Application of Remote Sensing and Geo-Statistical Analysis for Soil Salinity Monitoring in Tina Plain Area of Egypt 
Gehan A.H. Sallam1, Mohamed Embaby2, Mohamed Nower3
1Gehan A.H. Sallam*, Associate Professor, Drainage Research Institute (DRI), National Water Research Center, Delta Barrages, Cairo Egypt.
2Mohamed Embaby, Researcher, National water research center, Cairo, Egypt.
3Mohamed Nower, Associate Researcher, National water research center, Cairo, Egypt.
Manuscript received on June 01, 2020. | Revised Manuscript received on June 08, 2020. | Manuscript published on June 30, 2020. | PP: 851-859 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9765069520/2020©BEIESP | DOI: 10.35940/ijeat.E9765.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In Egypt require to improve agricultural production to save food demand with increase population. Soil salinity is a significant attractive problem for agriculture in irrigated areas. As a result, monitoring of soil salinity is required for salinization control. One of the greatest challenges faced by Egypt’s decision-makers is the acquisition of reliable integrated soil salinity information to introduce and recommendation of a simple approach to formulating a guideline for stakeholders. Saline affected areas are vast, making it extremely difficult to examine soil quality with field and laboratory data to provide reliable approach to forecast and monitor soil salinity. Objective of this paper is to research remote sensing and ArcGIS tools by Geo-Statistical Analyst techniques to map saline-sodic heavy clay soils of the Tina plain area in Egypt. Tina Plain characterizes a serious area for potential development of agricultural land in Egypt. Satellite images were downloaded by Landsat 8, Sentinel-2A, Sentinel-1, and Synthetic Aperture Radar C-band data were wont to map soil salinity within the area. By coefficient of correlation method were preformed, evaluated and compared of the Three models. The results revealed that the Sentinel-2A optical imaging satellite yielded the very best prediction performance. ArcGIS Geo-Statistical Analyst was also successfully wont to predict and map the saline-sodic heavy clay soils with a mistake percentage of about 4.28%, which is taken into account as a minor error. generally, the study confirms that the Remote Sensing and ArcGIS Geo-Statistical Analyst are often considered by researchers and decision-makers as a credible, cost-effective, and time-controlled techniques to work out and predict the extension of soil salinity.
Keywords: Soil Salinity; Remote Sensing; Geo-statistical Analyst; ArcGIS.