Soil Surface Salinity Prediction Using ASTER Data: Comparing Statistical and Geostatistical Models uri icon

abstract

  • This study was conducted to evaluate the performance of univariate spatial (ordinarykriging- OK), hybrid/multivariate geostatistical methods (regression-kriging- RK, Co-kriging- CK) withmultivariate linear regression (MLR) in incorporation with ASTER data in order to predict the spatialvariability of surface soil salinity in an arid area in northern Iran. The primary attributes were obtainedfrom grid soil sampling with nested-systematic pattern of 169 samples and the secondary informationextracted from spectral data of ASTER satellite images. The principal component analysis, NDVI andsome suitable ratioing bands were applied to generate new arithmetic bands. According to validationbased RMSE and ME calculated by a validation data set, the predictions for soil salinity were foundto be the best and varied in the following order: RK ASTERmultivariate > REG ASTERmultivariate > Co-krigingASTER> kriging. Overall, this comparative study demonstrated that RK approach was a better predicatorthan other selected methods to predict spatial variability of soil salinity. The overall results confirmedthat using ancillary variables such as remotely sensed data, the accuracy of spatial prediction canfurther improved

publication date

  • 2010