Soil Salinity Monitoring and Quantification Using Modern Techniques
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Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
Civil Engineering Department, College of Engineering, University of Al-Qadisiyah, Al-Qadisiyah, Iraq
Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia
Khairul Nizam Abdul Maulud   

Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
Along with sea-level rise, one of the most detrimental effects of climate change, is salinity leakage, it significantly affects agricultural activities throughout most of the world. This occurrence is becoming increasingly dangerous. The purpose of this study was to use Geographic Information Systems (GIS) to assess the current situation of agricultural lands in the province of Al-Diwaniyah, by using GIS to document salt-affected sites and arrive at the most important criteria affecting those lands and build an application model for suitability to clarify the affected sites and come up with paper and digital maps. To accomplish this, the study relied on available data by extrapolating and analyzing remote sensing images using salt equations to analyze Landsat 8 satellite images, after which these data were subjected to spatial statistical treatment in ArcGIS software, and on the other hand, 20 sample points were taken from ground ones and subjected to laboratory analysis to compare and document results. The research resulted in the creation of an up-to-date database for the locations of salt ratio growth or decrease in the province of Al-Diwaniyah, which can be relied on, starting from and expanding in the future. Land maps, both paper and digital, have been created and can be used and inferred. The findings demonstrated the model's ability to steadily discriminate among all salinity groups while maintaining consistency with the ground truth data. Each of the four major salinity categories is highlighted. The best-performing indicators were used to build the MLR model, which was then used to anticipate soil salinity. Salt levels may be determined by the MLR combining NDVI and SI-5 with a high correlation value (R2 = 75.29%). Finally, it is shown that by combining spectral indicators with field measurements, it is possible to chart and forecast soil salinity on a large scale.