Soil Data Access (SDA2) Tutorial
2016-08-17
Dylan Beaudette
Introduction
This is a short tutorial on how to interact with the Soil Data Access (SDA) web-service using R. Queries are written using a dialect of SQL. On first glance SQL appears similar to the language used to write NASIS queries and reports, however, these are two distinct languages. Soil Data Access is a "window" into the spatial and tabular data associated with the current SSURGO snapshot. Queries can contain spatial and tabular filters. If you are new to SDA or SQL, have a look at this page.
Spatial queries can be included in SQL statements submitted to SDA as long as the geometry has first been transformed to WGS84 geographic (or psuedo-Web Mercator) coordinates and formatted as "well known text" (WKT). The sp and rgdal packages provide functionality for converting between coordinate systems via spTransform(). Coordinate reference system definitions (a "CRS") are typically provided using proj4 notation. You can search for various CRS definitions in a variety of formats using spatialreference.org/.
The soilDB library for R provides a helper function (SDA_query()) for submitting queries to SDA, processing the result, and reformatting the results into a rectangular table (a data.frame). Most of the work required to use the SDA_query() function will be writing SQL to describe the columns you would like returned and how the data should be filtered and possibly grouped.
Follow along with the blocks of code below by copying / pasting into a new R "script" document. Each block of command can be run by pasting into the R console, or by "stepping through" lines of code by moving the cursor to the top of a block (in the R script panel) and repeatedly pressing ctrl + enter.
If you are feeling adventurous, have a look at a draft tutorial on queries that return geometry from SDA. Additional tips on advanced spatial queries can be found here.
Install Required R Packages
You only need to do this once. If you haven't installed these packages, then copy the code below and paste into the RStudio "console" pane.
# run these commands in the R console
# stable version from CRAN + dependencies
install.packages("httr", dep=TRUE)
install.packages("soilDB", dep=TRUE)
install.packages("rgdal", dep = TRUE)
install.packages("raster", dep = TRUE)
install.packages("rgeos", dep = TRUE)
# latest versions from r-forge
install.packages("soilDB", repos = "http://R-Forge.R-project.org", type = "source")Simple Queries
Now that you have the required packages, load them into the current R session.
library(soilDB)
library(sp)
library(rgdal)
library(plyr)
library(raster)
library(rgeos)When was the CA653 survey area last exported to SSURGO?
SDA_query("SELECT areasymbol, saverest FROM sacatalog WHERE areasymbol = 'CA653'")## areasymbol saverest
## 1 CA653 10/1/2015 1:41:01 PMAre there any survey areas that haven't been updated since Jan 1, 2010?
SDA_query("SELECT areasymbol, saverest FROM sacatalog WHERE saverest < '01/01/2010'")## areasymbol saverest
## 1 MXNL001 11/27/2009 9:36:38 AM
## 2 US 7/6/2006 8:49:17 AMWhat is the most recently updates survey in CA?
SDA_query("SELECT areasymbol, saverest FROM sacatalog WHERE areasymbol LIKE 'CA%' ORDER BY saverest DESC")[1, ]## areasymbol saverest
## 1 CA653 10/1/2015 1:41:01 PMA simple query from the component table, for a single map unit: mukey = '461958'.
q <- "SELECT
mukey, cokey, comppct_r, compname, taxclname
FROM component
WHERE mukey = '461958'"
# run the query
res <- SDA_query(q)
