Abstract: Abstract: Fast acquisition of the soil salt content, characteristics, and spatial distributing are the objective needs of saline soil management and utilization. This paper focused on the saline soil on the Yellow River Delta, and took Kenli County as an example. Based on the multi-spectral remote sensing image of Landsat 8 OLI, the traditional vegetation index (VI) was extended by adding the short-wave infrared band, and the modified vegetation index (MVI) was put forward. Then, based on the corresponding VI and MVI, using multivariate stepwise regression (MLR), a back propagation neural network (BPNN), and the support vector machine (SVM) method respectively, the remote sensing inversion models of soil salinity were built, validated, and compared. Finally, the spatial distribution of soil salinity was analyzed using the best model in the study area. The results indicated that the correlation between the vegetation indices and soil salinity was heightened and the multicollinearity between vegetation indices was greatly reduced by extending the traditional vegetation index. Extended normalized difference vegetation index(ENDVI) and extended ratio vegetation index (ERVI) which were added band 7 were selected as the modified vegetation index(MVI). Using MLR, a BPNN and the SVM method, the precision of the models based on the MVI was improved compared to the VI with the calibration coefficient of determination (R2) raised between 0.05 and 0.11, and the calibration root mean squares error (RMSE) reduced between 0.09 and 0.55, the validation R2 raised between 0.04 and 0.10, the validation RMSE reduced between 0.13 and 0.73, and the validation relative prediction deviation (RPD) raised between 0.25 and 0.34. The models based on MVI obtained generally good performance with the validation RPD greater than 2.00. The main reasons improved the model precision were that the band 7 on Landsat 8 OLI had more information and the MVI including band 7 could more protrude the difference in vegetation coverage and production status. Comparing the three modeling methods, the SVM achieved the highest accuracy, the second was the BPNN, and the MLR analysis resulted in the lowest accuracy. With the calibration R2 and RMSE of 0.75 and 3.48, the validation R2, RMSE and RPD of 0.78, 3.02 and 2.56, the SVM model of soil salinity based on MVI was the best and obtained very high accuracy and reliability for remote sensing inversion of soil salinity content. The spatial distribution of soil salinity content in the study area was analyzed based on the best model. The statistical information of the inversed soil salinity was very close to the measured value of soil samples, the soil salinity content in the study area was very high generally, the area that belonged to severe saline soil and solonchak accounted for 77.91%, and the spatial distribution of soil salinization showed that the soil salinity content was gradually increased from the southwest agriculture region to the northeast coastal region, which was consistent with the field survey and geostatistical analysis. Therefore, the experiment indicated that the vegetation index was modified by introducing the band 7 based on Landsat 8 OLI, and the SVM model of soil salinity was built, which could obtain better inversion result of soil salinity spatial distribution.