Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey A. Volkan Bilgili a , H.M. van Es b, * , F. Akbas c , A. Durak c , W.D. Hively d a Department of Soil Science, Agriculture Faculty, Harran University, Sanliurfa 63300, Turkey b Department of Crop and Soil Science, Cornell University, Ithaca, NY 14853-1901, USA c Department of Soil Science, Agriculture Faculty, Gaziosmanpasa University, Tokat 60100, Turkey d USDA-ARS Hydrology and Remote Sensing Laboratory, 10300 Baltimore Avenue, Beltsville, MD 20705, USA article info Article history: Received 24 April 2007 Received in revised form 8 August 2008 Accepted 5 August 2009 Available online 10 September 2009 Keywords: MARS PLSR Soil reflectance Turkey VNIR spectroscopy abstract Reflectance spectroscopy can be used to nondestructively characterize materials for a wide range of applications. In this study, visible-near infrared reflectance spectroscopy (VNIR) was evaluated for prediction of diverse soil properties related to four different soil series of the Entisol soil group within a single field in northern Turkey. Soil samples were collected from 512 locations in a 25  25 m sampling grid over a 32 ha (800  400 m) area. Air-dried soil samples were scanned at 1 nm resolution from 350 to 2500 nm, and calibrations between soil physical and chemical properties and reflectance spectra were developed using cross-validation under partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). Raw reflectance and first derivative reflectance data were used separately and combined for all samples in the data set. Data were additionally divided into two random subsets of 70 and 30% of the full data, which were each used for calibration and validation. Overall, MARS provided better predictions when under cross-validation. However, PLSR and MARS results were comparable in terms of prediction accuracy when using separate data sets for calibration and validation. No improvement was obtained by combining first derivative and raw data. Strongest correlations were obtained with exchangeable Ca and Mg, cation exchange capacity, and organic matter, clay, sand, and CaCO 3 contents. When soil data were classified into groups, VNIR spectroscopy estimated class memberships well, especially for soil texture. In conclusion, VNIR spectroscopy was variably successful in estimating soil properties at the field scale, and showed potential for substituting laboratory analyses or providing inexpensive co-variable data. Ó 2009 Elsevier Ltd. All rights reserved. 1. Introduction 1.1. Use of reflectance spectroscopy for soil analyses Soil characteristics often exhibit high spatial variability, even across single agricultural fields. Mapping soil fertility indicators and quantifying soil parameters that control the fate of chemicals are therefore important for site-specific soil management and protec- tion of the environment. Typically, large numbers of samples must be collected and analyzed in order to capture this spatial variability and adequately estimate soil properties. Conventional methods may be expensive and require large amounts of labor and chemicals for performing these tasks (Viscarra Rossel and McBratney, 1998a,b). Visible and near infrared reflectance (VNIR) spectroscopy shows promise as a low-cost method that can be used to substitute or complement traditional soil characterization methods. Once calibrated, it can be used to predict multiple soil characteristics simultaneously and explain within-field spatial variability (Bowers and Hanks, 1965; Ben-Dor and Banin, 1995; Chang et al., 2001; Islam et al., 2003; Shepherd and Walsh, 2002). The scale of application for this method can affect its utility. Shepherd and Walsh (2002) and Brown et al. (2006) used VNIR spectroscopy based on samples from many soil types over large geographical areas. This type of sampling generally provides a wide range of soil indicator values, which promotes good regressive results. On the other hand, wide distribution of soils in a sample set challenges the methodology by requiring greater universality in the statistical prediction relations, especially when including different parent materials (Reeves and van Kessel, 1999; Shepherd and Walsh, 2002). In precision agriculture, the interest is often limited to characterizing single or multiple fields within a relatively small geographical area. This poses different challenges in that the data * Corresponding author. Tel.: þ1 607 255 5459; fax: þ1 607 255 2644. E-mail address: hmv1@cornell.edu (H.M. van Es). Contents lists available at ScienceDirect Journal of Arid Environments journal homepage: www.elsevier.com/locate/jaridenv 0140-1963/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaridenv.2009.08.011 Journal of Arid Environments 74 (2010) 229–238