The present research attempts to establish a soil salinization monitoring model through a combination of remote and near sensing technologies. An electromagnetic induction instrument (EM38) was used to measure the electronic conductivity of soil samples collected from the delta oasis between the Weigan River and the Kuqa River in the north rim of the Tarim basin. Hyperspectral images were obtained via ASD Field Specpro FR and were transformed via 11 different approaches including root mean squares, logarithm, inversion, inversion-logarithm, continuum removal, and first order differentiation, etc. After the transformation, the obtained soil spectra that correlate well with soil electronic conductivity as measured by the EM38 were used to calculate five salinity indexes (salinity index 1, 2, and 3, normalized differential salinity index, and brightness index). Our analyses suggest that the salinity index 2 obtained via first order differentiation transformation of the spectra with the wavelengths of 456, 686 and 1 373 nm generated the highest correlation with salinity information derived via the EM38. By incorporating near sensing information (soil electronic conductivity information obtained via EM38), the current research provides a potentially more accurate approach to monitoring and predicting soil salinization in the future.