HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Janik, L. J. Articles by Rawson, A. Search for Related Content PubMed Articles by Janik, L. J. Articles by Rawson, A. GeoRef GeoRef Citation Agricola Articles by Janik, L. J. Articles by Rawson, A. Related Collections Soil Methods/Instrumentation Soil Analysis Soil Physics

Rapid Prediction of Soil Water Retention using Mid Infrared Spectroscopy

^a CSIRO Land & Water PMB 2, Glen Osmond, South Australia 5064
^b CSIRO Sustainable Ecosystems Waite Rd. Urrbrae South Australia 5062
^c New South Wales Dep. of Natural Resources c/o Faculty of Science and Agriculture Charles Sturt Univ., Leeds Pde, Orange PO Box 883 NSW 2800

View larger version (15K): [in this window] [in a new window]   Fig. 1. Mid-infrared (MIR) diffuse reflectance mean spectrum of samples from the CSIRO calibration soil set. Assignments of the major mineral and organic components are indicated.

View larger version (18K): [in this window] [in a new window]   Fig. 2. (a) Principal components analysis (PCA) score2 (PC-2) vs. score1 (PC-1) plot and (b) the corresponding PCA loadings for the CSIRO calibration samples in the 4000 to 450 cm–1 frequency range.

View larger version (27K): [in this window] [in a new window]   Fig. 3. Partial least squares loading weights for principal components score1 (PC-1) and score2 (PC-2) for volumetric water retention for the CSIRO calibration set at matric suctions (a) 10 kPa and (b) 1500 kPa.

View larger version (13K): [in this window] [in a new window]   Fig. 4. Partial least squares (PLS) cross-validation regression plot for the prediction of volumetric water retention v (% v/v) at (a) 10 kPa suction and (b) 1500 kPa suction for the CSIRO calibration set.

View larger version (26K): [in this window] [in a new window]   Fig. 5. (a) Principal components analysis (PCA) score2 (PC-2) vs. score1 (PC-1) plot for the combined spectral data for the CSIRO calibration samples, New South Wales (NSW) validation samples, and the 35 NSW validation samples representing outliers with scores lying outside the extremities of the CSIRO calibration samples, and (b) the corresponding PCA loadings for PC-1 and PC-2.

View larger version (19K): [in this window] [in a new window]   Fig. 6. Regression plots for volumetric water retention predicted by mid-infrared partial least squares (MIR PLS) (% v/v) at matric suctions (a) 10 kPa and (b) 1500 kPa for the New South Wales validation set using the CSIRO calibration samples.

View larger version (20K): [in this window] [in a new window]   Fig. 7. Regression plots for predicted volumetric water retention (% v/v) at matric suctions (a) 10 kPa and (b) 1500 kPa for the New South Wales (NSW) validation set using the combined CSIRO calibration plus 35 NSW validation samples.

View larger version (21K): [in this window] [in a new window]   Fig. 8. Regression plots for predicted volumetric water retention (% v/v) at matric suctions (a) 10 kPa and (b) 1500 kPa for the New South Wales (NSW) validation set using the combined CSIRO calibration plus 35 NSW validation samples after preprocessing the water retention data with a loge–preprocessing transform before partial least squares analysis.