Aggregate stability (AS) is an important parameter to evaluate soil resistance to erosion. The conventional determination methods to measure AS are time consuming, difficult and labour intensive. Visible (vis) and near infrared (NIR) spectroscopy could be a better alternative to the conventional determination methods of AS. This study explored the possibilities of estimating three AS indices, reflecting stability upon slow wetting (SW), fast wetting (FW) and mechanical breakdown (MB), using vis-NIR spectra data on some air-dried, non-sieved soils of the Belgian loam belt. Partial least squares regression (PLSR) was used to build calibration models for the three stability indices, using a calibration set accounting for 70% and a validation set of 30% of the total samples. Results showed that all three AS indices can be predicted to appreciable accuracies from vis-NIR-PLSR models [coefficient of determination (R²) = 0.72–0.80, residual prediction deviation (RPD) = 1.93–2.27, ratio of performance to interquartile range (RPIQ) = 2.23–4.09 and root mean square error (RMSE) = 0.29–0.52 mm]. The prediction results suggest that omission of sample pre-treatment by sieving or grinding may have very limited impact on the prediction accuracy. This opens up opportunities for the in-situ deployment of vis-NIR spectroscopy, provided that problems associated with variable soil moisture contents can be overcome.

集料稳定性（AS）是评价土壤抗侵蚀能力的重要参数。测量AS的传统测定方法耗时、困难且劳动强度大。可见光 (vis) 和近红外 (NIR) 光谱可以更好地替代传统的 AS 测定方法。本研究探讨了估计三个 AS 指数的可能性，这些指数反映了慢润湿 (SW)、快速润湿 (FW) 和机械击穿 (MB) 的稳定性，使用了一些风干、非筛分土壤的可见近红外光谱数据。比利时壤土带。使用偏最小二乘回归（PLSR）建立三个稳定性指标的校准模型，使用占总样本70%的校准集和30%的验证集。² ) = 0.72–0.80，残差预测偏差 (RPD) = 1.93–2.27，性能与四分位距 (RPIQ) 的比值 (RPIQ) = 2.23–4.09 和均方根误差 (RMSE) = 0.29–0.52 mm]。预测结果表明，省略过筛或研磨的样品预处理可能对预测精度的影响非常有限。这为可见近红外光谱的原位部署提供了机会，前提是可以克服与可变土壤水分含量相关的问题。