Diffuse reflectance spectroscopy (DRS) operating in 350-2500 nm wavelength range is fast emerging as a rapid and non-invasive technique for analyzing multiple soil attributes. Because the spectral reflectance values in this range of wavelengths are highly co-linear, it is important to select relevant spectral information from the reflectance spectra to build a robust spectral algorithm. The objective of this study is to examine the utility of different variable indicators such as partial least squares regression (PLSR) coefficients (β), variable influence on projection, squared residual (SqRes), correlation coefficient (r), biweightmidcorrelation (bicor), mutual information based adjacency value (AMI), signal-to-noise ratio (StN), covariance procedures (CovProc) and their combinations in conjunction with an ordered predictor selection (OPS) approach for selecting optimum number of spectral variables (NSV) which could improve DRS model performance. The approach was tested with the PLSR models of pH, organic carbon, extractable iron (Fe), sand and clay contents and geometric mean diameter in Vertisols and Alfisols. The prediction accuracy of best models selected via OPS approach was found to be superior to full-spectrum (NSV = 2048) model for all the soil attributes. The percent decrease in RMSE value was found to be highest for Fe (14%, NSV = 79) in Alfisols followed by pH (9%, NSV = 660) in Vertisols while it varied between 3 and 8% for other soil attributes. Although the results were not conclusive in favor of one specific variable indicator, the CovProc and bicor were found to be more appropriate for accurate and moderate DRS models in this study, respectively. The overall results of this study advocate the use of OPS approach with variable indicators and their combinations as a promising strategy to develop simple and effective DRS models for soils.

All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Diffuse Reflectance Spectroscopic Approach for the Characterization of Soil Aggregate Size Distribution Soil Physics S oil structure is much studied in different geoscience fields because of its role in providing the fundamental pore-solid network through which mass and energy are transported. The development of a pore fabric in soil is even considered as the fundamental process of regolith formation (Graham et al., 2010; Brantley, 2010). Recently, soil aggregation to form a specific soil structure has been linked with the sequestration of specific C fractions (Stamati et al., 2013). Although the importance of soil structure has long been recognized, its characterization remains a challenging task. The geometric mean diameter (GMD) and probabilistic frameworks of aggregate size distribution (ASD) functions (Fieller and Flenley, 1992) are, by far, the only quantitative descriptors of soil structure.

All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Dependency Measures for Assessing the Covariation of Spectrally Active and Inactive Soil Properties in Diffuse Reflectance Spectroscopy Soil Physics R apid and reliable assessment of soil characteristics has become a mainstream component for monitoring and management of agricultural and natural resources. Soil properties vary widely both in time and space (Cohen et al., 2005; Minasny and Hartemink, 2011). Even with decades of research and development, the in situ and frequent assessment of different soil properties remains a formidable task. Remote sensing tools appear to be a feasible technology to provide a comprehensive solution to this problem (Vasques et al., 2010). Specifically, the diffuse reflectance spectroscopy (DRS) technique in the visible to near-infrared (VNIR) regions (350-2500 nm) has emerged as a rapid and noninvasive technique for the estimation of soil properties (Ben-Dor et al., 2009). Two

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