Using Vector Soil Data in a Raster World?

Using Vector Soil Data in a Raster World?

Why spend the time and resources to extract the NRCS ‘Soil SURvey GeOgraphic’ (SSURGO) soil polygons only to display the vector lines as a reference? There is a massive amount of soil information within the SSURGO database, however most precision farming web portals provide the NRCS soil survey vector lines as a reference layer. The USDA Soil Data Mart database (a.k.a. SSURGO) has multiple one-to-many database relationships. This complexity causes a great deal of misunderstanding and frustration for those who attempt to mine data.


For example, the map unit symbol is a label for the soil map and refers to the map unit, commonly referred to in precision web portals as a ‘soil type’. That map unit has multiple soil components referred to as a one-map-unit-to-many components relationship. Most programming staff choose not to extract component soil properties and interpretations even though specific properties and interpretations can add value to the web portals. The image here depicts the multiple soil types (compname) within this particular set of map units (musym).



Precision farming relies on various data layers such as satellite imagery, drone imagery, harvest yields, EC mapping, soil sampling, topography, and soil survey data. These are raster and point data layers that can be analyzed to provide a prescription that best addresses the farmer’s bottom line. Two issues of concern for web portal users is the use of vector soil survey polygons as a reference layer (that could be best utilized as a raster data layer) and the lack of appropriate soil information needed to aid the prescription.

A little known soil survey publication product is the gSSURGO database. The gSSURGO is the Gridded Soil SURvey GeOgraphic database, a raster spatial product. The raster layer is delivered as a 10m and 30m gridded product. As a raster product, this allows the GIS user to combine it with the other raster products for additional analysis.


The attribute gSSURGO database is the same information delivered with the SSURGO data. There is an additional table (Value) in gSSURGO that is not provided in the standard SSURGO database. The gSSURGO raster product is a useful tool for those serious in understanding how the soil morphology relates to crop productivity. Combining the raster soil survey data layer with other raster data layers (e.g., yield data, EC mapping, topography, etc.) allows the intersection and analysis of the wide variety of thematic factors to identify features that influence productivity.

The soils database, when properly mined, can provide data layers of the various soil properties for the soil types contained in the soil survey map units. Teasing out the soil types and their specific soil properties provides useful information to those serious in understanding the productivity. Mining the database requires an understanding of the various aggregation methods used to migrate the one-to-many database relationships to a one-to-one map making relationship.

There is a table in the SSURGO and gSSURGO database products named the MUAGGATT (map unit aggregated attribute). This table contains columns of properties and interpretations that are aggregated to the map unit symbol making it easy for users to create property or interpretive maps at the map unit level. Methods of aggregation include: dominant component, dominant condition, weighted average, most limiting, and least limiting. Additionally, the database can be queried to extract useful information to assist in developing prescriptions. For example, this table below contains the available water storage (AWS) for each soil type found in the map unit. The query presents the AWS by the upper 100 cm (40 inches) and for the entire soil profile (as identified in the database).


Other information presented is the Hydrologic Soil Group (used by engineers), the parent material (pm), the landform the soil exists on, along with the soil depth to a restricted layer and the type of restriction. Each of these data are presented for each soil type (compname) within the map unit (musym).

A common question asked is “What is the ONE soil property that most affects productivity?” If one property is selected, it would be available water storage, hence the reason it is queried and presented in this image. This image presents the diversity of AWS across the range of soil types in both the AWS100 cm (40 inches) and in the AWS_profile.

Additionally, this image presents the numerous soils identified within each map unit. Mining the data in this way allows the programmer to develop a variety of map layers to identify the many soil types within the field. Analysis can determine how the morphology of each type influences the field productivity. There are a great deal of soil properties and interpretations that can be queried from the SSURGO database to assist analysis.

Future articles will be written to provide examples of raster-based analysis. For more information on value-added analysis from the SSURGO or gSSURGO database for precision farming web portals, contact Paul R. Finnell at [email protected] or visit or LinkedIn.

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