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Applied and Environmental Soil Science
Volume 2012 (2012), Article ID 294121, 14 pages
http://dx.doi.org/10.1155/2012/294121
Research Article

Effects of Subsetting by Carbon Content, Soil Order, and Spectral Classification on Prediction of Soil Total Carbon with Diffuse Reflectance Spectroscopy

1Natural Resources and Environmental Management Department, University of Hawai‘i Mānoa, 1910 East-West Road, Sherman 101, Honolulu, HI 96822, USA
2Biology & Natural Resources Department, Principia College, 1 Maybeck Place, Elsah, IL 62028, USA
3Tropical Plant and Soil Sciences Department, University of Hawai‘i Mānoa, 3190 Maile Way, Honolulu, HI 96822, USA
4Soil and Water Science Department, University of Florida, 2169 McCarty Hall, P.O. Box 110290, Gainesville, FL 32611-0290, USA

Received 15 March 2012; Revised 20 August 2012; Accepted 14 October 2012

Academic Editor: Sabine Chabrillat

Copyright © 2012 Meryl L. McDowell et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Subsetting of samples is a promising avenue of research for the continued improvement of prediction models for soil properties with diffuse reflectance spectroscopy. This study examined the effects of subsetting by soil total carbon ( ) content, soil order, and spectral classification with k-means cluster analysis on visible/near-infrared and mid-infrared partial least squares models for prediction. Our sample set was composed of various Hawaiian soils from primarily agricultural lands with contents from <1% to 56%. Slight improvements in the coefficient of determination ( ) and other standard model quality parameters were observed in the models for the subset of the high activity clay soil orders compared to the models of the full sample set. The other subset models explored did not exhibit improvement across all parameters. Models created from subsets consisting of only low samples (e.g., < 10%) showed improvement in the root mean squared error (RMSE) and percent error of prediction for low soil samples. These results provide a basis for future study of practical subsetting strategies for soil prediction.