Enhancing the Spatial Variability of Soil Salinity Indicators by Remote Sensing Indices and Geo-Statistical Approach
Received Date: Dec 11, 2017 / Accepted Date: Apr 04, 2018 / Published Date: Apr 10, 2018
Abstract
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.
Keywords: Soil salinization; Spatial prediction; Cokriging; NDVI; Salinity index; Brightness index
Citation: Babiker S, Abulgasim E, Hamid HS (2018) Enhancing the Spatial Variability of Soil Salinity Indicators by Remote Sensing Indices and Geo-Statistical Approach. J Earth Sci Clim Change 9: 462. Doi: 10.4172/2157-7617.1000462
Copyright: © 2018 Babiker S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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