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

Resumen

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 refl ectance spectroscopy for the estimation of texture and Soil Organic Carbon (SOC) of an Oxisol. A
total of 313 samples were collected at fi xed 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 coeffi cient 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 R2 values
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.