# check
head(res)## mukey cokey comppct_r compname taxclname
## 1 461958 12008609 85 San Joaquin Fine, mixed, thermic Abruptic Durixeralfs
## 2 461958 12008610 4 Galt <NA>
## 3 461958 12008611 4 Bruella <NA>
## 4 461958 12008612 3 Hedge <NA>
## 5 461958 12008613 3 Kimball <NA>
## 6 461958 12008614 1 Unnamed <NA>Get a list of map units that contain "Amador" as minor component.
q <- "SELECT
muname, mapunit.mukey, cokey, compname, comppct_r
FROM mapunit INNER JOIN component on mapunit.mukey = component.mukey
WHERE compname LIKE '%amador%'
AND majcompflag = 'No'"
# run the query
res <- SDA_query(q)
# check
head(res)## muname mukey cokey compname comppct_r
## 1 Whiterock rocky silt loam, 3 to 8 percent slopes 463199 12757553 Amador 5
## 2 Whiterock rocky silt loam, 3 to 8 percent slopes, eroded 463200 12757558 Amador 5
## 3 Whiterock rocky silt loam, 8 to 30 percent slopes 463201 12757563 Amador 5
## 4 Whiterock rocky silt loam, 8 to 30 percent slopes, eroded 463202 12757568 Amador 5
## 5 Pardee-Ranchoseco complex, 3 to 15 percent slopes 461931 12008455 Amador 3
## 6 Peters clay, 1 to 8 percent slopes 461933 12008470 Amador 5# optionally save the results to CSV file
# write.csv(res, file='path-to-file.csv', row.names=FALSE)Get basic map unit and component data for a single survey area, Yolo County (ca113).
q <- "SELECT
component.mukey, cokey, comppct_r, compname, taxclname,
taxorder, taxsuborder, taxgrtgroup, taxsubgrp
FROM legend
INNER JOIN mapunit ON mapunit.lkey = legend.lkey
LEFT OUTER JOIN component ON component.mukey = mapunit.mukey
WHERE legend.areasymbol = 'ca113'"
# run the query
res <- SDA_query(q)
# check
head(res)## mukey cokey comppct_r compname taxclname
## 1 757748 12207591 80 Scribner Fine-loamy, mixed, superactive, thermic Cumulic Endoaquolls
## 2 757748 12207592 10 Vina Coarse-loamy, mixed, superactive, thermic Pachic Haploxerolls
## 3 757748 12207593 8 Corbiere Fine, mixed, superactive, thermic Cumulic Vertic Endoaquolls
## 4 757748 12207594 2 Unnamed <NA>
## 5 757749 12207595 5 Hustabel Coarse-loamy, mixed, superactive, thermic Cumulic Haploxerolls
## 6 757749 12207596 80 Westfan Fine-loamy, mixed, superactive, thermic Pachic Haploxerolls
## taxorder taxsuborder taxgrtgroup taxsubgrp
## 1 Mollisols Aquolls Endoaquolls Cumulic Endoaquolls
## 2 Mollisols Xerolls Haploxerolls Pachic Haploxerolls
## 3 Mollisols Aquolls Endoaquolls Cumulic Vertic Endoaquolls
## 4 <NA> <NA> <NA> <NA>
## 5 Mollisols Xerolls Haploxerolls Cumulic Haploxerolls
## 6 Mollisols Xerolls Haploxerolls Pachic HaploxerollsCross tabulate the occurrence of components named "Auburn" and "Dunstone" with survey areasymbol.
q <- "SELECT areasymbol, component.mukey, cokey, comppct_r, compname, compkind, taxclname
FROM legend
INNER JOIN mapunit ON mapunit.lkey = legend.lkey
LEFT OUTER JOIN component ON component.mukey = mapunit.mukey
WHERE compname IN ('Auburn', 'Dunstone')
AND areasymbol != 'US'
ORDER BY areasymbol, compname"
res <- SDA_query(q)
xtabs(~ areasymbol + compname, data=res)## compname
## areasymbol Auburn Dunstone
## CA067 9 0
## CA607 21 0
## CA612 8 19
## CA618 31 1
## CA619 25 1
## CA620 14 0
## CA624 24 0
## CA628 20 0
## CA632 4 0
## CA644 13 0
## CA648 6 0
## CA649 21 0
## CA707 11 0
## CA731 5 0
## CA750 1 0Queries Using Simple Spatial Filters
Get the map unit key and name at a single, manually-defined point (-121.77100 37.368402).
q <- "SELECT mukey, muname
FROM mapunit
WHERE mukey IN (
SELECT * from SDA_Get_Mukey_from_intersection_with_WktWgs84('point(-121.77100 37.368402)')
)"
SDA_query(q)## mukey muname
## 1 1882921 Diablo clay, 15 to 30 percent slopes, MLRA 15Get the map names and mukey values for a 1000m buffer around a manually-defined point (-121.77100 37.368402). A 1000m buffer applied to geographic coordinates will require several spatial transformations. First, the query point needs to be initialized in a geographic coordinate system with WGS84 datum. Next, the point is transformed to a planar coordinate system with units in meters; the NLCD coordinate reference system works well for points within the continental US. After computing a buffer in planar coordinates, the resulting polygon is converted back to geographic coordinates--this is what SDA is expecting.
