Fractional Order Differentiation Preprocessing Based Fusion of Vis– <scp>NIR</scp> and <scp>pXRF</scp> : A New Strategy for Precise Monitoring of Low Concentration Heavy Metals in Arid Agricultural Soils

ABSTRACT Soil contamination by heavy metals has become a significant issue threatening the ecological security of global agriculture, particularly in arid regions, where accurate monitoring of low‐concentration heavy metals remains a technical challenge. This study proposes a proximal sensing method based on the fusion of visible–near infrared (Vis–NIR) spectroscopy and portable X‐ray fluorescence (pXRF) sensors, aiming to address the limitations of traditional single sensors in predicting low‐concentration heavy metals in arid farmland areas. Using 116 soil samples from Qapqal County, this study screened 225 preprocessing combinations and identified Vis–NIR with 1.75‐order differentiation as the most effective approach for predicting heavy metals. It achieved high prediction accuracy with R 2 values of 0.71 (As), 0.68 (Pb), 0.64 (Cd), and 0.50 (Cu), clearly outperforming pXRF. The best performance of pXRF was achieved with standard normal variate transformation preprocessing, yet its accuracy remained considerably low—for instance, the R 2 for Cd was only 0.29. Moreover, its accuracy further decreased under fractional‐order differentiation, indicating that fractional‐order differentiation preprocessing is unsuitable for pXRF. The model accuracy was significantly improved by employing differentiated spectral preprocessing combinations, particularly for As, with an R 2 of 0.72, LCCC of 0.76, and RPIQ of 3.27. Furthermore, the analysis of critical characteristic bands revealed that the characteristic bands of As, Pb, and Cu are mainly concentrated in the low‐energy region (5–16 keV) of pXRF, providing an essential spectral basis for heavy metal feature extraction. This study innovatively proposes differentiated preprocessing strategies and highlights the critical role of pXRF low‐energy region spectra in heavy metal prediction. The research provides a scientific basis for heavy metal monitoring and ecological risk assessment of farmland in arid areas, which has significant practical value, contributing to improved environmental quality and the safety of agricultural products.