Data fusion of Vis-NIR and pXRF with machine learning for predicting andic properties in volcanic soils

Volcanic soils are characterized by andic properties such as organic carbon (OC), bulk density (Bd), phosphate retention (P_ret), and the sum of ammonium oxalate-extractable aluminum and half iron (Al_o + 0.5Fe_o), and play an important role in agricultural production, global carbon cycling, and ecological functions. However, conventional determination of andic properties relies on destructive, labor-intensive, and time-consuming wet chemistry analyses. Soil spectroscopic techniques such as visible and near-infrared (Vis-NIR) spectroscopy and portable X-ray fluorescence (pXRF) provide rapid and non-destructive alternatives. Previous studies have shown that soil properties can be well predicted by integrating spectroscopic data with machine learning algorithms such as partial least squares regression (PLSR) and Cubist. However, no study has investigated the data fusion of Vis-NIR and pXRF for predicting andic properties. Therefore, this study aimed to elucidate the relationships between andic properties and signals from Vis-NIR and pXRF, and to evaluate the accuracy of sensor-based models for predicting andic properties and soil classification. A total of 93 soil samples were collected from 24 pedons of volcanic soils (0–60 cm depth) in northern Taiwan, including Andisols and Inceptisols. Soil samples were measured by Vis-NIR and pXRF, and predictive models were developed using individual sensors and a data fusion approach calibrated with PLSR and Cubist algorithms. Laboratory analyses were conducted to quantify andic properties as reference values. Both Vis-NIR and pXRF signals demonstrated associations with andic properties. Data fusion of these two sensors markedly improved model performance compared with single-sensor approaches. In particular, the Vis-NIR + pXRF-based model calibrated with Cubist yielded good predictive performance for all andic properties, achieving R² and LCCC values higher than 0.90 for OC, P_ret, and Al_o + 0.5Fe_o, and R² = 0.83 and LCCC = 0.89 for Bd. Moreover, 23 out of the 24 studied pedons were correctly classified by this model. Integrating Vis-NIR and pXRF through data fusion provides an efficient approach for assessing andic properties, improving management and resource-use efficiency in volcanic soils and supporting sustainable smart agriculture. Further studies incorporating additional spectroscopic sensors may further broaden applicability across diverse soil types.