[Objective] To explore the best technical route for salt salinity estimation based on spectral indices in order to provide theoretical basis and technical reference for the quantitative calculation and rapid remote sensing monitoring of soil salinity in the study area.[Methods] Taking Kenli County of Shandong Province as the study area, samples were collected in the field, the content of soil salt and its main ions(Cl^-,Na⁺,Ca²⁺)were measured, and the hyperspectra were obtained. Two different methods were used to select the sensitive spectral indices. The first one was to select the sensitive bands of salt and its major ions and then to build five spectral indices. The second one was to combine any two bands and to construct the five spectral indices, and the sensitive spectral indices were then filtered. The random forest(RF) method was used to build quantitative hyperspectral models of soil salinity and ions contents.[Results] The RF model of brightness spectral indices(1 750, 1 620 nm)exhibited the best precision, thus it was the best estimation model of soil salinity in the study area, and the brightness spectral index was the best spectral index. The characteristic spectral range based on the second method covered the selected sensitive bands based on the first method, thus was more conducive to the spectral characteristics analysis. Meanwhile, the salt prediction model built based on the second method was better than that on the first one. Therefore, the best technical route was to construct the spectral indices by combination of any two bands firstly, then to select the sensitive spectral index of soil salinity and its main ions by correlation analysis, finally to build the RF model.[Conclusion] The technical route is suitable for the extraction of soil salinization information in the Yellow River delta.