Modelling and Mapping

Once soil data is collected, analyzed, archived, and organized, the soil data can be used for modelling and mapping in the next step of the development of a SIS. Soil databases typically contain observations and measurements taken at sampling (point) locations. Many users though, require information in the form of soil maps for their applications. Therefore, developing soil maps from soil (point) data stored in a database is a logical next step in the soil information workflow. For soil mapping, two mapping approaches are often used:

• conventional soil mapping (also known as landscape-based soil class mapping)
• digital soil mapping.

In conventional soil mapping soil mapping, soil surveyors take observations in the landscape and then, often supported by aerial photography, delineate soil bodies that are homogeneous in terms of morphology and composition. These soil bodies are subsequently classified based on a soil classification system. The result is a traditional soil class map that is often accompanied by a survey report that provides descriptive information and quantitative data on the soil bodies.

Since the beginning of the 21st century, more maps are often developed using statistical methods for modelling and mapping soil spatial variation. These methods are referred to as ‘digital soil mapping’ (DSM). Nowadays, DSM is an accepted practice for soil mapping and more often used than conventional soil mapping. The widely used Soil Survey Manual of the U.S. Department of Agriculture now contains a chapter dedicated to DSM. With the advent of DSM, not only the way soil maps were produced changed, but also the type of maps. DSM typically produces gridded maps of quantitative soil properties at a specific spatial resolution (grid cell size), while conventional survey typically produces polygon maps of soil types and associated soil properties at a certain (cartographic) scale level.

This workflow step presents an overview of methods, tools, and standards for developing soil maps from soil observational data. Other type of models, such as dynamic or process models, that use soil data as input to predict functional properties of the soil (that often cannot be directly measured such as carbon sequestration potential) or other type of agricultural or environmental variables (such as yield potential based on soil nutrient status and soil depth) are considered in step on Applying soil information.