Diffuse reflectance spectroscopy (DRS) is an emerging alternative to conventional laboratory methods of soil testing although poor estimation accuracies continue to limit its wide-spread adoption. Spectral reflectance values of different aggregate size fractions and their bulk soil have recently been shown to improve the estimation accuracy of soil textural fractions in the DRS approach. The objective of the present study is to assess this approach for estimating nine different soil parameters covering soil structure, soil chemical properties and micronutrient contents. We used three different chemometric models (PLSR: partial-least-squares regression, PLSR_FS: PLSR with feature selection, and PLSR_LW: locally-weighted PLSR) on three different spectral libraries (total of 1013 soil samples) to test this objective. On an average, 29% improvement in the coefficient of determination (R²) was achieved with the addition of aggregate spectra to bulk soil spectra. Improvement in the root-mean-squared error (RMSE) ranged from 5% to 48% for aggregate size distribution parameters, 7% to 40% for chemical properties, and 7% to 26% for micronutrient contents. These improvements were also observed to be a result of high correlation coefficients between estimated soil properties and the best-performing aggregate fraction spectra, which suggested that the spectral reflectance of individual aggregate size fractions holds important soil information for improving the performance of a DRS model. Segregation of soils into individual size fractions and the collection of corresponding spectra are additional burdens in the proposed approach. Nevertheless, the extent of improvements in the model performance observed in this study opens an opportunity to develop an automated system for separating soil aggregates and collecting spectra so that the information may readily be used for improving the performance of the DRS approach.

漫反射光谱 (DRS) 是传统实验室土壤测试方法的新兴替代方法，尽管估计精度差继续限制其广泛采用。最近显示，不同聚集体大小部分及其大块土壤的光谱反射率值可以提高 DRS 方法中土壤质地部分的估计精度。本研究的目的是评估这种方法来估计九种不同的土壤参数，包括土壤结构、土壤化学性质和微量营养素含量。我们使用了三种不同的化学计量模型（PLSR：偏最小二乘回归、PLSR _FS：具有特征选择的 PLSR 和 PLSR _LW：局部加权 PLSR）在三个不同的光谱库（总共 1013 个土壤样本）上测试这个目标。平均而言，决定系数（R ²) 是通过将聚合光谱添加到大块土壤光谱来实现的。骨料粒度分布参数的均方根误差 (RMSE) 改善范围为 5% 至 48%，化学性质改善了 7% 至 40%，微量营养素含量改善了 7% 至 26%。还观察到这些改进是由于估计的土壤性质和表现最佳的骨料分数光谱之间的高相关系数的结果，这表明单个骨料大小分数的光谱反射率对于提高 DRS 模型的性能具有重要的土壤信息。将土壤分离成单独的大小部分和收集相应的光谱是所提出方法的额外负担。尽管如此，