Open AccessApplication of Remote Sensing and Geo Statistical Analysis for Soil Salinity Monitoring in Tina Plain Area of Egypt
Author(s) -
Gehan A.H. Sallam,
Mohamed Embaby,
Mohamed Nower
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e9765.069520
Subject(s) - soil salinity , environmental science , salinity , remote sensing , hydrology (agriculture) , dryland salinity , soil water , agriculture , population , water resource management , soil science , soil fertility , geology , geography , soil biodiversity , geotechnical engineering , oceanography , demography , archaeology , sociology
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.