4.4 Article

Complete Soil Texture is Accurately Predicted by Visible Near-Infrared Spectroscopy

Journal

SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
Volume 81, Issue 4, Pages 758-769

Publisher

WILEY
DOI: 10.2136/sssaj2017.02.0066

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Funding

  1. Danish Pesticide Leaching Assessment Programme
  2. Aarhus University Research Foundation [AUFF-E-2016-9-36]

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The particle-size curve (PSC) defines the continuous size distribution of mineral particles <2 mm. It is used for soil classification and to derive functional soil parameters such as the soil-water characteristic (SWC) curve, soil hydraulic properties, and gas transport properties. Conventional methods for measuring texture are time-consuming and most methods only provide discrete particle-size intervals. The Rosin-Rammler and Fredlund functions enable a continuous description of the size distribution of mineral particles using two and three fitting parameters, respectively. Visible near-infrared diffuse reflectance spectroscopy (vis-NIRS) is a time-saving and well-known alternative soil analysis method. In this study vis-NIRS was used to indirectly obtain PSCs by predicting the fitting parameters of the Rosin-Rammler (alpha(R), beta(R)) and Fredlund (alpha(F), n(F), and m(F)) functions. A total of 431 soil samples from 7 agricultural fields in Denmark and Greenland were analyzed for soil texture (clay: 0.028-0.426 kg kg(-1)) and organic matter (OM) content (0.018-0.143 kg kg(-1)). The Rosin-Rammler and Fredlund functions were fitted to the PSCs. Soil diffuse reflectance was measured from 400 to 2500 nm with a spectrometer. The important spectral regions for correlating alpha(R), beta(R), alpha(F), n(F), m(F), and OM to spectra were selected using forward interval partial least squares (iPLS) regression on a calibration set. The soil spectra showed high correlation to PSC function parameters and OM content for the validation set. Further, vis-NIRS cross-validation models for the fitting parameters of the Rosin-Rammler and Fredlund functions were built on all samples and used as input for the PSCs, generating RMSE values of 4.2 and 3.5%, respectively. Both PSC functions convincingly covered the PSC variation within fields, although the Fredlund function performed slightly better. From one vis-NIRS scanning the complete texture comprising the PSC and the OM content was successfully characterized.

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