Soil electrical conductivity is highly correlated with salt content. Accurate soil conductivity monitoring helps to determine the degree of salinisation in regional soils, and it is of great significance to the prevention and control of regional salinisation, the sustainable development of agriculture, and the construction of ecological civilisation. In this study, indoor hyperspectral and conductivity measurements were performed on soil samples. For the purpose of determining the best hyperspectral parameters for predicting soil conductivity, the simplified spectral indices (nitrogen planar domain index, NPDI), were carried out by the two band optimisation algorithm. The most sensitive hyperspectral parameters of different hyperspectral data (original hyperspectral reflectance and the corresponding 5 mathematical transformations), were selected to establish the hyperspectral estimation model of soil conductivity, for the realisation of efficient monitoring of soil salinity information. The results showed that the correlation between NPDIs and soil conductivity was significant. With the transformation of the original data, optimised spectral indices were more sensitive to soil conductivity, and the absolute value of the correlation coefficient exceeded 0.80. Among them, the correlation coefficient of the (R_{2020 nm}+R_{1893 nm})/R_{1893 nm} band combination based on the 1.6 order differential transformation was the highest, reaching 0.888. The prediction model based on 1.6 order differential band optimisation was the accurate, and the prediction accuracy was R_pre²=0.84, RMSE_Pre=2.07 mS/cm, RPD=2.94, and AIC=158.11. Therefore, the appropriate mathematical transformation of hyperspectral data was beneficial to optimise the spectral index to better estimate soil conductivity, and further achieve high precision dynamic monitoring of soil salinisation.