Environmental covariates improve the spectral predictions of organic carbon in subtropical soils in southern Brazil

Abstract Including environmental covariates, when available, can be a valuable strategy for achieving higher soil predictive performance, but it is still unknown whether environmental data should be used either as covariates, combined with Vis-NIR spectra, to predict soil organic carbon (SOC) or as criteria to stratify a soil spectral library (SSL). We hypothesized that the performance of Vis-NIR spectroscopy in predicting SOC could be improved by the inclusion of auxiliary environmental data as covariates in Cubist models and thereby overcome the data stratification limitations. To test this, we evaluated six covariate sets, in which the following covariates were combined: spectral data, spectral classes, pedological data, and environmental data calibrated using Cubist models. An SSL composed of 2,461 samples from southern Brazil was used to calibrate models considering different sets of covariates. Model 1 included Vis-NIR reflectance (350–2500 nm), and Model 2 included Vis-NIR reflectance and spectral class. Model 3 included physiographic region, land-use/land-cover (LULC), climate, parent material, elevation, and clay content, while Model 4 included the parameters from Model 3 with the addition of Vis-NIR reflectance. Model 5 included Vis-NIR reflectance, spectral class, physiographic region, LULC, and soil textural class, while Model 6 included Vis-NIR reflectance, spectral class, physiographic region, LULC, climate, parent material, elevation, and clay content. The inclusion of environmental data as covariates along with Vis-NIR improved the predictive performance for SOC. Among the six covariate sets tested, the set including all covariates (Model 6) showed the best performance, improving the accuracy in prediction by 12% and reducing the prediction error (RMSE) by 22% compared to Model 1. Model 6 was the most accurate, and the input of environmental data as covariates is a promising strategy to achieve more accurate SOC predictions. Thus, covariates (e.g. elevation, clay content) that correlate with SOC improved the prediction accuracy of Vis-NIR spectroscopy models. The Cubist model was able to achieve similar accuracy and overcome the limitations presented by the stratification strategy documented in the literature by preventing the reduction of the sample size in the calibration of the models.