Detecting Degraded Arable Land on the Basis of Remote Sensing Big Data Analysis

The recent development of precision farming technologies has necessitated the creation of the maps of sustainable intrafield heterogeneity of soil fertility on the basis of the analysis of remote sensing big data. To create such maps, thousands of remote sensing images for 35 years have been analyzed for each element of the earth surface to identify the areas of reduced fertility. The conducted analysis allows us to detect the areas of decreased fertility. In turn, such areas are indicative of the negative soil properties. Some of the negative properties are of the initial natural origin, and others appeared as a result of the development of degradation processes. It is possible to separate the zones of reduced fertility emerged due to the natural factors from those specified by the anthropogenic impacts on the basis of the analysis of morphometric parameters of surface topography coupled with technology of the spectral neighborhood of soil line. A combination of various methods of the analysis of big remote sensing data and landscape parameters has made it possible to create the maps of degraded lands with an accuracy sufficient for precision farming technologies. This study presents the results of identification of three natural and four anthropogenic factors of fertility decrease on arable land. The areas under the impact of particular degradation factors are identified with the help of remote sensing data. They have been verified by the ground soil survey. The results obtained are the direct consequence of the remote sensing big data analysis development. A soil cartographer, as well as a farmer, has now received a new method for detecting distribution patterns of degradation factors.

Automated summary

The recent development of precision farming technologies has led to the creation of sustainable intrafield heterogeneity maps of soil fertility based on the analysis of remote sensing big data. By analyzing areas of reduced fertility, negative soil properties can be identified, distinguishing between natural and anthropogenic factors. Through the combination of various methods of big remote sensing data analysis and landscape parameters, accurate maps of degraded lands have been successfully created.