Soil Salinity Monitoring and Quantification Using Modern Techniques

• Autorzy: Al-Khuzaie Marwah M. , Abdul Maulud Khairul Nizam , Mohd Taib Aizat

Identyfikatory

DOI

10.12911/22998993/152542

• Języki publikacji: EN

Abstrakty

EN

Along with sea-level rise, one of the most detrimental effects of climate change, is salinity leakage, which significantly affects agricultural activities throughout most of the world. This occurrence is becoming increasingly dangerous. The purpose of this study was to use Geographical Information Systems (GIS) to assess the current situation of agricultural lands in the province of Al-Diwaniyah, by employing GIS to document the salt-affected sites and arrive at the most important criteria affecting those lands as well as 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 the available data by extrapolating and analyzing remote sensing images using salt equations to analyze the Landsat 8 satellite images, after which these data were subjected to spatial statistical treatment in ArcGIS software. Moreover, 20 samples were taken from ground sampling points and subjected to laboratory analysis to compare and document the 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 was highlighted. The best-performing indicators were used to build the MLR model, which was then used to anticipate soil salinity. The 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.

Słowa kluczowe

EN

salinity; GIS; soil salinity indicator; regression model

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2022
• Tom: Vol. 23, nr 11
• Strony: 57--67
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Al-Khuzaie Marwah M.

• Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
• Civil Engineering Department, College of Engineering, University of Al-Qadisiyah, Al-Qadisiyah, Iraq

• https://orcid.org/0000-0002-7834-9566

autor

Abdul Maulud Khairul Nizam

• Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
• Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia

• https://orcid.org/0000-0002-9215-2778

autor

Mohd Taib Aizat

• Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

• https://orcid.org/0000-0002-0307-3016

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).