For this discussion I use the terms “soil property” and “soil type” to mean two distinct things:
Examples of soil properties: percentage clay, rockiness, pH, drainage, elec‑ trical conductivity. Examples of soil types: clay, loam, silt‑loam, clay‑loam.
Spatial soil data describe soil properties at certain locations and depths. There are many potential soil properties and they are usually described in one or more fields of the soil data table(s) such as pH, texture, and water‑holding capacity, among others. (See Figure 6.100 for color suggestions.) Soil data also include one or more soil type fields that utilize a standard taxonomic system that groups all of the properties into categories. Several taxonomic systems are in use around the world, usually standard‑ ized by country or region such as the U.S. Department of Agriculture soil taxonomy and the World Reference Base for Soil Resources. Soils are not standard 2D data. Because soil properties and types change depending on the depth of the soil, soil data will include property and type data not just for the surface polygons but for subsurface polygons as well. These different depth layers are known as “horizons.” This has implications for how the data are visually displayed on a 2D map. Usually vector‑based, soil data have a one‑to‑many relationship between the polygons and the linked horizon attributes. The unique thing about soils is that soil properties are a continuously changing variable even though the data that capture them are usually a set of discrete polygons stretching across a geographic area. Adjacent polygons with different soil type attri‑ butes may, in fact, be quite similar near their common edge despite their disparate categorizations. In other words, while soils are continuously changing in real life, the data that describe them are discrete in the database. Sometimes soil properties and therefore soil types change over a very small distance, like a few centimeters, and sometimes they change over a large distance, like a few kilometers. This is because the mapping units, or polygons, almost always contain a variety of soil types within them. Only when you get to the very large‑scale datasets like 1:5,000 or larger do the soil mapping polygons begin to identify single types of soil. For most national and regional datasets, therefore, the soil polygons will contain a one‑to‑many rela‑ tionship to soil type. These soil types themselves change in concentration within