (Ca), magnesium (Mg), potassium (K), and sodium (Na). After removal of outliers identified by principal components analysis (PCA), 75% of the sample set was randomly selected for calibration (n=157) and the remaining used for validation. Modified partial least squares (PLS) regression with cross-validation was used to develop prediction models. The reliability of the models was assessed using the coefficient of determination in validation (R2V) and the ratio of standard deviation of the reference data in the validation set to the standard error of prediction (RPDV). Excellent models were achieved for TN, OC and pH (RPD≥ 3.5, R2c ≥ 0.91). Good models were obtained for EC and CEC (RPD from 2 to 3, R2c ≥ 0.75), and moderate capability for prediction of particle size and exchangeable cations (RPD from 1.5 to 1.99, R2c ≥ 0.68). Soil spectra produced acceptable models for predicting relevant soil structural indicators, and the mean soil spectra were different between soil structural classes. Therefore VNIRS has the potential as a non-dest
