Soil organic carbon (SOC) plays a major role concerning chemical, physical, and biological soil properties and functions. To get a better understanding of how soil management affects the SOC content, the precise monitoring of SOC on long-term field experiments (LTFEs) is needed. Visible and near-infrared (Vis–NIR) reflectance spectrometry provides an inexpensive and fast opportunity to complement conventional SOC analysis and has often been used to predict SOC. For this study, 100 soil samples were collected at an LTFE in central Germany by two different sampling designs. SOC values ranged between 1.5 % and 2.9 %. Regression models were built using partial least square regression (PLSR). In order to build robust models, a nested repeated 5-fold group cross-validation (CV) approach was used, which comprised model tuning and evaluation. Various aspects that influence the obtained error measure were analysed and discussed. Four pre-processing methods were compared in order to extract information regarding SOC from the spectra. Finally, the best model performance which did not consider error propagation corresponded to a mean RMSE_MV of 0.12 % SOC (R²=0.86). This model performance was impaired by ΔRMSE_MV=0.04 % SOC while considering input data uncertainties (ΔR²=0.09), and by ΔRMSE_MV=0.12 % SOC (ΔR²=0.17) considering an inappropriate pre-processing. The effect of the sampling design amounted to a ΔRMSE_MV of 0.02 % SOC (ΔR²=0.05). Overall, we emphasize the necessity of transparent and precise documentation of the measurement protocol, the model building, and validation procedure in order to assess model performance in a comprehensive way and allow for a comparison between publications. The consideration of uncertainty propagation is essential when applying Vis–NIR spectrometry for soil monitoring.

中文翻译：

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土壤有机碳光谱函数中的误差传播

土壤有机碳（SOC）在化学，物理和生物土壤特性和功能方面起着重要作用。为了更好地了解土壤管理如何影响SOC含量，需要对长期田间试验（LTFE）进行SOC的精确监控。可见光和近红外（Vis–NIR）反射光谱法为常规SOC分析提供了廉价且快速的机会，并且经常被用来预测SOC。对于本研究，通过两种不同的采样设计，在德国中部的LTFE中收集了100个土壤样品。SOC值介于1.5％和2.9％之间。回归模型是使用偏最小二乘回归（PLSR）建立的。为了构建健壮的模型，使用了嵌套的重复5倍组交叉验证（CV）方法，其中包括模型调整和评估。分析和讨论了影响获得的误差度量的各个方面。比较了四种预处理方法，以便从光谱中提取有关SOC的信息。最后，没有考虑误差传播的最佳模型性能对应于平均RMSE_MV为0.12％SOC（ R ²＝ 0.86）。该模型性能通过受损Δ RMSE _MV = 0.04  ％SOC，同时考虑输入数据的不确定性（ Δ - [R ² = 0.09），和通过Δ RMSE _MV = 0.12  ％SOC（ Δ - [R ² = 0.17）在考虑的不适当预处理。抽样设计的效果达到Δ RMSE _MV 0.02％SOC的（ Δ - [R ² = 0.05）。总体而言，我们强调必须透明，精确地记录测量协议，模型构建和验证过程，以便以全面的方式评估模型性能并允许在出版物之间进行比较。当使用Vis-NIR光谱仪进行土壤监测时，必须考虑不确定性传播。