Detection of Soil Salinity for Bare and Cultivated Lands Using Landsat ETM+ Imagery Data: A Case Study from El-Beheira Governorate, Egypt | ||
| Alexandria Science Exchange Journal | ||
| Article 27, Volume 38, July-September - Serial Number 3, September 2017, Pages 642-653 PDF (1.65 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/asejaiqjsae.2017.4055 | ||
| Authors | ||
| Emad Fawzy Abdelaty; Emad Farouk Aboukila | ||
| Assistant Professor, Department of Natural Resources and Agricultural Engineering, Faculty of Agriculture, Damanhour University, Damanhour, Egypt | ||
| Abstract | ||
| environmental universal issues due to its adverse effects on agricultural productivity and sustainable development. Remote sensing is an important tool for investigating soil characteristics such as soil salinity. In saline soils, the spectral reflectance of salt at the surface or of vegetation execution that was adversely influenced by salt varies with different salinity levels. So, many salinity and vegetation indices have been developed and used. This study used ground data and Landsat Enhanced Thematic Mapper Plus (ETM+) satellite images (visible and near-infrared reflectance) to compare between eleven spectral indices, which encompassed soil salinity and vegetation indices, to determine the best index to the estimations of soil salinity for bare and cultivated soil. Soil samples were gathered from two locations in El-Beheira governorate in Egypt; 24 samples from Wadi-El-Natroun (bare soil) and 22 samples from El-Bostan (cultivated soil) and the soil samples locations were overlaid on ETM+ satellite image to extract the exact index values. The electrical conductivity (EC) measured in saturated soil-paste extract. Among those spectral indices, SI3 showed the highest correlation coefficient with EC (R2 = 0.77) according to linear regression analysis and S6 according to Polynomial regression (R2 = 0.83), followed by S3 for bare soil. NDVI and SAVI get the best result for assessing the soil salinity of cultivated soil (R2 = 0.83 and 0.76) according to Polynomial and linear regression, respectively, followed by RVI. | ||
| Keywords | ||
| Soil salinity; salinity indices; Vegetation Indices; Remote Sensing | ||
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