Digital soil mapping (DSM) approaches provide soil information by utilising the relationship between soil properties and environmental variables. Calibration of DSM models requires measurements that may often have substantial measurement errors which propagate to the DSM outputs and need to be accounted for. This study applied a geostatistical-based DSM approach that incorporates measurement error variances in the covariance structure of the spatial model, weights measurements in accordance with their measurement accuracies and assesses the effects of measurement errors on the accuracies of DSM outputs. The method was applied in the Western Cameroon, where soil samples from 480 locations were collected and analysed for pH, clay and soil organic carbon (SOC) using conventional and mid-infrared spectroscopy methods. Variogram parameters and regression coefficients were estimated using residual maximum likelihood under two scenarios: with and without taking measurement errors into account. Performance of the spatial models in the two scenarios was compared using validation metrics obtained with three types of cross-validation. Acknowledging measurement errors impacted the regression coefficients and influenced the variogram parameters by reducing the nugget and sill variance for the three soil properties. Validation metrics including mean error, root mean square error and model efficiency coefficient were quite similar in both scenarios, but the prediction uncertainties were more realistically quantified by the models that account for measurement errors, as indicated by accuracy plots. There were relatively small absolute differences in predicted values of soil properties of up to 0.1 for pH, 1.6% for clay and 2 g/kg for SOC between the two scenarios. We emphasised the need of incorporating measurement errors in DSM approaches to improve uncertainty quantification, particularly when applying spectroscopy for estimating soil properties. Further development of the approach is the extension to non-linear machine learning regression methods.

中文翻译：

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考虑数字土壤测绘中的分析和近端土壤传感误差

数字土壤测绘 (DSM) 方法通过利用土壤特性和环境变量之间的关系来提供土壤信息。DSM 模型的校准需要测量，这些测量通常可能具有大量测量误差，这些误差会传播到 DSM 输出并需要加以考虑。本研究应用了一种基于地统计的 DSM 方法，该方法将测量误差方差纳入空间模型的协方差结构中，根据测量精度对测量进行加权，并评估测量误差对 DSM 输出精度的影响。该方法应用于喀麦隆西部，收集了来自 480 个地点的土壤样品，并使用常规和中红外光谱方法分析了 pH、粘土和土壤有机碳 (SOC)。变异函数参数和回归系数是在两种情况下使用残差最大似然估计的：考虑和不考虑测量误差。使用通过三种交叉验证获得的验证指标比较了两种场景中空间模型的性能。承认测量误差会影响回归系数，并通过减少三种土壤特性的块金和基台方差来影响变异函数参数。包括平均误差、均方根误差和模型效率系数在内的验证指标在两种情况下都非常相似，但预测不确定性通过考虑测量误差的模型更真实地量化，如准确度图所示。两种情景之间土壤特性预测值的绝对差异相对较小，pH 值高达 0.1，粘土为 1.6%，SOC 为 2 g/kg。我们强调了在 DSM 方法中加入测量误差以改善不确定性量化的必要性，特别是在应用光谱法估计土壤特性时。该方法的进一步发展是对非线性机器学习回归方法的扩展。