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The relationship between visible-near-infrared (Vis-NIR-SWIR) spectra and soil organic carbon (SOC) and the effects of preprocessing techniques on SOC predictive models have been shown in several studies. However, little attention has been given to the effect of analytical methods used to produce the SOC data used to calibrate those models. The predictive performance of Vis-NIR spectral models depends not only on the preprocessing technique and machine learning method but also on the analytical method employed to produce the SOC data. Our hypothesis is that some combinations of preprocessing and models may be more sensitive to laboratory (measurement) error than others. To test this hypothesis, we evaluated the leave-one-out cross-validation performance of three predictive models (Random Forest (RF), Cubist, and Partial Least Square Regression (PLSR)) calibrated using SOC data produced via three analytical methods (dry combustion (DC) and wet combustion with quantification by titration (WCt) and colorimetry (WCc)) and three Vis-NIR spectra preprocessing techniques (smoothing (SMO), continuum removal (CRR), and Savitzky-Golay first derivative (SGD)). The prediction performance varied among the models. DC and WCt provided a higher correlation between SOC and spectra than WCc, and thus, resulted in higher accuracy. The Cubist+CRR was ranked the best performing model, with an average of R2 = 0.81 and RMSE = 0.81% among analytical methods. Cubist+CRR also minimized the accuracy differences resulting from SOC analytical methods employed. The RF model had low accuracy and was unable to explain more than 46% of the variance. Overall, the analytical method significantly affects SOC predictions, and its impact may be larger than the preprocessing.

Diffuse reflectance spectroscopy (DRS) has the potential to predict soil organic carbon (SOC). However, it is still little used as a matter of routine in soil laboratories in Brazil. The objective of this study was to make evaluations as to whether SOC predicted by spectral techniques can replace measurement by routine chemical methods with no loss in quality and be applied in the recommendation of nitrogen fertilizer as well as identifying the best prediction strategies to use. A data set containing 2,471 samples from six soil spectral libraries (SSL) was used to develop spectroscopic models for SOC content prediction, including consideration of sample stratification and preprocessing techniques. The SOC was quantified through the analytical-chemical methods of wet combustion with determination by titration, designated as the reference method (REM), and colorimeter, designated as the routine method (ROM in an independent data set). SOC contents predicted by the spectral analysis method (SAM) were compared to the REM and ROM results, converted to soil organic matter (SOM) and used for N recommendations. The best estimate for SOM content using the SAM was achieved through stratification of the SSL and application of the standard normal variate (SNV) preprocessing. The SOC predicted by spectral techniques proved capable of replacing the SOC measured by routine chemical methods with no loss of quality and supported by an appropriate nitrogen fertilizer recommendation, provided the models met the conditions and possessed the characteristics of the samples to be analyzed.

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.

More accurate models for the prediction of soil organic carbon (SOC) by visible-near-infrared (Vis-NIR) spectroscopy remains a challenging task, especially when the soil spectral libraries (SSL) is composed of soils with a high pedological variation. One proposition to increase the models accuracy is to reduce the SSL variance, which can be achieved by stratifying the library into sub-libraries. Thus, the main objective of this study was to evaluate whether the stratification of a SSL by environmental, pedological and Vis-NIR spectral criteria results in greater accuracy of spectroscopic models than to general models for prediction of SOC content. The performance of the models was evaluated considering the variance of soil components and sample number. In addition, we tested the effect of two spectral preprocessing techniques and two multivariate calibration methods on spectroscopic modeling. For these purposes, a SSL composed of 2471 samples from Southern Brazil was stratified based on i) physiographic region; ii) land-use/land-cover; iii) soil texture, and iv) spectral class. Two spectral processing techniques: Savitzky-Golay - 1st derivative (SGD) and continuum removed reflectance (CRR) and two multivariate methods (partial least squares regression - PLSR and Cubist) were used to fit the models. The best performances for the global and local models were achieved with the CRR spectral processing associated with the Cubist method. The stratification of the SSL in more homogeneous sample groups by environmental criteria (physiographic regions and land-use/land-cover) improved the accuracy of SOC predictions compared to pedological (soil texture) and Vis-NIR spectral (spectral classes) criteria. The reduction in the number of samples negatively affected the performance of models for sub-libraries with high pedological and spectral variation. Stratification criteria were proposed in a flowchart to assist in decision making in future studies. Our findings suggest the importance of sample balance across environmental, pedological and spectral strata, in order to optimize SOC predictions.