In search of an optimum sampling algorithm for prediction of soil properties from infrared spectra.

Background: The use of visible-near infrared (vis-NIR) spectroscopy for rapid soil characterisation has gained a lot of interest in recent times. Soil spectra absorbance from the visible-infrared range can be calibrated using regression models to predict a set of soil properties. The accuracy of these regression models relies heavily on the calibration set.

Combining ancillary soil data with VisNIR spectra to improve predictions of organic and inorganic carbon content of soils.

While traditional laboratory methods of determining soil organic carbon (SOC) content are generally simple, this becomes more challenging when carbonates are present in the soil; such is commonly found in semi-arid areas. Additionally, soil inorganic carbon (SIC) content itself is difficult to determine. This study uses visible near infrared (VisNIR) spectra to predict SOC and SIC contents of samples, and the impact of including soil pH and soil total carbon (STC) data as predictor variables was evaluated.