This paper aims to investigate the feasibility combination of X-ray fluorescence (XRF), near-infrared (NIR), and mid-infrared (MIR) sensors for the detection of seven key monitoring elements in the soil. Two strategies for data fusion were adopted: (i) the XRF characteristic bands were fused with all spectral data of NIR and MIR separately, and (ii) the XRF characteristic bands were fused with the characteristic bands of NIR and MIR, respectively. Also, different feature extraction methods were compared. The best feature extraction methods for XRF-NIR and XRF-MIR models were principal component analysis (PCA) and successive projections algorithm (SPA). The modeling results showed that strategy (ii) showed better performance, and the XRF-MIR model provided more accurate results than the XRF-NIR model. Both XRF-MIR and XRF-NIR methods improved the accuracy of As, Cr, Cu, Ni, and Zn. The XRF-MIR obtained the best predictions, and the determination coefficients (R²) were 0.93, 0.98, 0.98, 0.95, and 0.98. For Pb and Cd, the measurement could be obtained by XRF alone, and the corresponding R² were both 0.98. The results confirmed that sensor fusion can effectively improve the accuracy of the spectrometer in detecting metal elements in the soil.

本文旨在研究X射线荧光（XRF）、近红外（NIR）和中红外（MIR）传感器组合用于检测土壤中七种关键监测元素的可行性。采用两种数据融合策略：(i) XRF 特征波段分别与 NIR 和 MIR 的所有光谱数据融合，(ii) XRF 特征波段分别与 NIR 和 MIR 的特征波段融合。此外，还比较了不同的特征提取方法。XRF-NIR 和 XRF-MIR 模型的最佳特征提取方法是主成分分析 (PCA) 和连续投影算法 (SPA)。建模结果表明策略（ii）表现出更好的性能，并且 XRF-MIR 模型比 XRF-NIR 模型提供了更准确的结果。XRF-MIR 和 XRF-NIR 方法均提高了 As、Cr、Cu、Ni 和 Zn 的准确度。XRF-MIR 获得了最佳预测，确定系数 (R² ) 为 0.93、0.98、0.98、0.95 和 0.98。对于Pb和Cd，仅通过XRF即可测得，对应的R ²均为0.98。结果证实传感器融合可以有效提高光谱仪检测土壤中金属元素的准确度。