The development of new technologies for use in field soil survey has produced powerful new quantitative tools for assessing soil physicochemical properties in-situ. One such technology, portable X-ray fluorescence (PXRF) spectrometry, has shown considerable promise in evaluating elemental concentrations in soils for a wide variety of applications. Less research is available on how PXRF can be applied to quantify soil physical properties (e.g. soil texture). This study evaluated the feasibility of predicting soil clay and sand contents from PXRF data on 584 soil samples collected from highly diverse regions of Louisiana and northeastern New Mexico (Capulin Volcano National Monument), USA. An Innov-X Delta Premium PXRF was used to sequentially scan soil samples under both field and laboratory conditions and assess 15 elements (K, Ca, Ti, Cr, Mn, Fe, Co, Cu, Zn, As, Rb, Sr, Zr, Ba, and Pb). Elemental concentrations were then related to soil textural data processed through traditional laboratory methods with multiple linear regression. Among all elements evaluated, Fe and Rb showed particular significance for soil textural prediction. The regression models for sand and clay contents of both study sites were strongly correlated to soil textural separates with PXRF-soil texture R2 of 0.862, 0.959, 0.892, and 0.780 for Louisiana sand and clay, and Capulin sand and clay, respectively. Independent validation sub-datasets confirmed that PXRF readings can be used to estimate soil textural parameters with high R2 values of 0.854, 0.682, 0.975, 0.891, 0.875, and 0.876, and low RMSE values of 5.53%, 5.92%, 2.68%, 6.26%, 5.43%, and 2.66%, for Louisiana sand, silt, and clay and Capulin sand, silt, and clay, respectively. The RMSE values of clay are substantially lower than those reported in previous studies using other proximal sensing techniques. While regional differences may require localized standardization of the PXRF with a unique combination of elements germane to the study area, PXRF shows considerable promise as a technique for rapidly assessing soil textural separates in-situ.
- Proximal soil sensors
- Soil texture
- X-ray fluorescence spectrometry