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Soil salinization is one of the most predominant environmental hazards responsible for agricultural land degradation,especially in the arid and semi-arid regions.An accurate spatial prediction and modeling of soil salinity in agricultural land are so important for farmers and deci-sion-makers to develop the appropriate mechanisms to prevent the loss of fertile soil and increase crop production.El Outaya plain is marked by soil salinity increases due to the excessive use of poor groundwater quality for irriga-tion.This study aims to compare the performance of simple kriging,cokriging(SCOK),multilayer perceptron neural networks(MLP-NN),and support vector machines(SVM)in the prediction of topsoil and subsoil salinity.The field covariates including geochemical properties of irrigation groundwater and physical properties of soil and environ-mental covariates including digital elevation model and remote sensing derivatives were used as input candidates to SCOK,MLP-NN,and SVM.The optimal input combina-tion was determined using multiple linear stepwise regression(MLSR).The results revealed that the SCOK using field covariates including water electrical conduc-tivity(ECw)and sand percentage(sand％),and environ-mental covariates including land surface temperature(LST),topographic wetness index(TWI),and elevation could significantly increase the accuracy of soil salinity spatial prediction.The comparison of the prediction accu-racy of the different modeling techniques using the Taylor diagram indicated that MLP-NN using LST,TWI,and elevation as inputs were more accurate in predicting the topsoil salinity[ECs(TS)]with a mean absolute error(MAE)of 0.43,root mean square error(RMSE)of 0.6 and correlation coefficient of 0.946.MLP-NN using ECw and sand％as inputs were more accurate in predicting the subsoil salinity[ECs(SS)]with MAE of 0.38,RMSE of 0.6,and R of 0.968.