Enhancing the Spatial Variability of Soil Salinity Indicators by Remote Sensing Indices and Geo-Statistical Approach

Soil salinization is considered limiting factor for crop production and land management for dry land in Sudan, its spatial variation is affected by different factors of soil properties, vegetation and environment hence its interaction formulate the planning for successful sustainable agriculture in salt affected soils. This study aims to evolve the spatial prediction of soil salinity indicators by integrated remote sensing indices and geo-statistical cokriging model. Soil samples were collected from 476 square kilometer area in salt affected area, the samples were analyzed following standard procedures for electrical conductivity, sodium adsorption ratio, hydrogen ions and saturation percentage. Information of vegetation status identified by Normalized Difference Vegetation Index (NDVI) and soil salinization by Salinity index and brightness index were used and utilized for prediction of the soil parameters variability by cokriging model. It was found that the method was resulted in high accuracy based on RMSE and enhances the soil spatial variability assessment and provides significant interaction of different variables and indices in the landscape.