Soil survey data are typically built upon a foundation of soil-landscape relationships that have been verified in the field.

A map depicting the spatial distribution of a given soil series can be very useful when working on a new soil survey, updating an old one, or searching for specific soil characteristics. We have recently added a soil series query facility to SoilWeb, where results are returned in the form of a KML file. Two modes are currently supported:

1. map unit based querying- only map units named for the given soil series are returned
2. component based querying- map units containing components named for the given series are returned

For example, if someone was interested in the spatial distribution of the Amador soil series, they could use the Series Extent Mapping tool to get a quick description of which survey areas contain (and how many corresponding acres of) this series. For an even more detailed description of where the Amador series is mapped, one could use our new soil series query like this:

http://casoilresource.lawr.ucdavis.edu/soil_web/reflector_api/soils.php?what=soil_series_extent&q_string=amador

 
This is a preliminary version, and a subsequent post will contain links to a Google Earth file that can be used to simplify the query process. In most cases queries take about 1-5 seconds, which is quite fast considering: 1) either 730k component names or 275k map unit names are searched, 2) 35 million map unit polygons are filtered for the series in question, and, 3) bounding boxes for matching polygons are merged together-- all on-the-fly. Full text searches for map unit/component names are very fast thanks to advanced text indexing and searching algorithms implemented in PostgreSQL and spatial processing functions implemented in PostGIS. In the final version, the location of the official series description (OSD) will be included in query results.

Attached at the bottom of the page is a KMZ demo showing sample output from the two query modes. Screen shots from the demo are posted below.

Soil Series Query Results: Amador Series: blue regions: map units dominated by the Amador series; red regions: map units that contain at least one component of the Amador series.

LCC KMZ
Soil Texture KMZ

 
Updated versions of three soils-related KMZ files: 1-km scale, aggregate LCC, CA Storie Index, and soil texture data, derived from SSURGO. These are part of a series of KMZ / raster datasets that will be published soon. See attached files at the bottom of the page. Enjoy!

It turns out that you can generate a quasi-numerical distance between soil profiles classified according to Soil Taxonomy (or any other hierarchical system) using Gower's generalized dissimilarity metric. For example, taxonomic distances computed from subgroup membership are based on the number of matches at the order, suborder, greatgroup, and subgroup level. This approach allows for the derivation of a quasi-numerical classification system from Soil Taxonomy, but it is severly limited by the fact that each split in the hierarchy is given equal weight. In other words, the quasi-numerical dissimilarity associated with divergence at the soil order level is identical to that associated with divergence at the subgroup level. Clearly this is not ideal.

Gower's generalized dissimilarity metric is conveniently implemented in the cluster package for R. I have posted some related material in the past, but left out some of the details regarding which clustering algorithms produce the most useful dendrograms. Divisive clustering best represents the step-wise splits within the hierarchy of Soil Taxonomy, as expressed in terms of pair-wise dissimilarities. Code examples are below, along with the data used to generate the figure of California subgroups. Discontinuities in figure below are caused by errors in the underlying data, e.g. mis-matches in soil order vs. suborder membership.

Subgroups from California

Fresno Area Urban Areas vs Irrigated LCC: grey regions are current urban areas

A couple of maps generated from a 1km gridded soil property database, derived from SSURGO data where available with holes filled with STATSGO data. Soil properties visualized at this scale illustrate several important soil-forming factors operating within California: sediment source in the Great Valley, the interplay between precipitation and ET, and removal of salts. This database and the details on its creation should be available within a couple of months. This builds on a related post highlighting some of these maps packaged in KMZ format. Check back in a couple of weeks of updates.

Major updates to the SoilWeb iPhone Application.

 
Parent material data is stored within the copm and copmgrp tables. The copm table can be linked to the copmgrp table via the 'copmgrpkey' field, and the copmgrp table can be linked to the component table via the 'cokey' field. The following queries illustrate these table relationships, and show one possible strategy for extracting the parent material information associated with the largest component of each map unit.

 
Several of the example queries are based on this map unit:

 
About

 
Premise
SSURGO is a digital, high-resolution (1:24,000), soil survey database produced by the USDA-NRCS. It is one of the largest and most complete spatial databases in the world; and is available for nearly the entire USA at no cost. These data are distributed as a combination of geographic and text data, representing soil map units and their associated properties. Unfortunately the text files do not come with column headers, so a template is required to make sense of the data. Alternatively, one can use an MS Access template to attach column names, generate reports, and other such tasks. CSV file can be exported from the MS Access database for further use. A follow-up post with text file headers, and complete PostgreSQL database schema will contain details on implementing a SSURGO database without using MS Access.

If you happen to have some of the SSURGO tabular data that includes column names, the following R code may be of general interest for resolving the 1:many:many hierarchy of relationships required to make a thematic map.

 
This is the format we want the data to be in

    mukey     clay      silt      sand water_storage
   458581 20.93750 20.832237 20.861842     14.460000
   458584 43.11513 30.184868 26.700000     23.490000
   458593 50.00000 27.900000 22.100000     22.800000
   458595 34.04605 14.867763 11.776974     18.900000

 
So we can make a map like this

Rapid urban and sub-urban expansion in the San Joaquin Valley have resulted in the loss of millions of acres of prime farmland in the last 100 years. Approximately 11% of class 1 (irrigated) land and 7% of class 2 land have already been paved over in the Fresno-Madera region (first image below). Recent projections in the expansion of urban areas in this region suggests that by 2085 an additional 28% of class 1 and 25% of class 2 land will be paved over (second image below). This is a preliminary summary-- details to follow.

Fresno Area Urban Areas vs Irrigated LCC: grey regions are current urban areas