# the query point is in geographic coordinates with WGS84 datum
p <- SpatialPoints(cbind(-121.77100, 37.368402), proj4string = CRS('+proj=longlat +datum=WGS84'))
# transform to planar coordinate system for buffering
p.aea <- spTransform(p, CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs '))
# create 1000 meter buffer
p.aea <- gBuffer(p.aea, width = 1000)
# transform back to WGS84 GCS
p.buff <- spTransform(p.aea, CRS('+proj=longlat +datum=WGS84'))
# convert to WKT
p.wkt <- writeWKT(p.buff)
q <- paste0("SELECT mukey, muname
FROM mapunit
WHERE mukey IN (
SELECT * from SDA_Get_Mukey_from_intersection_with_WktWgs84('", p.wkt, "')
)")
res <- SDA_query(q)
head(res)## mukey muname
## 1 456983 Diablo clay, 9 to 15 percent slopes
## 2 456993 Gaviota loam, 15 to 30 percent slopes
## 3 457017 Los Gatos-Gaviota complex, 50 to 75 percent slopes
## 4 1882920 Diablo clay, 30 to 50 percent slopes
## 5 1882921 Diablo clay, 15 to 30 percent slopes, MLRA 15
## 6 1882923 Alo-Altamont complex, 15 to 30 percent slopesIt is possible to download small collections of SSURGO map unit polygons from SDA using a bounding-box in WGS84 geographic coordinates. SDA will return polygons and their map unit keys that overlap with the BBOX query.
# extract bounding-box from out last point
# coordinates are in WGS84 GCS
b <- as.vector(bbox(p.buff))
# download map unit polygons that overlap with bbox
p.mu.polys <- mapunit_geom_by_ll_bbox(b)Graphical description of the previous steps: query point, 1000m buffer, buffer bounding box (BBOX), intersecting map unit polygons, and overlapping polygons.
# plot
par(mar=c(0,0,0,0))
plot(p.mu.polys)
plot(p.mu.polys[which(p.mu.polys$mukey %in% setdiff(p.mu.polys$mukey, res$mukey)), ], add=TRUE, col='grey')
lines(p.buff, col='red', lwd=2)
plot(extent(bbox(p.buff)), add=TRUE, col='RoyalBlue')
points(p, col='orange', pch=15)
legend('bottomleft', legend=c('query point', '1000m buffer', 'buffer BBOX', 'intersected polygons', 'overlapping polygons'), col=c('orange', 'red', 'royalblue', 'black', 'grey'), lwd=c(NA, 2, 2, 2, 2), pch=c(15, NA, NA, NA, NA))
box()
Get some component data for a manually-defined bounding box, defined in WGS84 geographic coordinates.
# define a bounding box: xmin, xmax, ymin, ymax
#
# +-------------------(ymax, xmax)
# | |
# | |
# (ymin, xmin) ----------------+
b <- c(-120.9, -120.8, 37.7, 37.8)
# convert bounding box to WKT
p <- writeWKT(as(extent(b), 'SpatialPolygons'))
# compose query, using WKT BBOX as filtering criteria
q <- paste0("SELECT mukey, cokey, compname, comppct_r
FROM component
WHERE mukey IN (SELECT DISTINCT mukey FROM SDA_Get_Mukey_from_intersection_with_WktWgs84('", p, "') )
ORDER BY mukey, cokey, comppct_r DESC")
res <- SDA_query(q)
# check
head(res)## mukey cokey compname comppct_r
## 1 462527 11158457 Madera 10
## 2 462527 11158458 Alamo 85
## 3 462527 11158459 San Joaquin 5
## 4 462541 11158510 Chualar 85
## 5 462541 11158511 Oakdale 5
## 6 462541 11158512 Modesto 5Queries Using Complex Spatial Filters
The examples above illustrate how to perform SDA queries using a single spatial filter. Typically we need to perform these queries over a collection of points, lines or polygons. The soilDB package provides some helper functions for iterating over a collection of features (usually points). Note that spatial queries for more than 1000 features should probably be done using a local copy of the map unit polygons.
