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- W1470496170 abstract "Abstract In arid and semi-arid regions of the world, soil salinization is one of the most crucial environmental problems due to its adverse effects on agricultural productivity and sustainable development. Unconscious irrigation and old irrigation techniques extremely damage fertile land and accelerate water logging and salt accumulation in soil. In addition, some natural factors also exacerbate soil salinity. Therefore, it is an important concern to predict and monitor soil salinity in order to take protective measures against further deterioration of the soil. In this study, emphasis is given to the techniques used for predicting and monitoring soil salinity throughout the different regions of the world. Examples of soil salinity mapping will also be referred to alert especially the soil scientists and farmers. Traditionally, soil salinity prediction and monitoring are often carried out with intensive field work and sampling. Most previous studies have focused on differentiating salinized and non-salinized soil qualitatively by analysing the salinity distribution and monitoring its dynamics. Remote Sensing (RS), Geographical Information Systems (GIS) and modelling have recently outperformed the traditional methods. Mapping has progressed from qualitative to quantitative mapping via multiple-temporal and multi-spectral information obtained from RS observations. In zones of thickly vegetated soils, using vegetation indices in the evaluation, mapping of soil salinity have started to yield promising results; whereas indices have become the appropriate method in the case of exposed soils or soils with low scattered vegetation. This study reviews the application of various satellite images to delineate soil salinity maps by conducting either salinity indices generated by different spectral bands or vegetation indices." @default.
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- W1470496170 date "2015-01-01" @default.
- W1470496170 modified "2023-10-01" @default.
- W1470496170 title "Soil Salinity Prediction, Monitoring and Mapping Using Modern Technologies" @default.
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- W1470496170 doi "https://doi.org/10.1016/j.proeps.2015.08.062" @default.
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