Use of color parameters in the grouping of soil samples produces more accurate predictions of soil texture and soil organic carbon

Prediction of soil properties such as texture and soil organic carbon (SOC) content by reflectance spectroscopy (RS) is influenced by the heterogeneity of soil samples used to calibrate multivariate models. These soil properties are directly related to color, which, in turn, can be estimated by color parameters derived from the visible (Vis) spectrum at no additional cost. At present, only a few publications have addressed the effect that input data structure and soil heterogeneity have on model performance. Therefore, the objectives of this study were to use Vis-based-color parameters combined with multivariate statistical techniques to group soil samples and comparing the results of different SOC, clay, sand and silt prediction models. Soil sampling was conducted over an area of approximately 500 ha in the region of São Joaquim National Park, Santa Catarina State, Brazil, where a total of 260 soil samples were collected. Soil reflectance data were obtained by Vis-NIR-SWIR spectroscopy in the laboratory, through a spectroradiometer that covers the 350–2500 nm. Soil organic carbon content was determined by dry combustion in an elemental analyzer. Sand, silt and clay fractions were determined using the pipette method. Twenty-two components of color parameters were derived from the Vis spectrum with the use of colorimetry models. For the definition of the most appropriate number of soil sample clusters, two multivariate statistical analyzes: principal component analysis (PCA) and cluster analysis of the samples were applied to the color parameter values. Partial least squares regression (PLSR) and support vector machines (SVM) multivariate models were calibrated for each cluster and also for the models without stratification using 260 soil samples and 95 selected samples (MWS-260 and MWS-95). Overall, the PLSR model performed better than the SVM model, as confirmed by the statistical difference between RMSE results. Multivariate statistical analyzes applied to color parameters were able to group soil samples with similar characteristics, reducing data amplitude and improving the accuracy of soil property predictions. These analyzes demonstrated that the use of Vis-based-color parameters to group soil samples can be a quick and inexpensive way to increase the potential of spectroscopy to accurately predict soil physical and chemical properties.