The first function SDA_make_spatial_query() will convert a single Spatial* (Points, Lines, Polygons) object to WKT and submit a spatial query to SDA, returning intersecting map unit keys and names. Let's try it using a SpatialPoints object with 1 feature.
# the query point is in geographic coordinates with WGS84 datum
p <- SpatialPoints(cbind(-121.77100, 37.368402), proj4string = CRS('+proj=longlat +datum=WGS84'))
SDA_make_spatial_query(p)The second function SDA_query_features() will iterate over the features of a Spatial* (Points, Lines, Polygons) object, send a query to SDA, and return a set of the results as a data.frame object. This time, our example set of 2 points will also have some fake pedons IDs.
# the query points are in geographic coordinates with WGS84 datum
p <- SpatialPointsDataFrame(cbind(c(-121, -122), c(37, 37)), data=data.frame(pedon_id=1:2), proj4string = CRS('+proj=longlat +datum=WGS84'))
SDA_query_features(p, id='pedon_id')Let's apply the SDA_query_features() function to some real data; KSSL pedons correlated to "Auburn". Not all of these pedons have coordinates, so we will have to do some filtering first. See the in-line comments for details on each line of code.
# get KSSL pedons with taxonname = Auburn
# coordinates will be NAD83 GCS
auburn <- fetchKSSL('auburn')
# keep only those pedons with valid coordinates
auburn <- subsetProfiles(auburn, s='!is.na(x) & !is.na(y)')
# init spatial information
coordinates(auburn) <- ~ x + y
# define projection
proj4string(auburn) <- '+proj=longlat +datum=NAD83'
# extract "site data"
auburn.sp <- as(auburn, 'SpatialPointsDataFrame')
# perform SDA query on each "site", result is a data.frame
mu.data <- SDA_query_features(auburn.sp, id='pedlabsampnum')
# join results to original SoilProfileCollection using 'pedlabsampnum'
site(auburn) <- mu.dataCheck the results and plot the "Auburn" KSSL pedons, grouped by intersecting map unit key and coloring horizons according to clay content.
# check results
print(mu.data)## pedlabsampnum mukey muname
## 1 40A3004 461845 Amador-Gillender complex, 2 to 15 percent slopes
## 2 40A3005 461922 Mokelumne gravelly loam, 2 to 15 percent slopes
## 3 83P0801 461854 Auburn-Argonaut-Rock outcrop complex, 8 to 30 percent slopes
## 4 84P0879 460384 Auburn-Sobrante complex, 3 to 8 percent slopes
## 5 91P0411 460408 Auburn-Timbuctoo-Argonaut complex, 8 to 15 percent slopes
## 6 91P0414 460408 Auburn-Timbuctoo-Argonaut complex, 8 to 15 percent slopes
## 7 01N0262 461425 Dunstone-loafercreek complex, dry, 1 to 15 percent slopes
## 8 05N0395 1403441 Auburn silt loam, 5 to 15 percent slopes
## 9 UCD6445143 459938 Auburn clay loam, 8 to 30 percent slopes, eroded
## 10 UCD6505005 2600526 No Digital Data Available
## 11 UCD6604002 461423 Dunstone-Loafercreek , 2 to 15 percent slopes
## 12 UCD6605008 2600526 No Digital Data Available
## 13 UCD6605014 2600526 No Digital Data Available
## 14 UCD6705021 2600526 No Digital Data Available
## 15 UCD6705022 2600526 No Digital Data Available
## 16 UCD7355010 2600526 No Digital Data Available
## 17 UCD7455019 2600526 No Digital Data Available
## 18 UCD7455021 2600526 No Digital Data Available
## 19 UCD8005083 2600526 No Digital Data Available# plot profiles, grouped by mukey
# color horizons with clay content
par(mar=c(0,0,4,0))
groupedProfilePlot(auburn, groups='mukey', group.name.cex=0.65, color='clay', name='hzn_desgn', id.style='side', label='pedon_id', max.depth=100)
# describe IDs
mtext('user pedon ID', side=2, line=-1.5)
mtext('mukey', side=3, line=-1, at = c(0,0), adj = 0)
More examples pending...
This document is based on soilDB version 1.8-1.