Estimating texture and organic carbon of an Oxisol by near infrared spectroscopy

Abstract

ABSTRACT Laboratory analyses are a fundamental basis for monitoring soil behavior. These analyses are usually tedious and expensive depending on the methodology used, which may limit data acquisition. The aim of this research was to evaluate the potential of Near Infrared (NIR) diffuse reflectance spectroscopy for the estimation of texture and Soil Organic Carbon (SOC) of an Oxisol. A total of 313 samples were collected at fixed depths of 0.0-0.10, 0.10-0.20, 0.20-0.30, 0.30-0.40 and 0.40-0.50 m in 70 points distributed in 248 ha, from which SOC and the fractions of sand, silt and clay were determined. The spectral signatures were obtained from a NIRFlex sensor, and the modeling was done applying partial least squares regression. A highly representative model was obtained for the SOC estimation, with a coefficient of determination (R2) of 0.97, Root Mean Square Error (RMSE) of 1.10 g kg-1 and Residual Prediction Deviation (RPD) of 5.63. For the textural fractions, estimation models of lesser performance were obtained, with R2values of 0.62; 0.44 and 0.62, RMSE values of 1.10%, 2.92% and 3.08%, and RPD values of 1.82, 1.61 and 1.81 for sand, silt and clay, respectively. By means of geostatistical interpolation surfaces, the behavior of the measured and spectrally estimated variables was compared. NIR spectroscopy proved to be a viable alternative for the precise estimation of SOC, while for the textural fractions it is convenient to explore the improvement of the estimates.

Keywords

• NIR spectroscopy
• Pedometrics
• Spectral modelling
• Geostatistics
• Chemometrics