Digital mapping of indicators that determine the sorption properties of soils in relation to pollutants, according to remote sensing data of the Earth using machine learning

Abstract

According to the data of remote sensing of the Earth, the accuracy of the spatial prediction of soil indicators determining sorption properties in relation to pollutants was compared. To build spatial maps of changes in soil properties, machine learning methods based on support vector regression models (SVMr) and random forest (RF) were used. It was shown that the methods of machine modeling using remote sensing can be successfully used for spatial prediction of the content of particle size fractions, organic matter, pH and the capacity of cation exchange of soils in small areas. It is shown that the spatial prediction of the content of silt fraction is best modeled using the RF algorithm, while the other properties of soils that can determine their sorption potential in relation to pollutants are better modeled using the SVMr method. In general, both machine learning methods have similar spatial prediction results.

Keywords

• sorption properties of soil
• spatial prediction
• remote sensing data of the earth
• machine learning methods