Only using soil spectrum to model soil salinity is not enough to meet the actual demands because of the complicated soil context. As a remotely sensed indicator, the vegetation type and its growing condition can provide a spatial overview of salinity distribution. Based on the synergistic relationship between soil salinity and vegetation in arid land, this paper tries to combine the spectrum of soil and vegetation to quantitatively estimate the salt content with the help of the concept of two-dimensional feature space. After the analysis of scatter diagram, the soil salinity detecting model was constructed to improve reasoning precision. However, because the impact of soil reflectance on the quantification of vegetation parameters under the individual pixel, the Normalized Difference Vegetation Index (NDVI) was difficult to accurately obtain sparse vegetation cover in arid areas. Therefore, in order to avoid the limitations of NDVI, the Combined Vegetation Indicative Factor(CVIF)was created and supported by Linear Spectral Unmixing Model (LSUM). Then, the study constructed the feature space based on the CVIF and salinity index (SI) and analyzed the response relationship between soil salinity and the trend of scattered points. Finally, a new and operational model termed Salinity Inference Model (SID) was developed. The results showed that the CVIF (R2>0.84, RMSE=3.92) performed better than NDVI(R2>0.66, RMSE=13.77), which means the CVIF was more appropriate for analyzing variations in vegetation cover (particularly halophytes) than NDVI in the study area. The SID was then compared to the Combined Cpectral Response Index (COSRI)(NDVI-based) from field measurements with respect to the soil salt content. The results indicated that the SID values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SID (R2>0.86, RMSE<6.86) compared to COSRI (R2=0.71, RMSE=16.21). These results suggested that the feature space related to biophysical properties combined with CVIF and SI can effectively provide information on soil salinity.