Soil salinization is an important factor that affects crop and vegetation growth condition and can result in environmental impacts with considerable economic consequences. Therefore, it is necessary to determine an effective method to monitor spatiotemporal salinity distribution. We used MOD13A1 time-series NDVI data to determine the vegetation phenology, including start of season (SOS), end of season (EOS), length of season (LEN), etc., and calculated several vegetation, salinity, terrain, and drought indexes, and spatial models. These were used as input parameters for the BP-ANN model. Meanwhile, we predict the soil salinity through vegetation and geomorphological partitioning, which described the correlations between vegetation or geomorphic type and salinization. The main conclusions are as follows: salinity is influenced by many factors, and many of them show non-linear relationships between phenological indicators and salinization, so we utilized artificial neural networks to predict soil salinity than mathematical equations; through a combination of phenology parameters, the precision of inversion salinity R² improved from 0.68 (no phenologcial indicators were included) to 0.79 (phenological indicators were included). However, additional auxiliary data to predict soil salinity, such as terrain, image, and soil moisture parameters should also be included. After the classification of the vegetation, the inversion precision improved obviously, where R² increased to 0.88. Phenological characters, such as large seasonal integrals (LSIs) and small seasonal integrals (SSIs) are good indicators to represent soil salinity. After geomorphological partitioning, R² increased to 0.85, indicating that it could be a good salinity predictor, but the ability of comprehensive inversion was lower than vegetation type partitioning. In farmland, the salinity level was low. The low, intermediate, and high salinization was 53.42, 13.71, and 32.87% respectively. Generally, salinization was higher at lower altitudes, and the salinity level was affected by terrain and geomorphological factors. The above conclusions indicate an effective method for the inversion of salinization levels that combines phenology and other parameters for comprehensively determining the effect of phenological information on salinity monitoring ability in data mining. The inversion of soil salinity is enhanced by the inclusion of phenological parameters.