Effects of Subsetting by Carbon Content, Soil Order, and Spectral Classification on Prediction of Soil Total Carbon with Diffuse Reflectance Spectroscopy

Abstract

Subsetting of samples is a promising avenue of research for the continued improvement of prediction models for soil properties with diffuse reflectance spectroscopy. This study examined the effects of subsetting by soil total carbon (Ct ) content, soil order, and spectral classification with k-means cluster analysis on visible/near-infrared and mid-infrared partial least squares models for Ct prediction. Our sample set was composed of various Hawaiian soils from primarily agricultural lands with Ct contents from <1% to 56%. Slight improvements in the coefficient of determination (R2 ) and other standard model quality parameters were observed in the models for the subset of the high activity clay soil orders compared to the models of the full sample set. The other subset models explored did not exhibit improvement across all parameters. Models created from subsets consisting of only low Ct samples (e.g., Ct < 10%) showed improvement in the root mean squared error (RMSE) and percent error of prediction for low Ct soil samples. These results provide a basis for future study of practical subsetting strategies for soil Ct prediction.