Soil salinity prediction using Machine Learning and Sentinel-2 Remote Sensing Data in Hyper-Arid areas

We are experiencing a considerable increase in soil salinity as a result of the influence of climate change or environmental contamination produced by excessive industry and agriculture. To be able to cope with this issue, reliable and up-to-date soil salinity measurements are required. The use of remote sensing data allows for faster and more efficient soil salinity mapping. This paper investigates several Machine Learning approaches and modeling methodologies for predicting soil salinity in hyper-arid environments using Sentinel-2 satellite imag-ery. Thus, 393 soil samples collected and used for modeling and testing in the study area, United Arab Emirates. Also, the paper benefits from open-source data and programs, such as Google Earth Engine and Weka. Different modeling strategies have been applied over the data. The results of the modeling show a strong correlation (0.84) with the test results. This study also shows interesting findings that will be examined further in future studies at other sites. As machine learning methods are evolving on a daily basis, new approaches needs to be considered in future studies for the demands of more precise modeling and mapping of soil salinity.