Soil aggregate stability (AS) is a crucial soil physical property for sustainable agricultural and environmental land management practices. Yet, its conventional laboratory methods are fastidious and time consuming thereby restricting high-density sampling and large-scale estimation. Therefore, the potential of fusing visible near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy to enhance the prediction of three AS indices, namely, fast wetting (FW), slow wetting (SW) and mechanical breakdown (MB) on some Belgian Retisol, Cambisol and Luvisol topsoils was evaluated. Partial least squares regression (PLSR) was used to build calibration models for the three AS indices using the vis-NIR spectra, MIR spectra and the fusion of both spectra (SF). Another data fusion approach (i.e. model output averaging, MOA) included the use of four averaging algorithms. Results showed that MIR models outperformed the vis-NIR models for all three indices. The SF approach showed an improved prediction performance over both individual sensor techniques for the FW index only. MOA models outperformed the individual and SF models and yielded the best prediction accuracy for SW and MB indices. Data fusion modelling thus enhanced the accuracy of FW, SW and MB predictions, although the selection of the best data fusion approach is dependent on the nature of the dataset and the stability index to be assessed.

土壤团聚体稳定性 (AS) 是可持续农业和环境土地管理实践的关键土壤物理特性。然而，其传统的实验室方法繁琐且耗时，从而限制了高密度采样和大规模估计。因此，融合可见近红外 (vis-NIR) 和中红外 (MIR) 光谱以增强三个 AS 指数的预测的潜力，即快速润湿 (FW)、慢速润湿 (SW) 和机械击穿 (MB) ) 在一些比利时 Retisol、Cambisol 和 Luvisol 表土上进行了评估。偏最小二乘回归 (PLSR) 用于使用可见近红外光谱、中红外光谱和两种光谱的融合 (SF) 为三个 AS 指数建立校准模型。另一种数据融合方法（即模型输出平均，MOA）包括使用四种平均算法。结果表明，MIR 模型在所有三个指标上都优于 vis-NIR 模型。SF 方法仅针对 FW 指数显示出优于两种单独传感器技术的预测性能。MOA 模型优于个体模型和 SF 模型，并且对 SW 和 MB 指数产生了最佳预测精度。因此，数据融合建模提高了 FW、SW 和 MB 预测的准确性，尽管最佳数据融合方法的选择取决于数据集的性质和要评估的稳定性指数。MOA 模型优于个体模型和 SF 模型，并且对 SW 和 MB 指数产生了最佳预测精度。因此，数据融合建模提高了 FW、SW 和 MB 预测的准确性，尽管最佳数据融合方法的选择取决于数据集的性质和要评估的稳定性指数。MOA 模型优于个体模型和 SF 模型，并且对 SW 和 MB 指数产生了最佳预测精度。因此，数据融合建模提高了 FW、SW 和 MB 预测的准确性，尽管最佳数据融合方法的选择取决于数据集的性质和要评估的稳定性指数。