Digital Soil Mapping for Precision Agriculture

Dear Colleagues,

Precision agriculture (PA, or precision farming) seeks correct and precise localized information on soil properties as a support for decision making on site-specific field operations. Digital soil mapping (DSM, also known as predictive mapping) aims at the quantitative creation of geographically-referenced soil databases and maps at various scales using soil and environmental data. This rather new branch of soil science has adopted or developed a wide range of tools to acquire, combine, and process information on soil and environment (relief parameters, vegetation indices, proximal and remote sensing data), create, calibrate and validate spatial prediction models, produce maps and georeferenced databases of soil properties tailored to user demands, and assess the accuracy of predicted data. All these tools can be well exploited in precision agriculture. This Special Issue aims to show the suitability, application and implementation of DSM tools and principles in PA at various stages of the process. The papers could cover, particularly, but not exclusively, the following topics applied in PA: Optimization of soil sampling designs, exploitation of proximal and remote sensing data (ground sensors, unmanned aerial vehicles, airborne and satellite data), incorporation of vegetation data (e.g., yield maps, crop history, plant colour), pre-processing of data and data fusion, big data management and processing, models for spatial prediction of soil properties in the field scale (geostatistics and other interpolation methods, advanced regression models, machine learning etc.), map production and utilization, and accuracy assessments. Scale issues and regional aspects of DSM application in PA can be also studied. Both novel methodological works and well documented and inspiring case studies are welcome.

Prof. Dr. Luboš Borůvka
Guest Editor

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• spatial prediction
• soil properties
• soil and environmental data
• proximal soil sensing
• remote sensing
• quantitative models
• accuracy assessment