Predicting Long-term Dynamics of Soil Salinity and Sodicity on a Global Scale

Published: 26 November 2020| Version 1 | DOI: 10.17632/v9mgbmtnf2.1
Contributors:
Amirhossein Hassani,
Adisa Azapagic,
Nima Shokri

Description

This dataset globally (excluding frigid/polar zones) quantifies the different facets of variability in surface soil (0 – 30 cm) salinity and sodicity for the period between 1980 and 2018. This is realised by developing 4-D predictive models of Electrical Conductivity of saturated soil Extract (ECe) and soil Exchangeable Sodium Percentage (ESP) as indicators of soil salinity and sodicity. These machine learning-based models make predictions for ECe and ESP at different times, locations, and depths and by extracting meaningful statistics form those predictions, different facets of variability in the surface soil salinity and sodicity are quantified. The dataset includes 10 maps documenting different aspects of soil salinity and sodicity variations, and auxiliary data required for generation of those maps. Users are referred to the corresponding "READ_ME" file for more information about this dataset.

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Institutions

The University of Manchester

Categories

Machine Learning Algorithm, Soil Salinity

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