Abstract: Abstract: Salinization and alkalinization are two of important land degradation processes in flood area of the Yellow River in central China. A synthesized model for assessment of regional soil salinity was established based on multi-source data including soil salinity, topographical variable, the groundwater level and mineralization degree, vegetation and other factors to the soil salinization. A total of 101 soil columns were sampled from the study area using grid sampling method, and then analyzed for soil electrical conductivity (ECe) and other soil properties. Auxiliary data used in this study to interpret variability of soil salinization were Landsat 5 TM data, apparent electrical conconductivity (ECa) measured using an electromagnetic induction instrument (EM38), altitude derived from topographic map, the groundwater table and mineralization degree and soil pH. The spatial variability of soil salinity was assessed in Fengqiu County, Henan Province, China. Classification and regression tree was applied to obtain the relationships between ECe (0-120 cm) and the auxiliary data. The results showed that ECa accounted for the major proportion of model prediction from multi-source data in classification and regression tree model of total soil layer. Generally, ECaH (apparent soil electrical conductivity from EM38 horizontal mode) and spectral index (dvi: difference vegetation index, bi:soil index, int2: intentity, int1: intentity, ndvi: normalized difference vegetation index, si2: soil index and si1: soil index) were common variable for 0-60cm soil layer. For the 0-30 cm depth, plant index (ndvi and dvi), soil index (si1, si2 and bi) and intentity (int1 and int2) had the highest influence on the model prediction followed by ECa. Plant index (dvi) accounted of more than 50% for 0-60 cm soil layer used in the model. Meanwhile, for ≥60-120 cm, ECaV (apparent soil electrical conductivity from EM38 vertical mode) was the most important variable used in regression tree model. Validation of the predictive models at each depth resulted in determination coefficient (R2 values) ranging from 0.52 to 0.65. The root mean square error (RMSE) value ranged between 0.72 to 1.27 dS/m. The model for the evaluation of the soil salinity of 0-60 cm was better than that of soil layer of ≥60-120 cm. The mean of soil salinity varied from 1.26 to 1.61 dS/m from top to the bottom of soil profiles, and the soil salinity at the bottom was the highest in total soil profile, which indicated soil salinity accumulation at the bottom. ECe varied from 0.79 to 3.68 dS/m from top to the bottom of soil profiles. Coefficient of variation of soil salinity at each soil 1ayer was from 0.22 to 0.28 and exhibited the moderate spatial variability. The groundwater table varied from -14.0 m to -0.2 m. Coefficient of variation of groundwater table was 0.7 which exhibited the moderate spatial variability. The mean of groundwater electrical conductivity was 1.44 dS/m, which was similar to soil electrical conductivity. The mean of pH value varied from 8.88 to 9.28 and increased with increasing soil depth. Digital maps of ECa (horizontal and vertical modes) along with other environmental variables were used to predict the spatial distribution of ECe. The high values of soil salinity were mainly distributed in the northern and southern area along the Yellow river in the study area, especially the southeast region. The synthesized model based on multi-source data had high precision for assessment of regional soil salinity. Thus, the application of this technique provides a new method to improve soil salinization and soil quality in the flood area of the Yellow River in central China.