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.

包括环境协变量（如果可用）可能是实现更高的土壤预测性能的有价值的策略，但是仍然不知道是否应将环境数据用作协变量，并结合Vis-NIR光谱来预测土壤有机碳（SOC）或作为对土壤光谱库（SSL）进行分层的标准。我们假设通过将辅助环境数据作为协变量包含在立体主义模型中，可以改善Vis-NIR光谱预测SOC的性能，从而克服了数据分层的局限性。为了测试这一点，我们评估了六个协变量集，其中组合了以下协变量：光谱数据，光谱类别，儿童学数据和使用Cubist模型校准的环境数据。由2个组成的SSL 考虑到不同的协变量集，使用了来自巴西南部的461个样本来校准模型。模型1包括Vis-NIR反射率（350-2500 nm），模型2包括Vis-NIR反射率和光谱等级。模型3包括地理区域，土地利用/土地覆盖率（LULC），气候，母体材料，海拔和粘土含量，而模型4包括模型3中的参数以及Vis-NIR反射率。模型5包括Vis-NIR反射率，光谱等级，自然地理区域，LULC和土壤质地类别，而模型6包括Vis-NIR反射率，光谱等级，自然地理区域，LULC，气候，母体材料，高程和粘土含量。将环境数据作为协变量与Vis-NIR一起使用可改善SOC的预测性能。在测试的六组协变量中，与模型1相比，包含所有协变量（模型6）的集合显示出最佳性能，将预测准确性提高了12％，并将预测误差（RMSE）降低了22％。数据作为协变量是实现更准确的SOC预测的有希望的策略。因此，与SOC相关的协变量（例如高程，粘土含量）提高了Vis-NIR光谱模型的预测准确性。通过防止模型校准中样本量的减少，Cubist模型能够达到相似的准确性，并克服了文献记载的分层策略所带来的局限性。模型6是最准确的，将环境数据作为协变量输入是实现更准确的SOC预测的有希望的策略。因此，与SOC相关的协变量（例如高程，粘土含量）提高了Vis-NIR光谱模型的预测准确性。通过防止模型校准中样本量的减少，Cubist模型能够达到相似的准确性，并克服了文献记载的分层策略所带来的局限性。模型6是最准确的，将环境数据作为协变量输入是实现更准确的SOC预测的有希望的策略。因此，与SOC相关的协变量（例如高程，粘土含量）提高了Vis-NIR光谱模型的预测准确性。通过防止模型校准中样本量的减少，Cubist模型能够达到相似的准确性，并克服了文献记载的分层策略所带来的局限性。