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[Display omitted] • SNV pre-processing amplified the absorption characteristics of the spectrum. • Two variable selection algorithms were used to extract spectral variables. • OBCA improved the correlation of spectra with soil salinity vs. CARS. • RF has better stability in predicting soil salinity than PLSR. Hyperspectral remote sensing by unmanned aerial vehicle (UAV) is an important technical tool for rapid, accurate, and real-time monitoring of soil salinity in arid zone agroecosystems. However, the key to effective soil salinity (electrical conductivity, EC) prediction by UAV visible and near-infrared (Vis-NIR) spectroscopy depends on the selection of effective features selection techniques and robust prediction characteristics algorithms. Therefore, in this study, two advanced feature selection methods and two commonly used modeling methods were applied to predict and characterize the spatial patterns of soil salinity (EC). The aim of this study was to explore the predictive performance of different feature band selection methods and to identify a robust soil salinity mapping strategy. The results demonstrated that standard normal variate (SNV) pre-processing broadened the absorption characteristics of the spectrum. Compared with competitive adaptive reweighted sampling (CARS), the optimal band combination algorithm (OBCA) strengthened the correlation with soil salinity and had a higher variable importance in the modeling. Random forest (RF) was more stable in mapping the spatial pattern of surface soil salinity compared to the partial least squares regression model (PLSR). Our results confirm the effectiveness of OBCA and RF in the developing UAV remote sensing models for surface soil salinity estimation and mapping. [ABSTRACT FROM AUTHOR]