Arid soil is common worldwide and has unique properties that often limit agronomic productivity, specifically, salinity expressed as soluble salts and large amounts of calcium carbonate and gypsum. Currently, the most common methods for evaluating these properties in soil are laboratory-based techniques such as titration, gasometry and electrical conductivity. In this research, we used two proximal sensors (portable X-ray fluorescence (PXRF) and visible near-infrared diffuse reflectance spectroscopy (Vis-NIR DRS)) to scan a diverse set (n=116) of samples from arid soil in Spain. Then, samples were processed by standard laboratory procedures and the two datasets were compared with advanced statistical techniques. The latter included penalized spline regression (PSR), support vector regression (SVR) and random forest (RF) analysis, which were applied to Vis-NIR DRS data, PXRF data and PXRF and Vis-NIR DRS data, respectively. Independent validation (30% of the data) of the calibration equations showed that PSR+RF predicted gypsum with a ratio of performance to interquartile distance (RPIQ) of 5.90 and residual prediction deviation (RPD) of 4.60, electrical conductivity (1:5 soil:water) with RPIQ of 3.14 and RPD of 2.10, Ca content with RPIQ of 2.92 and RPD of 2.07 and calcium carbonate equivalent with RPIQ of 2.13 and RPD of 1.74. The combined PXRF and Vis-NIR DRS approach was superior to those that use data from a single proximal sensor, enabling the prediction of several properties from two simple, rapid, non-destructive scans. Highlights: Two combined proximal sensors were used to improve assessment of arid soil properties. Combining sensors improved quantitative accuracy relative to either sensor alone. Prediction accuracy: gypsum > electrical conductivity and Ca content > calcium carbonate equivalent. Combined proximal sensor platforms outperform single sensors for quantifying arid soil properties.